Patent Publication Number: US-2022222767-A1

Title: Memory prefetching in multiple gpu environment

Description:
CLAIM TO PRIORITY 
     This application is a continuation of and claims the benefit of and priority to U.S. application Ser. No. 16/355,274, entitled MEMORY PREFETCHING IN MULTIPLE GPU ENVIRONMENT, by Joydeep Ray, et al., filed Mar. 19, 2019, the entire contents of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     Embodiments described herein generally relate to the field of electronic devices and, more particularly, memory prefetching in a multiple graphics processing unit (GPU) environment. 
     BACKGROUND 
     Current parallel graphics data processing includes systems and methods developed to perform specific operations on graphics data such as, for example, linear interpolation, tessellation, rasterization, texture mapping, depth testing, etc. Traditionally, graphics processors used fixed function computational units to process graphics data; however, more recently, portions of graphics processors have been made programmable, enabling such processors to support a wider variety of operations for processing vertex and fragment data. 
     To further increase performance, graphics processors typically implement processing techniques such as pipelining that attempt to process, in parallel, as much graphics data as possible throughout the different parts of the graphics pipeline. Parallel graphics processors with single instruction, multiple thread (SIMT) architectures are designed to maximize the amount of parallel processing in the graphics pipeline. In an SIMT architecture, groups of parallel threads attempt to execute program instructions synchronously together as often as possible to increase processing efficiency. A general overview of software and hardware for SIMT architectures can be found in Shane Cook,  CUDA Programming  Chapter 3, pages 37-51 (2013). 
     Conventional graphics processing systems provide for prefetching of data elements for efficiency in graphics data processing. However, prefetching can be problematic when employed in an environment with multiple graphics processors. In a conventional system a prefetcher operating in a non-unified memory environment may not distinguish between multiple underlying physical memory units. If a prefetcher is not aware of the physical layout of memory this can result in extra bus traffic because of prefetching of memory from remote physical memory sources. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments described here are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements. 
         FIG. 1  is a block diagram illustrating a computer system configured to implement one or more aspects of the embodiments described herein; 
         FIG. 2A-2D  illustrate parallel processor components, according to an embodiment; 
         FIG. 3A-3C  are block diagrams of graphics multiprocessors and multiprocessor-based GPUs, according to embodiments; 
         FIG. 4A-4F  illustrate an exemplary architecture in which a plurality of GPUs is communicatively coupled to a plurality of multi-core processors; 
         FIG. 5  illustrates a graphics processing pipeline, according to an embodiment; 
         FIG. 6  illustrates a machine learning software stack, according to an embodiment; 
         FIG. 7  illustrates a general-purpose graphics processing unit, according to an embodiment; 
         FIG. 8  illustrates a multi-GPU computing system, according to an embodiment; 
         FIG. 9A-9B  illustrate layers of exemplary deep neural networks; 
         FIG. 10  illustrates an exemplary recurrent neural network; 
         FIG. 11  illustrates training and deployment of a deep neural network; 
         FIG. 12  is a block diagram illustrating distributed learning; 
         FIG. 13  illustrates an exemplary inferencing system on a chip (SOC) suitable for performing inferencing using a trained model; 
         FIG. 14  is an illustration of a multiple GPU environment according to some embodiments; 
         FIG. 15  is an illustration of protected prefetching in a multiple GPU environment according to some embodiments; 
         FIG. 16  is an illustration of prefetching from memory surfaces in a multiple GPU environment according to some embodiments; 
         FIG. 17  is a flowchart to illustrate a process for prefetching in a multiple GPU environment according to some embodiments; 
         FIG. 18A  is an illustration of a gather/scatter prefetch instruction according to some embodiments; 
         FIG. 18B  is an illustration of a selective prefetch instruction according to some embodiments; 
         FIG. 19  is an illustration of prefetch operation with status notification according to some embodiments; 
         FIG. 20  is an illustration of an apparatus or system to provide for improved prefetching performance, according to some embodiments; 
         FIG. 21  is a block diagram of a processing system, according to an embodiment; 
         FIG. 22  is a block diagram of a processor according to an embodiment; 
         FIG. 23  is a block diagram of a graphics processor, according to an embodiment; 
         FIG. 24  is a block diagram of a graphics processing engine of a graphics processor in accordance with some embodiments; 
         FIG. 25  is a block diagram of hardware logic of a graphics processor core, according to some embodiments described herein; 
         FIG. 26A-26B  illustrate thread execution logic including an array of processing elements employed in a graphics processor core according to embodiments described herein; 
         FIG. 27  is a block diagram illustrating a graphics processor instruction formats according to some embodiments; 
         FIG. 28  is a block diagram of a graphics processor according to another embodiment; 
         FIG. 29A-29B  illustrate a graphics processor command format and command sequence, according to some embodiments; 
         FIG. 30  illustrates exemplary graphics software architecture for a data processing system according to some embodiments; 
         FIG. 31A  is a block diagram illustrating an IP core development system, according to an embodiment; 
         FIG. 31B  illustrates a cross-section side view of an integrated circuit package assembly, according to some embodiments described herein; 
         FIG. 32  is a block diagram illustrating an exemplary system on a chip integrated circuit, according to an embodiment; and 
         FIG. 33A-33B  are block diagrams illustrating exemplary graphics processors for use within an SoC, according to embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments described herein are generally directed to memory prefetching in a multiple GPU environment. 
     In some embodiments, an apparatus, system, or process provides for improvements in memory prefetching for a multiple graphic processing unit (GPU) environment. In some embodiments, rules are applied for use by a prefetcher when a multiple GPU workload is executing across a cluster of GPUs having unified virtual memory and non-unified physical memory. 
     In some embodiments, an apparatus, system, or process includes one or more of: 
     (1) Protected prefetch optimizations for cross-GPU coherency; 
     (2) Gather/scatter prefetch instructions; and 
     (3) Prefetch operation with status notification. 
     In some embodiments, a graphics processing unit (GPU) is communicatively coupled to host/processor cores to accelerate graphics operations, machine-learning operations, pattern analysis operations, and various general-purpose GPU (GPGPU) functions. The GPU may be communicatively coupled to the host processor/cores over a bus or another interconnect (e.g., a high-speed interconnect such as PCIe or NVLink). In other embodiments, the GPU may be integrated on the same package or chip as the cores and communicatively coupled to the cores over an internal processor bus/interconnect (i.e., internal to the package or chip). Regardless of the manner in which the GPU is connected, the processor cores may allocate work to the GPU in the form of sequences of commands/instructions contained in a work descriptor. The GPU then uses dedicated circuitry/logic for efficiently processing these commands/instructions. 
     In the following description, numerous specific details are set forth to provide a more thorough understanding. However, it will be apparent to one of skill in the art that the embodiments described herein may be practiced without one or more of these specific details. In other instances, well-known features have not been described to avoid obscuring the details of the present embodiments. 
     System Overview 
       FIG. 1  is a block diagram illustrating a computing system  100  configured to implement one or more aspects of the embodiments described herein. The computing system  100  includes a processing subsystem  101  having one or more processor(s)  102  and a system memory  104  communicating via an interconnection path that may include a memory hub  105 . The memory hub  105  may be a separate component within a chipset component or may be integrated within the one or more processor(s)  102 . The memory hub  105  couples with an I/O subsystem  111  via a communication link  106 . The I/O subsystem  111  includes an I/O hub  107  that can enable the computing system  100  to receive input from one or more input device(s)  108 . Additionally, the I/O hub  107  can enable a display controller, which may be included in the one or more processor(s)  102 , to provide outputs to one or more display device(s)  110 A. In one embodiment the one or more display device(s)  110 A coupled with the I/O hub  107  can include a local, internal, or embedded display device. 
     In one embodiment the processing subsystem  101  includes one or more parallel processor(s)  112  coupled to memory hub  105  via a bus or other communication link  113 . The communication link  113  may be one of any number of standards based communication link technologies or protocols, such as, but not limited to PCI Express, or may be a vendor specific communications interface or communications fabric. In one embodiment the one or more parallel processor(s)  112  form a computationally focused parallel or vector processing system that can include a large number of processing cores and/or processing clusters, such as a many integrated core (MIC) processor. In one embodiment the one or more parallel processor(s)  112  form a graphics processing subsystem that can output pixels to one of the one or more display device(s)  110 A coupled via the I/O Hub  107 . The one or more parallel processor(s)  112  can also include a display controller and display interface (not shown) to enable a direct connection to one or more display device(s)  110 B. 
     Within the I/O subsystem  111 , a system storage unit  114  can connect to the I/O hub  107  to provide a storage mechanism for the computing system  100 . An I/O switch  116  can be used to provide an interface mechanism to enable connections between the I/O hub  107  and other components, such as a network adapter  118  and/or wireless network adapter  119  that may be integrated into the platform, and various other devices that can be added via one or more add-in device(s)  120 . The network adapter  118  can be an Ethernet adapter or another wired network adapter. The wireless network adapter  119  can include one or more of a Wi-Fi, Bluetooth, near field communication (NFC), or other network device that includes one or more wireless radios. 
     The computing system  100  can include other components not explicitly shown, including USB or other port connections, optical storage drives, video capture devices, and the like, may also be connected to the I/O hub  107 . Communication paths interconnecting the various components in  FIG. 1  may be implemented using any suitable protocols, such as PCI (Peripheral Component Interconnect) based protocols (e.g., PCI-Express), or any other bus or point-to-point communication interfaces and/or protocol(s), such as the NV-Link high-speed interconnect, or interconnect protocols known in the art. 
     In one embodiment, the one or more parallel processor(s)  112  incorporate circuitry optimized for graphics and video processing, including, for example, video output circuitry, and constitutes a graphics processing unit (GPU). In another embodiment, the one or more parallel processor(s)  112  incorporate circuitry optimized for general purpose processing, while preserving the underlying computational architecture, described in greater detail herein. In yet another embodiment, components of the computing system  100  may be integrated with one or more other system elements on a single integrated circuit. For example, the one or more parallel processor(s)  112 , memory hub  105 , processor(s)  102 , and I/O hub  107  can be integrated into a system on chip (SoC) integrated circuit. Alternatively, the components of the computing system  100  can be integrated into a single package to form a system in package (SIP) configuration. In one embodiment at least a portion of the components of the computing system  100  can be integrated into a multi-chip module (MCM), which can be interconnected with other multi-chip modules into a modular computing system. 
     It will be appreciated that the computing system  100  shown herein is illustrative and that variations and modifications are possible. The connection topology, including the number and arrangement of bridges, the number of processor(s)  102 , and the number of parallel processor(s)  112 , may be modified as desired. For instance, in some embodiments, system memory  104  is connected to the processor(s)  102  directly rather than through a bridge, while other devices communicate with system memory  104  via the memory hub  105  and the processor(s)  102 . In other alternative topologies, the parallel processor(s)  112  are connected to the I/O hub  107  or directly to one of the one or more processor(s)  102 , rather than to the memory hub  105 . In other embodiments, the I/O hub  107  and memory hub  105  may be integrated into a single chip. Some embodiments may include two or more sets of processor(s)  102  attached via multiple sockets, which can couple with two or more instances of the parallel processor(s)  112 . 
     Some of the particular components shown herein are optional and may not be included in all implementations of the computing system  100 . For example, any number of add-in cards or peripherals may be supported, or some components may be eliminated. Furthermore, some architectures may use different terminology for components similar to those illustrated in  FIG. 1 . For example, the memory hub  105  may be referred to as a Northbridge in some architectures, while the I/O hub  107  may be referred to as a Southbridge. 
       FIG. 2A  illustrates a parallel processor  200 , according to an embodiment. The various components of the parallel processor  200  may be implemented using one or more integrated circuit devices, such as programmable processors, application specific integrated circuits (ASICs), or field programmable gate arrays (FPGA). The illustrated parallel processor  200  is a variant of the one or more parallel processor(s)  112  shown in  FIG. 1 , according to an embodiment. 
     In one embodiment the parallel processor  200  includes a parallel processing unit  202 . The parallel processing unit includes an I/O unit  204  that enables communication with other devices, including other instances of the parallel processing unit  202 . The I/O unit  204  may be directly connected to other devices. In one embodiment the I/O unit  204  connects with other devices via the use of a hub or switch interface, such as memory hub  105 . The connections between the memory hub  105  and the I/O unit  204  form a communication link  113 . Within the parallel processing unit  202 , the I/O unit  204  connects with a host interface  206  and a memory crossbar  216 , where the host interface  206  receives commands directed to performing processing operations and the memory crossbar  216  receives commands directed to performing memory operations. 
     When the host interface  206  receives a command buffer via the I/O unit  204 , the host interface  206  can direct work operations to perform those commands to a front end  208 . In one embodiment the front end  208  couples with a scheduler  210 , which is configured to distribute commands or other work items to a processing cluster array  212 . In one embodiment the scheduler  210  ensures that the processing cluster array  212  is properly configured and in a valid state before tasks are distributed to the processing clusters of the processing cluster array  212 . In one embodiment the scheduler  210  is implemented via firmware logic executing on a microcontroller. The microcontroller implemented scheduler  210  is configurable to perform complex scheduling and work distribution operations at coarse and fine granularity, enabling rapid preemption and context switching of threads executing on the processing array  212 . In one embodiment, the host software can prove workloads for scheduling on the processing array  212  via one of multiple graphics processing doorbells. The workloads can then be automatically distributed across the processing array  212  by the scheduler  210  logic within the scheduler microcontroller. 
     The processing cluster array  212  can include up to “N” processing clusters (e.g., cluster  214 A, cluster  214 B, through cluster  214 N). Each cluster  214 A- 214 N of the processing cluster array  212  can execute a large number of concurrent threads. The scheduler  210  can allocate work to the clusters  214 A- 214 N of the processing cluster array  212  using various scheduling and/or work distribution algorithms, which may vary depending on the workload arising for each type of program or computation. The scheduling can be handled dynamically by the scheduler  210 , or can be assisted in part by compiler logic during compilation of program logic configured for execution by the processing cluster array  212 . In one embodiment, different clusters  214 A- 214 N of the processing cluster array  212  can be allocated for processing different types of programs or for performing different types of computations. 
     The processing cluster array  212  can be configured to perform various types of parallel processing operations. In one embodiment the processing cluster array  212  is configured to perform general-purpose parallel compute operations. For example, the processing cluster array  212  can include logic to execute processing tasks including filtering of video and/or audio data, performing modeling operations, including physics operations, and performing data transformations. 
     In one embodiment the processing cluster array  212  is configured to perform parallel graphics processing operations. In embodiments in which the parallel processor  200  is configured to perform graphics processing operations, the processing cluster array  212  can include additional logic to support the execution of such graphics processing operations, including, but not limited to texture sampling logic to perform texture operations, as well as tessellation logic and other vertex processing logic. Additionally, the processing cluster array  212  can be configured to execute graphics processing related shader programs such as, but not limited to vertex shaders, tessellation shaders, geometry shaders, and pixel shaders. The parallel processing unit  202  can transfer data from system memory via the I/O unit  204  for processing. During processing the transferred data can be stored to on-chip memory (e.g., parallel processor memory  222 ) during processing, then written back to system memory. 
     In one embodiment, when the parallel processing unit  202  is used to perform graphics processing, the scheduler  210  can be configured to divide the processing workload into approximately equal sized tasks, to better enable distribution of the graphics processing operations to multiple clusters  214 A- 214 N of the processing cluster array  212 . In some embodiments, portions of the processing cluster array  212  can be configured to perform different types of processing. For example a first portion may be configured to perform vertex shading and topology generation, a second portion may be configured to perform tessellation and geometry shading, and a third portion may be configured to perform pixel shading or other screen space operations, to produce a rendered image for display. Intermediate data produced by one or more of the clusters  214 A- 214 N may be stored in buffers to allow the intermediate data to be transmitted between clusters  214 A- 214 N for further processing. 
     During operation, the processing cluster array  212  can receive processing tasks to be executed via the scheduler  210 , which receives commands defining processing tasks from front end  208 . For graphics processing operations, processing tasks can include indices of data to be processed, e.g., surface (patch) data, primitive data, vertex data, and/or pixel data, as well as state parameters and commands defining how the data is to be processed (e.g., what program is to be executed). The scheduler  210  may be configured to fetch the indices corresponding to the tasks or may receive the indices from the front end  208 . The front end  208  can be configured to ensure the processing cluster array  212  is configured to a valid state before the workload specified by incoming command buffers (e.g., batch-buffers, push buffers, etc.) is initiated. 
     Each of the one or more instances of the parallel processing unit  202  can couple with parallel processor memory  222 . The parallel processor memory  222  can be accessed via the memory crossbar  216 , which can receive memory requests from the processing cluster array  212  as well as the I/O unit  204 . The memory crossbar  216  can access the parallel processor memory  222  via a memory interface  218 . The memory interface  218  can include multiple partition units (e.g., partition unit  220 A, partition unit  220 B, through partition unit  220 N) that can each couple to a portion (e.g., memory unit) of parallel processor memory  222 . In one implementation the number of partition units  220 A- 220 N is configured to be equal to the number of memory units, such that a first partition unit  220 A has a corresponding first memory unit  224 A, a second partition unit  220 B has a corresponding memory unit  224 B, and an Nth partition unit  220 N has a corresponding Nth memory unit  224 N. In other embodiments, the number of partition units  220 A- 220 N may not be equal to the number of memory devices. 
     In various embodiments, the memory units  224 A- 224 N can include various types of memory devices, including dynamic random access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory. In one embodiment, the memory units  224 A- 224 N may also include 3D stacked memory, including but not limited to high bandwidth memory (HBM). Persons skilled in the art will appreciate that the specific implementation of the memory units  224 A- 224 N can vary, and can be selected from one of various conventional designs. Render targets, such as frame buffers or texture maps may be stored across the memory units  224 A- 224 N, allowing partition units  220 A- 220 N to write portions of each render target in parallel to efficiently use the available bandwidth of parallel processor memory  222 . In some embodiments, a local instance of the parallel processor memory  222  may be excluded in favor of a unified memory design that utilizes system memory in conjunction with local cache memory. 
     In one embodiment, any one of the clusters  214 A- 214 N of the processing cluster array  212  can process data that will be written to any of the memory units  224 A- 224 N within parallel processor memory  222 . The memory crossbar  216  can be configured to transfer the output of each cluster  214 A- 214 N to any partition unit  220 A- 220 N or to another cluster  214 A- 214 N, which can perform additional processing operations on the output. Each cluster  214 A- 214 N can communicate with the memory interface  218  through the memory crossbar  216  to read from or write to various external memory devices. In one embodiment the memory crossbar  216  has a connection to the memory interface  218  to communicate with the I/O unit  204 , as well as a connection to a local instance of the parallel processor memory  222 , enabling the processing units within the different processing clusters  214 A- 214 N to communicate with system memory or other memory that is not local to the parallel processing unit  202 . In one embodiment the memory crossbar  216  can use virtual channels to separate traffic streams between the clusters  214 A- 214 N and the partition units  220 A- 220 N. 
     While a single instance of the parallel processing unit  202  is illustrated within the parallel processor  200 , any number of instances of the parallel processing unit  202  can be included. For example, multiple instances of the parallel processing unit  202  can be provided on a single add-in card, or multiple add-in cards can be interconnected. The different instances of the parallel processing unit  202  can be configured to inter-operate even if the different instances have different numbers of processing cores, different amounts of local parallel processor memory, and/or other configuration differences. For example, in one embodiment some instances of the parallel processing unit  202  can include higher precision floating point units relative to other instances. Systems incorporating one or more instances of the parallel processing unit  202  or the parallel processor  200  can be implemented in a variety of configurations and form factors, including but not limited to desktop, laptop, or handheld personal computers, servers, workstations, game consoles, and/or embedded systems. 
       FIG. 2B  is a block diagram of a partition unit  220 , according to an embodiment. In one embodiment the partition unit  220  is an instance of one of the partition units  220 A- 220 N of  FIG. 2A . As illustrated, the partition unit  220  includes an L2 cache  221 , a frame buffer interface  225 , and a ROP  226  (raster operations unit). The L2 cache  221  is a read/write cache that is configured to perform load and store operations received from the memory crossbar  216  and ROP  226 . Read misses and urgent write-back requests are output by L2 cache  221  to frame buffer interface  225  for processing. Updates can also be sent to the frame buffer via the frame buffer interface  225  for processing. In one embodiment the frame buffer interface  225  interfaces with one of the memory units in parallel processor memory, such as the memory units  224 A- 224 N of  FIG. 2A  (e.g., within parallel processor memory  222 ). 
     In graphics applications, the ROP  226  is a processing unit that performs raster operations such as stencil, z test, blending, and the like. The ROP  226  then outputs processed graphics data that is stored in graphics memory. In some embodiments the ROP  226  includes compression logic to compress depth or color data that is written to memory and decompress depth or color data that is read from memory. The compression logic can be lossless compression logic that makes use of one or more of multiple compression algorithms. The type of compression that is performed by the ROP  226  can vary based on the statistical characteristics of the data to be compressed. For example, in one embodiment, delta color compression is performed on depth and color data on a per-tile basis. 
     In some embodiments, the ROP  226  is included within each processing cluster (e.g., cluster  214 A- 214 N of  FIG. 2A ) instead of within the partition unit  220 . In such embodiment, read and write requests for pixel data are transmitted over the memory crossbar  216  instead of pixel fragment data. The processed graphics data may be displayed on a display device, such as one of the one or more display device(s)  110  of  FIG. 1 , routed for further processing by the processor(s)  102 , or routed for further processing by one of the processing entities within the parallel processor  200  of  FIG. 2A . 
       FIG. 2C  is a block diagram of a processing cluster  214  within a parallel processing unit, according to an embodiment. In one embodiment the processing cluster is an instance of one of the processing clusters  214 A- 214 N of  FIG. 2A . The processing cluster  214  can be configured to execute many threads in parallel, where the term “thread” refers to an instance of a particular program executing on a particular set of input data. In some embodiments, single-instruction, multiple-data (SIMD) instruction issue techniques are used to support parallel execution of a large number of threads without providing multiple independent instruction units. In other embodiments, single-instruction, multiple-thread (SIMT) techniques are used to support parallel execution of a large number of generally synchronized threads, using a common instruction unit configured to issue instructions to a set of processing engines within each one of the processing clusters. Unlike a SIMD execution regime, where all processing engines typically execute identical instructions, SIMT execution allows different threads to more readily follow divergent execution paths through a given thread program. Persons skilled in the art will understand that a SIMD processing regime represents a functional subset of a SIMT processing regime. 
     Operation of the processing cluster  214  can be controlled via a pipeline manager  232  that distributes processing tasks to SIMT parallel processors. The pipeline manager  232  receives instructions from the scheduler  210  of  FIG. 2A  and manages execution of those instructions via a graphics multiprocessor  234  and/or a texture unit  236 . The illustrated graphics multiprocessor  234  is an exemplary instance of a SIMT parallel processor. However, various types of SIMT parallel processors of differing architectures may be included within the processing cluster  214 . One or more instances of the graphics multiprocessor  234  can be included within a processing cluster  214 . The graphics multiprocessor  234  can process data and a data crossbar  240  can be used to distribute the processed data to one of multiple possible destinations, including other shader units. The pipeline manager  232  can facilitate the distribution of processed data by specifying destinations for processed data to be distributed via the data crossbar  240 . 
     Each graphics multiprocessor  234  within the processing cluster  214  can include an identical set of functional execution logic (e.g., arithmetic logic units, load-store units, etc.). The functional execution logic can be configured in a pipelined manner in which new instructions can be issued before previous instructions are complete. The functional execution logic supports a variety of operations including integer and floating point arithmetic, comparison operations, Boolean operations, bit-shifting, and computation of various algebraic functions. In one embodiment the same functional-unit hardware can be leveraged to perform different operations and any combination of functional units may be present. 
     The instructions transmitted to the processing cluster  214  constitutes a thread. A set of threads executing across the set of parallel processing engines is a thread group. A thread group executes the same program on different input data. Each thread within a thread group can be assigned to a different processing engine within a graphics multiprocessor  234 . A thread group may include fewer threads than the number of processing engines within the graphics multiprocessor  234 . When a thread group includes fewer threads than the number of processing engines, one or more of the processing engines may be idle during cycles in which that thread group is being processed. A thread group may also include more threads than the number of processing engines within the graphics multiprocessor  234 . When the thread group includes more threads than the number of processing engines within the graphics multiprocessor  234 , processing can be performed over consecutive clock cycles. In one embodiment multiple thread groups can be executed concurrently on a graphics multiprocessor  234 . 
     In one embodiment the graphics multiprocessor  234  includes an internal cache memory to perform load and store operations. In one embodiment, the graphics multiprocessor  234  can forego an internal cache and use a cache memory (e.g., L1 cache  248 ) within the processing cluster  214 . Each graphics multiprocessor  234  also has access to L2 caches within the partition units (e.g., partition units  220 A- 220 N of  FIG. 2A ) that are shared among all processing clusters  214  and may be used to transfer data between threads. The graphics multiprocessor  234  may also access off-chip global memory, which can include one or more of local parallel processor memory and/or system memory. Any memory external to the parallel processing unit  202  may be used as global memory. Embodiments in which the processing cluster  214  includes multiple instances of the graphics multiprocessor  234  can share common instructions and data, which may be stored in the L1 cache  248 . 
     Each processing cluster  214  may include an MMU  245  (memory management unit) that is configured to map virtual addresses into physical addresses. In other embodiments, one or more instances of the MMU  245  may reside within the memory interface  218  of  FIG. 2A . The MMU  245  includes a set of page table entries (PTEs) used to map a virtual address to a physical address of a tile and optionally a cache line index. The MMU  245  may include address translation lookaside buffers (TLB) or caches that may reside within the graphics multiprocessor  234  or the L1 cache or processing cluster  214 . The physical address is processed to distribute surface data access locality to allow efficient request interleaving among partition units. The cache line index may be used to determine whether a request for a cache line is a hit or miss. 
     In graphics and computing applications, a processing cluster  214  may be configured such that each graphics multiprocessor  234  is coupled to a texture unit  236  for performing texture mapping operations, e.g., determining texture sample positions, reading texture data, and filtering the texture data. Texture data is read from an internal texture L1 cache (not shown) or in some embodiments from the L1 cache within graphics multiprocessor  234  and is fetched from an L2 cache, local parallel processor memory, or system memory, as needed. Each graphics multiprocessor  234  outputs processed tasks to the data crossbar  240  to provide the processed task to another processing cluster  214  for further processing or to store the processed task in an L2 cache, local parallel processor memory, or system memory via the memory crossbar  216 . A preROP  242  (pre-raster operations unit) is configured to receive data from graphics multiprocessor  234 , direct data to ROP units, which may be located with partition units as described herein (e.g., partition units  220 A- 220 N of  FIG. 2A ). The preROP  242  unit can perform optimizations for color blending, organize pixel color data, and perform address translations. 
     It will be appreciated that the core architecture described herein is illustrative and that variations and modifications are possible. Any number of processing units, e.g., graphics multiprocessor  234 , texture units  236 , preROPs  242 , etc., may be included within a processing cluster  214 . Further, while only one processing cluster  214  is shown, a parallel processing unit as described herein may include any number of instances of the processing cluster  214 . In one embodiment, each processing cluster  214  can be configured to operate independently of other processing clusters  214  using separate and distinct processing units, L1 caches, etc. 
       FIG. 2D  shows a graphics multiprocessor  234 , according to one embodiment. In such embodiment the graphics multiprocessor  234  couples with the pipeline manager  232  of the processing cluster  214 . The graphics multiprocessor  234  has an execution pipeline including but not limited to an instruction cache  252 , an instruction unit  254 , an address mapping unit  256 , a register file  258 , one or more general purpose graphics processing unit (GPGPU) cores  262 , and one or more load/store units  266 . The GPGPU cores  262  and load/store units  266  are coupled with cache memory  272  and shared memory  270  via a memory and cache interconnect  268 . In one embodiment the graphics multiprocessor  234  additionally includes tensor and/or ray-tracing cores  263  that include hardware logic to accelerate matrix and/or ray-tracing operations. 
     In one embodiment, the instruction cache  252  receives a stream of instructions to execute from the pipeline manager  232 . The instructions are cached in the instruction cache  252  and dispatched for execution by the instruction unit  254 . The instruction unit  254  can dispatch instructions as thread groups (e.g., warps), with each thread of the thread group assigned to a different execution unit within GPGPU core  262 . An instruction can access any of a local, shared, or global address space by specifying an address within a unified address space. The address mapping unit  256  can be used to translate addresses in the unified address space into a distinct memory address that can be accessed by the load/store units  266 . 
     The register file  258  provides a set of registers for the functional units of the graphics multiprocessor  234 . The register file  258  provides temporary storage for operands connected to the data paths of the functional units (e.g., GPGPU cores  262 , load/store units  266 ) of the graphics multiprocessor  234 . In one embodiment, the register file  258  is divided between each of the functional units such that each functional unit is allocated a dedicated portion of the register file  258 . In one embodiment, the register file  258  is divided between the different warps being executed by the graphics multiprocessor  234 . 
     The GPGPU cores  262  can each include floating point units (FPUs) and/or integer arithmetic logic units (ALUs) that are used to execute instructions of the graphics multiprocessor  234 . The GPGPU cores  262  can be similar in architecture or can differ in architecture, according to embodiments. For example and in one embodiment, a first portion of the GPGPU cores  262  include a single precision FPU and an integer ALU while a second portion of the GPGPU cores include a double precision FPU. In one embodiment the FPUs can implement the IEEE 754-2008 standard for floating point arithmetic or enable variable precision floating point arithmetic. The graphics multiprocessor  234  can additionally include one or more fixed function or special function units to perform specific functions such as copy rectangle or pixel blending operations. In one embodiment one or more of the GPGPU cores can also include fixed or special function logic. 
     In one embodiment the GPGPU cores  262  include SIMD logic capable of performing a single instruction on multiple sets of data. In one embodiment GPGPU cores  262  can physically execute SIMD4, SIMD8, and SIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32 instructions. The SIMD instructions for the GPGPU cores can be generated at compile time by a shader compiler or automatically generated when executing programs written and compiled for single program multiple data (SPMD) or SIMT architectures. Multiple threads of a program configured for the SIMT execution model can be executed via a single SIMD instruction. For example and in one embodiment, eight SIMT threads that perform the same or similar operations can be executed in parallel via a single SIMD8 logic unit. 
     The memory and cache interconnect  268  is an interconnect network that connects each of the functional units of the graphics multiprocessor  234  to the register file  258  and to the shared memory  270 . In one embodiment, the memory and cache interconnect  268  is a crossbar interconnect that allows the load/store unit  266  to implement load and store operations between the shared memory  270  and the register file  258 . The register file  258  can operate at the same frequency as the GPGPU cores  262 , thus data transfer between the GPGPU cores  262  and the register file  258  is very low latency. The shared memory  270  can be used to enable communication between threads that execute on the functional units within the graphics multiprocessor  234 . The cache memory  272  can be used as a data cache for example, to cache texture data communicated between the functional units and the texture unit  236 . The shared memory  270  can also be used as a program managed cached. Threads executing on the GPGPU cores  262  can programmatically store data within the shared memory in addition to the automatically cached data that is stored within the cache memory  272 . 
       FIG. 3A-3C  illustrate additional graphics multiprocessors, according to embodiments.  FIG. 3A-3B  illustrate graphics multiprocessors  325 ,  350 , which are variants of the graphics multiprocessor  234  of  FIG. 2C .  FIG. 3C  illustrates a graphics processing unit (GPU)  380  which includes dedicated sets of graphics processing resources arranged into multi-core groups  365 A- 365 N. The illustrated graphics multiprocessors  325 ,  350  and the multi-core groups  365 A- 365 N can be streaming multiprocessor (SM) capable of simultaneous execution of a large number of execution threads. 
       FIG. 3A  shows a graphics multiprocessor  325  according to an additional embodiment. The graphics multiprocessor  325  includes multiple additional instances of execution resource units relative to the graphics multiprocessor  234  of  FIG. 2D . For example, the graphics multiprocessor  325  can include multiple instances of the instruction unit  332 A- 332 B, register file  334 A- 334 B, and texture unit(s)  344 A- 344 B. The graphics multiprocessor  325  also includes multiple sets of graphics or compute execution units (e.g., GPGPU core  336 A- 336 B, tensor core  337 A- 337 B, ray-tracing core  338 A- 338 B) and multiple sets of load/store units  340 A- 340 B. In one embodiment the execution resource units have a common instruction cache  330 , texture and/or data cache memory  342 , and shared memory  346 . 
     The various components can communicate via an interconnect fabric  327 . In one embodiment the interconnect fabric  327  includes one or more crossbar switches to enable communication between the various components of the graphics multiprocessor  325 . In one embodiment the interconnect fabric  327  is a separate, high-speed network fabric layer upon which each component of the graphics multiprocessor  325  is stacked. The components of the graphics multiprocessor  325  communicate with remote components via the interconnect fabric  327 . For example, the GPGPU cores  336 A- 336 B,  337 A- 337 B, and  3378 A- 338 B can each communicate with shared memory  346  via the interconnect fabric  327 . The interconnect fabric  327  can arbitrate communication within the graphics multiprocessor  325  to ensure a fair bandwidth allocation between components. 
       FIG. 3B  shows a graphics multiprocessor  350  according to an additional embodiment. The graphics processor includes multiple sets of execution resources  356 A- 356 D, where each set of execution resource includes multiple instruction units, register files, GPGPU cores, and load store units, as illustrated in  FIG. 2D  and  FIG. 3A . The execution resources  356 A- 356 D can work in concert with texture unit(s)  360 A- 360 D for texture operations, while sharing an instruction cache  354 , and shared memory  353 . In one embodiment the execution resources  356 A- 356 D can share an instruction cache  354  and shared memory  353 , as well as multiple instances of a texture and/or data cache memory  358 A- 358 B. The various components can communicate via an interconnect fabric  352  similar to the interconnect fabric  327  of  FIG. 3A . 
     Persons skilled in the art will understand that the architecture described in  FIGS. 1, 2A-2D, and 3A-3B  are descriptive and not limiting as to the scope of the present embodiments. Thus, the techniques described herein may be implemented on any properly configured processing unit, including, without limitation, one or more mobile application processors, one or more desktop or server central processing units (CPUs) including multi-core CPUs, one or more parallel processing units, such as the parallel processing unit  202  of  FIG. 2A , as well as one or more graphics processors or special purpose processing units, without departure from the scope of the embodiments described herein. 
     In some embodiments a parallel processor or GPGPU as described herein is communicatively coupled to host/processor cores to accelerate graphics operations, machine-learning operations, pattern analysis operations, and various general purpose GPU (GPGPU) functions. The GPU may be communicatively coupled to the host processor/cores over a bus or other interconnect (e.g., a high speed interconnect such as PCIe or NVLink). In other embodiments, the GPU may be integrated on the same package or chip as the cores and communicatively coupled to the cores over an internal processor bus/interconnect (i.e., internal to the package or chip). Regardless of the manner in which the GPU is connected, the processor cores may allocate work to the GPU in the form of sequences of commands/instructions contained in a work descriptor. The GPU then uses dedicated circuitry/logic for efficiently processing these commands/instructions. 
       FIG. 3C  illustrates a graphics processing unit (GPU)  380  which includes dedicated sets of graphics processing resources arranged into multi-core groups  365 A-N. While the details of only a single multi-core group  365 A are provided, it will be appreciated that the other multi-core groups  365 B- 365 N may be equipped with the same or similar sets of graphics processing resources. 
     As illustrated, a multi-core group  365 A may include a set of graphics cores  370 , a set of tensor cores  371 , and a set of ray tracing cores  372 . A scheduler/dispatcher  368  schedules and dispatches the graphics threads for execution on the various cores  370 ,  371 ,  372 . A set of register files  369  store operand values used by the cores  370 ,  371 ,  372  when executing the graphics threads. These may include, for example, integer registers for storing integer values, floating point registers for storing floating point values, vector registers for storing packed data elements (integer and/or floating point data elements) and tile registers for storing tensor/matrix values. In one embodiment, the tile registers are implemented as combined sets of vector registers. 
     One or more combined level 1 (L1) caches and shared memory units  373  store graphics data such as texture data, vertex data, pixel data, ray data, bounding volume data, etc., locally within each multi-core group  365 A. One or more texture units  374  can also be used to perform texturing operations, such as texture mapping and sampling. A Level 2 (L2) cache  375  shared by all or a subset of the multi-core groups  365 A- 365 N stores graphics data and/or instructions for multiple concurrent graphics threads. As illustrated, the L2 cache  375  may be shared across a plurality of multi-core groups  365 A- 365 N. One or more memory controllers  367  couple the GPU  380  to a memory  366  which may be a system memory (e.g., DRAM) and/or a dedicated graphics memory (e.g., GDDR6 memory). 
     Input/output (I/O) circuitry  363  couples the GPU  380  to one or more I/O devices  362  such as digital signal processors (DSPs), network controllers, or user input devices. An on-chip interconnect may be used to couple the I/O devices  362  to the GPU  380  and memory  366 . One or more I/O memory management units (IOMMUs)  364  of the I/O circuitry  3195  couple the I/O devices  362  directly to the system memory  366 . In one embodiment, the IOMMU  364  manages multiple sets of page tables to map virtual addresses to physical addresses in system memory  366 . In this embodiment, the I/O devices  362 , CPU(s)  361 , and GPU(s)  380  may share the same virtual address space. 
     In one implementation, the IOMMU  364  supports virtualization. In this case, it may manage a first set of page tables to map guest/graphics virtual addresses to guest/graphics physical addresses and a second set of page tables to map the guest/graphics physical addresses to system/host physical addresses (e.g., within system memory  366 ). The base addresses of each of the first and second sets of page tables may be stored in control registers and swapped out on a context switch (e.g., so that the new context is provided with access to the relevant set of page tables). While not illustrated in  FIG. 3C , each of the cores  370 ,  371 ,  372  and/or multi-core groups  365 A- 365 N may include translation lookaside buffers (TLBs) to cache guest virtual to guest physical translations, guest physical to host physical translations, and guest virtual to host physical translations. 
     In one embodiment, the CPUs  361 , GPUs  380 , and I/O devices  362  are integrated on a single semiconductor chip and/or chip package. The illustrated memory  366  may be integrated on the same chip or may be coupled to the memory controllers  367  via an off-chip interface. In one implementation, the memory  366  comprises GDDR6 memory which shares the same virtual address space as other physical system-level memories, although the underlying principles of the invention are not limited to this specific implementation. 
     In one embodiment, the tensor cores  371  include a plurality of execution units specifically designed to perform matrix operations, which are the fundamental compute operation used to perform deep learning operations. For example, simultaneous matrix multiplication operations may be used for neural network training and inferencing. The tensor cores  371  may perform matrix processing using a variety of operand precisions including single precision floating-point (e.g., 32 bits), half-precision floating point (e.g., 16 bits), integer words (16 bits), bytes (8 bits), and half-bytes (4 bits). In one embodiment, a neural network implementation extracts features of each rendered scene, potentially combining details from multiple frames, to construct a high-quality final image. 
     In deep learning implementations, parallel matrix multiplication work may be scheduled for execution on the tensor cores  371 . The training of neural networks, in particular, requires a significant number matrix dot product operations. In order to process an inner-product formulation of an N×N×N matrix multiply, the tensor cores  371  may include at least N dot-product processing elements. Before the matrix multiply begins, one entire matrix is loaded into tile registers and at least one column of a second matrix is loaded each cycle for N cycles. Each cycle, there are N dot products that are processed. 
     Matrix elements may be stored at different precisions depending on the particular implementation, including 16-bit words, 8-bit bytes (e.g., INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes may be specified for the tensor cores  371  to ensure that the most efficient precision is used for different workloads (e.g., such as inferencing workloads which can tolerate quantization to bytes and half-bytes). 
     In one embodiment, the ray tracing cores  372  accelerate ray tracing operations for both real-time ray tracing and non-real-time ray tracing implementations. In particular, the ray tracing cores  372  include ray traversal/intersection circuitry for performing ray traversal using bounding volume hierarchies (BVHs) and identifying intersections between rays and primitives enclosed within the BVH volumes. The ray tracing cores  372  may also include circuitry for performing depth testing and culling (e.g., using a Z buffer or similar arrangement). In one implementation, the ray tracing cores  372  perform traversal and intersection operations in concert with the image denoising techniques described herein, at least a portion of which may be executed on the tensor cores  371 . For example, in one embodiment, the tensor cores  371  implement a deep learning neural network to perform denoising of frames generated by the ray tracing cores  372 . However, the CPU(s)  361 , graphics cores  370 , and/or ray tracing cores  372  may also implement all or a portion of the denoising and/or deep learning algorithms. 
     In addition, as described above, a distributed approach to denoising may be employed in which the GPU  380  is in a computing device coupled to other computing devices over a network or high speed interconnect. In this embodiment, the interconnected computing devices share neural network learning/training data to improve the speed with which the overall system learns to perform denoising for different types of image frames and/or different graphics applications. 
     In one embodiment, the ray tracing cores  372  process all BVH traversal and ray-primitive intersections, saving the graphics cores  370  from being overloaded with thousands of instructions per ray. In one embodiment, each ray tracing core  372  includes a first set of specialized circuitry for performing bounding box tests (e.g., for traversal operations) and a second set of specialized circuitry for performing the ray-triangle intersection tests (e.g., intersecting rays which have been traversed). Thus, in one embodiment, the multi-core group  365 A can simply launch a ray probe, and the ray tracing cores  372  independently perform ray traversal and intersection and return hit data (e.g., a hit, no hit, multiple hits, etc.) to the thread context. The other cores  370 ,  371  are freed to perform other graphics or compute work while the ray tracing cores  372  perform the traversal and intersection operations. 
     In one embodiment, each ray tracing core  372  includes a traversal unit to perform BVH testing operations and an intersection unit which performs ray-primitive intersection tests. The intersection unit generates a “hit”, “no hit”, or “multiple hit” response, which it provides to the appropriate thread. During the traversal and intersection operations, the execution resources of the other cores (e.g., graphics cores  370  and tensor cores  371 ) are freed to perform other forms of graphics work. 
     In one particular embodiment described below, a hybrid rasterization/ray tracing approach is used in which work is distributed between the graphics cores  370  and ray tracing cores  372 . 
     In one embodiment, the ray tracing cores  372  (and/or other cores  370 ,  371 ) include hardware support for a ray tracing instruction set such as Microsoft&#39;s DirectX Ray Tracing (DXR) which includes a DispatchRays command, as well as ray-generation, closest-hit, any-hit, and miss shaders, which enable the assignment of unique sets of shaders and textures for each object. Another ray tracing platform which may be supported by the ray tracing cores  372 , graphics cores  370  and tensor cores  371  is Vulkan 1.1.85. Note, however, that the underlying principles of the invention are not limited to any particular ray tracing ISA. 
     In general, the various cores  372 ,  371 ,  370  may support a ray tracing instruction set that includes instructions/functions for ray generation, closest hit, any hit, ray-primitive intersection, per-primitive and hierarchical bounding box construction, miss, visit, and exceptions. More specifically, one embodiment includes ray tracing instructions to perform the following functions: 
     Ray Generation—Ray generation instructions may be executed for each pixel, sample, or other user-defined work assignment. 
     Closest Hit—A closest hit instruction may be executed to locate the closest intersection point of a ray with primitives within a scene. 
     Any Hit—An any hit instruction identifies multiple intersections between a ray and primitives within a scene, potentially to identify a new closest intersection point. 
     Intersection—An intersection instruction performs a ray-primitive intersection test and outputs a result. 
     Per-primitive Bounding box Construction—This instruction builds a bounding box around a given primitive or group of primitives (e.g., when building a new BVH or other acceleration data structure). 
     Miss—Indicates that a ray misses all geometry within a scene, or specified region of a scene. 
     Visit—Indicates the children volumes a ray will traverse. 
     Exceptions—Includes various types of exception handlers (e.g., invoked for various error conditions). 
     Techniques for GPU to Host Processor Interconnection 
       FIG. 4A  illustrates an exemplary architecture in which a plurality of GPUs  410 - 413  are communicatively coupled to a plurality of multi-core processors  405 - 406  over high-speed links  440 A- 440 D (e.g., buses, point-to-point interconnects, etc.). In one embodiment, the high-speed links  440 A- 440 D support a communication throughput of 4 GB/s, 30 GB/s, 80 GB/s or higher, depending on the implementation. Various interconnect protocols may be used including, but not limited to, PCIe 4.0 or 5.0 and NVLink 2.0. However, the underlying principles of the invention are not limited to any particular communication protocol or throughput. 
     In addition, in one embodiment, two or more of the GPUs  410 - 413  are interconnected over high-speed links  442 A- 442 B, which may be implemented using the same or different protocols/links than those used for high-speed links  440 A- 440 D. Similarly, two or more of the multi-core processors  405 - 406  may be connected over high speed link  443  which may be symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s, 120 GB/s or higher. Alternatively, all communication between the various system components shown in  FIG. 4A  may be accomplished using the same protocols/links (e.g., over a common interconnection fabric). As mentioned, however, the underlying principles of the invention are not limited to any particular type of interconnect technology. 
     In one embodiment, each multi-core processor  405 - 406  is communicatively coupled to a processor memory  401 - 402 , via memory interconnects  430 A- 430 B, respectively, and each GPU  410 - 413  is communicatively coupled to GPU memory  420 - 423  over GPU memory interconnects  450 A- 450 D, respectively. The memory interconnects  430 A- 430 B and  450 A- 450 D may utilize the same or different memory access technologies. By way of example, and not limitation, the processor memories  401 - 402  and GPU memories  420 - 423  may be volatile memories such as dynamic random access memories (DRAMs) (including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatile memories such as 3D XPoint or Nano-Ram. In one embodiment, some portion of the memories may be volatile memory and another portion may be non-volatile memory (e.g., using a two-level memory (2LM) hierarchy). 
     As described below, although the various processors  405 - 406  and GPUs  410 - 413  may be physically coupled to a particular memory  401 - 402 ,  420 - 423 , respectively, a unified memory architecture may be implemented in which the same virtual system address space (also referred to as the “effective address” space) is distributed among all of the various physical memories. For example, processor memories  401 - 402  may each comprise 64 GB of the system memory address space and GPU memories  420 - 423  may each comprise 32 GB of the system memory address space (resulting in a total of 256 GB addressable memory in this example). 
       FIG. 4B  illustrates additional details for an interconnection between a multi-core processor  407  and a graphics acceleration module  446  in accordance with one embodiment. The graphics acceleration module  446  may include one or more GPU chips integrated on a line card which is coupled to the processor  407  via the high-speed link  440 . Alternatively, the graphics acceleration module  446  may be integrated on the same package or chip as the processor  407 . 
     The illustrated processor  407  includes a plurality of cores  460 A- 460 D, each with a translation lookaside buffer  461 A- 461 D and one or more caches  462 A- 462 D. The cores may include various other components for executing instructions and processing data which are not illustrated to avoid obscuring the underlying principles of the invention (e.g., instruction fetch units, branch prediction units, decoders, execution units, reorder buffers, etc.). The caches  462 A- 462 D may comprise level 1 (L1) and level 2 (L2) caches. In addition, one or more shared caches  456  may be included in the caching hierarchy and shared by sets of the cores  460 A- 460 D. For example, one embodiment of the processor  407  includes 24 cores, each with its own L1 cache, twelve shared L2 caches, and twelve shared L3 caches. In this embodiment, one of the L2 and L3 caches are shared by two adjacent cores. The processor  407  and the graphics accelerator integration module  446  connect with system memory  441 , which may include processor memories  401 - 402 . 
     Coherency is maintained for data and instructions stored in the various caches  462 A- 462 D,  456  and system memory  441  via inter-core communication over a coherence bus  464 . For example, each cache may have cache coherency logic/circuitry associated therewith to communicate to over the coherence bus  464  in response to detected reads or writes to particular cache lines. In one implementation, a cache snooping protocol is implemented over the coherence bus  464  to snoop cache accesses. Cache snooping/coherency techniques are well understood by those of skill in the art and will not be described in detail here to avoid obscuring the underlying principles of the invention. 
     In one embodiment, a proxy circuit  425  communicatively couples the graphics acceleration module  446  to the coherence bus  464 , allowing the graphics acceleration module  446  to participate in the cache coherence protocol as a peer of the cores. In particular, an interface  435  provides connectivity to the proxy circuit  425  over high-speed link  440  (e.g., a PCIe bus, NVLink, etc.) and an interface  437  connects the graphics acceleration module  446  to the high-speed link  440 . 
     In one implementation, an accelerator integration circuit  436  provides cache management, memory access, context management, and interrupt management services on behalf of a plurality of graphics processing engines  431 ,  432 , N of the graphics acceleration module  446 . The graphics processing engines  431 ,  432 , N may each comprise a separate graphics processing unit (GPU). Alternatively, the graphics processing engines  431 ,  432 , N may comprise different types of graphics processing engines within a GPU such as graphics execution units, media processing engines (e.g., video encoders/decoders), samplers, and blit engines. In other words, the graphics acceleration module may be a GPU with a plurality of graphics processing engines  431 - 432 , N or the graphics processing engines  431 - 432 , N may be individual GPUs integrated on a common package, line card, or chip. 
     In one embodiment, the accelerator integration circuit  436  includes a memory management unit (MMU)  439  for performing various memory management functions such as virtual-to-physical memory translations (also referred to as effective-to-real memory translations) and memory access protocols for accessing system memory  441 . The MMU  439  may also include a translation lookaside buffer (TLB) (not shown) for caching the virtual/effective to physical/real address translations. In one implementation, a cache  438  stores commands and data for efficient access by the graphics processing engines  431 - 432 , N. In one embodiment, the data stored in cache  438  and graphics memories  433 - 434 , M is kept coherent with the core caches  462 A- 462 D,  456  and system memory  441 . As mentioned, this may be accomplished via proxy circuit  425  which takes part in the cache coherency mechanism on behalf of cache  438  and memories  433 - 434 , M (e.g., sending updates to the cache  438  related to modifications/accesses of cache lines on processor caches  462 A- 462 D,  456  and receiving updates from the cache  438 ). 
     A set of registers  445  store context data for threads executed by the graphics processing engines  431 - 432 , N and a context management circuit  448  manages the thread contexts. For example, the context management circuit  448  may perform save and restore operations to save and restore contexts of the various threads during contexts switches (e.g., where a first thread is saved, and a second thread is stored so that the second thread can be execute by a graphics processing engine). For example, on a context switch, the context management circuit  448  may store current register values to a designated region in memory (e.g., identified by a context pointer). It may then restore the register values when returning to the context. In one embodiment, an interrupt management circuit  447  receives and processes interrupts received from system devices. 
     In one implementation, virtual/effective addresses from a graphics processing engine  431  are translated to real/physical addresses in system memory  441  by the MMU  439 . One embodiment of the accelerator integration circuit  436  supports multiple (e.g., 4, 8, 16) graphics accelerator modules  446  and/or other accelerator devices. The graphics accelerator module  446  may be dedicated to a single application executed on the processor  407  or may be shared between multiple applications. In one embodiment, a virtualized graphics execution environment is presented in which the resources of the graphics processing engines  431 - 432 , N are shared with multiple applications or virtual machines (VMs). The resources may be subdivided into “slices” which are allocated to different VMs and/or applications based on the processing requirements and priorities associated with the VMs and/or applications. 
     Thus, the accelerator integration circuit acts as a bridge to the system for the graphics acceleration module  446  and provides address translation and system memory cache services. In addition, the accelerator integration circuit  436  may provide virtualization facilities for the host processor to manage virtualization of the graphics processing engines, interrupts, and memory management. 
     Because hardware resources of the graphics processing engines  431 - 432 , N are mapped explicitly to the real address space seen by the host processor  407 , any host processor can address these resources directly using an effective address value. One function of the accelerator integration circuit  436 , in one embodiment, is the physical separation of the graphics processing engines  431 - 432 , N so that they appear to the system as independent units. 
     As mentioned, in the illustrated embodiment, one or more graphics memories  433 - 434 , M are coupled to each of the graphics processing engines  431 - 432 , N, respectively. The graphics memories  433 - 434 , M store instructions and data being processed by each of the graphics processing engines  431 - 432 , N. The graphics memories  433 - 434 , M may be volatile memories such as DRAMs (including stacked DRAMs), GDDR memory (e.g., GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3D XPoint or Nano-Ram. 
     In one embodiment, to reduce data traffic over the high-speed link  440 , biasing techniques are used to ensure that the data stored in graphics memories  433 - 434 , M is data which will be used most frequently by the graphics processing engines  431 - 432 , N and preferably not used by the cores  460 A- 460 D (at least not frequently). Similarly, the biasing mechanism attempts to keep data needed by the cores (and preferably not the graphics processing engines  431 - 432 , N) within the caches  462 A- 462 D,  456  of the cores and system memory  441 . 
       FIG. 4C  illustrates another embodiment in which the accelerator integration circuit  436  is integrated within the processor  407 . In this embodiment, the graphics processing engines  431 - 432 , N communicate directly over the high-speed link  440  to the accelerator integration circuit  436  via interface  437  and interface  435  (which, again, may be utilize any form of bus or interface protocol). The accelerator integration circuit  436  may perform the same operations as those described with respect to  FIG. 4B , but potentially at a higher throughput given its close proximity to the coherency bus  464  and caches  462 A- 462 D,  456 . 
     One embodiment supports different programming models including a dedicated-process programming model (no graphics acceleration module virtualization) and shared programming models (with virtualization). The latter may include programming models which are controlled by the accelerator integration circuit  436  and programming models which are controlled by the graphics acceleration module  446 . 
     In one embodiment of the dedicated process model, graphics processing engines  431 - 432 , N are dedicated to a single application or process under a single operating system. The single application can funnel other application requests to the graphics engines  431 - 432 , N, providing virtualization within a VM/partition. 
     In the dedicated-process programming models, the graphics processing engines  431 - 432 , N, may be shared by multiple VM/application partitions. The shared models require a system hypervisor to virtualize the graphics processing engines  431 - 432 , N to allow access by each operating system. For single-partition systems without a hypervisor, the graphics processing engines  431 - 432 , N are owned by the operating system. In both cases, the operating system can virtualize the graphics processing engines  431 - 432 , N to provide access to each process or application. 
     For the shared programming model, the graphics acceleration module  446  or an individual graphics processing engine  431 - 432 , N selects a process element using a process handle. In one embodiment, process elements are stored in system memory  441  and are addressable using the effective address to real address translation techniques described herein. The process handle may be an implementation-specific value provided to the host process when registering its context with the graphics processing engine  431 - 432 , N (that is, calling system software to add the process element to the process element linked list). The lower 16-bits of the process handle may be the offset of the process element within the process element linked list. 
       FIG. 4D  illustrates an exemplary accelerator integration slice  490 . As used herein, a “slice” comprises a specified portion of the processing resources of the accelerator integration circuit  436 . Application effective address space  482  within system memory  441  stores process elements  483 . In one embodiment, the process elements  483  are stored in response to GPU invocations  481  from applications  480  executed on the processor  407 . A process element  483  contains the process state for the corresponding application  480 . A work descriptor (WD)  484  contained in the process element  483  can be a single job requested by an application or may contain a pointer to a queue of jobs. In the latter case, the WD  484  is a pointer to the job request queue in the application&#39;s address space  482 . 
     The graphics acceleration module  446  and/or the individual graphics processing engines  431 - 432 , N can be shared by all or a subset of the processes in the system. Embodiments of the invention include an infrastructure for setting up the process state and sending a WD  484  to a graphics acceleration module  446  to start a job in a virtualized environment. 
     In one implementation, the dedicated-process programming model is implementation-specific. In this model, a single process owns the graphics acceleration module  446  or an individual graphics processing engine  431 . Because the graphics acceleration module  446  is owned by a single process, the hypervisor initializes the accelerator integration circuit  436  for the owning partition and the operating system initializes the accelerator integration circuit  436  for the owning process at the time when the graphics acceleration module  446  is assigned. 
     In operation, a WD fetch unit  491  in the accelerator integration slice  490  fetches the next WD  484  which includes an indication of the work to be done by one of the graphics processing engines of the graphics acceleration module  446 . Data from the WD  484  may be stored in registers  445  and used by the MMU  439 , interrupt management circuit  447  and/or context management circuit  448  as illustrated. For example, one embodiment of the MMU  439  includes segment/page walk circuitry for accessing segment/page tables  486  within the OS virtual address space  485 . The interrupt management circuit  447  may process interrupt events  492  received from the graphics acceleration module  446 . When performing graphics operations, an effective address  493  generated by a graphics processing engine  431 - 432 , N is translated to a real address by the MMU  439 . 
     In one embodiment, the same set of registers  445  are duplicated for each graphics processing engine  431 - 432 , N and/or graphics acceleration module  446  and may be initialized by the hypervisor or operating system. Each of these duplicated registers may be included in an accelerator integration slice  490 . Exemplary registers that may be initialized by the hypervisor are shown in Table 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Hypervisor Initialized Registers 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 1 
                 Slice Control Register 
               
               
                 2 
                 Real Address (RA) Scheduled Processes Area Pointer 
               
               
                 3 
                 Authority Mask Override Register 
               
               
                 4 
                 Interrupt Vector Table Entry Offset 
               
               
                 5 
                 Interrupt Vector Table Entry Limit 
               
               
                 6 
                 State Register 
               
               
                 7 
                 Logical Partition ID 
               
               
                 8 
                 Real address (RA) Hypervisor Accelerator Utilization Record Pointer 
               
               
                 9 
                 Storage Description Register 
               
               
                   
               
            
           
         
       
     
     Exemplary registers that may be initialized by the operating system are shown in Table 2. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Operating System Initialized Registers 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 1 
                 Process and Thread Identification 
               
               
                 2 
                 Effective Address (EA) Context Save/Restore Pointer 
               
               
                 3 
                 Virtual Address (VA) Accelerator Utilization Record Pointer 
               
               
                 4 
                 Virtual Address (VA) Storage Segment Table Pointer 
               
               
                 5 
                 Authority Mask 
               
               
                 6 
                 Work descriptor 
               
               
                   
               
            
           
         
       
     
     In one embodiment, each WD  484  is specific to a particular graphics acceleration module  446  and/or graphics processing engine  431 - 432 , N. It contains all the information a graphics processing engine  431 - 432 , N requires to do its work or it can be a pointer to a memory location where the application has set up a command queue of work to be completed. 
       FIG. 4E  illustrates additional details for one embodiment of a shared model. This embodiment includes a hypervisor real address space  498  in which a process element list  499  is stored. The hypervisor real address space  498  is accessible via a hypervisor  496  which virtualizes the graphics acceleration module engines for the operating system  495 . 
     The shared programming models allow for all or a subset of processes from all or a subset of partitions in the system to use a graphics acceleration module  446 . There are two programming models where the graphics acceleration module  446  is shared by multiple processes and partitions: time-sliced shared and graphics directed shared. 
     In this model, the system hypervisor  496  owns the graphics acceleration module  446  and makes its function available to all operating systems  495 . For a graphics acceleration module  446  to support virtualization by the system hypervisor  496 , the graphics acceleration module  446  may adhere to the following requirements: 1) An application&#39;s job request must be autonomous (that is, the state does not need to be maintained between jobs), or the graphics acceleration module  446  must provide a context save and restore mechanism. 2) An application&#39;s job request is guaranteed by the graphics acceleration module  446  to complete in a specified amount of time, including any translation faults, or the graphics acceleration module  446  provides the ability to preempt the processing of the job. 3) The graphics acceleration module  446  must be guaranteed fairness between processes when operating in the directed shared programming model. 
     In one embodiment, for the shared model, the application  480  is required to make an operating system  495  system call with a graphics acceleration module  446  type, a work descriptor (WD), an authority mask register (AMR) value, and a context save/restore area pointer (CSRP). The graphics acceleration module  446  type describes the targeted acceleration function for the system call. The graphics acceleration module  446  type may be a system-specific value. The WD is formatted specifically for the graphics acceleration module  446  and can be in the form of a graphics acceleration module  446  command, an effective address pointer to a user-defined structure, an effective address pointer to a queue of commands, or any other data structure to describe the work to be done by the graphics acceleration module  446 . In one embodiment, the AMR value is the AMR state to use for the current process. The value passed to the operating system is similar to an application setting the AMR. If the accelerator integration circuit  436  and graphics acceleration module  446  implementations do not support a User Authority Mask Override Register (UAMOR), the operating system may apply the current UAMOR value to the AMR value before passing the AMR in the hypervisor call. The hypervisor  496  may optionally apply the current Authority Mask Override Register (AMOR) value before placing the AMR into the process element  483 . In one embodiment, the CSRP is one of the registers  445  containing the effective address of an area in the application&#39;s address space  482  for the graphics acceleration module  446  to save and restore the context state. This pointer is optional if no state is required to be saved between jobs or when a job is preempted. The context save/restore area may be pinned system memory. 
     Upon receiving the system call, the operating system  495  may verify that the application  480  has registered and been given the authority to use the graphics acceleration module  446 . The operating system  495  then calls the hypervisor  496  with the information shown in Table 3. 
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 OS Hypervisor Call Parameters 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 1 
                 A work descriptor (WD) 
               
               
                 2 
                 An Authority Mask Register (AMR) value (potentially masked). 
               
               
                 3 
                 An effective address (EA) Context Save/Restore Area Pointer (CSRP) 
               
               
                 4 
                 A process ID (PID) and optional thread ID (TID) 
               
               
                 5 
                 A virtual address (VA) accelerator utilization record pointer (AURP) 
               
               
                 6 
                 The virtual address of the storage segment table pointer (SSTP) 
               
               
                 7 
                 A logical interrupt service number (LISN) 
               
               
                   
               
            
           
         
       
     
     Upon receiving the hypervisor call, the hypervisor  496  verifies that the operating system  495  has registered and been given the authority to use the graphics acceleration module  446 . The hypervisor  496  then puts the process element  483  into the process element linked list for the corresponding graphics acceleration module  446  type. The process element may include the information shown in Table 4. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Process Element Information 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 1 
                 A work descriptor (WD) 
               
               
                 2 
                 An Authority Mask Register (AMR) value (potentially masked). 
               
               
                 3 
                 An effective address (EA) Context Save/Restore Area Pointer  
               
               
                   
                 (CSRP) 
               
               
                 4 
                 A process ID (PID) and optional thread ID (TID) 
               
               
                 5 
                 A virtual address (VA) accelerator utilization record pointer  
               
               
                   
                 (AURP) 
               
               
                 6 
                 The virtual address of the storage segment table pointer (SSTP) 
               
               
                 7 
                 A logical interrupt service number (LISN) 
               
               
                 8 
                 Interrupt vector table, derived from the hypervisor call parameters. 
               
               
                 9 
                 A state register (SR) value 
               
               
                 10 
                 A logical partition ID (LPID) 
               
               
                 11 
                 A real address (RA) hypervisor accelerator utilization record  
               
               
                   
                 pointer 
               
               
                 12 
                 The Storage Descriptor Register (SDR) 
               
               
                   
               
            
           
         
       
     
     In one embodiment, the hypervisor initializes a plurality of accelerator integration slice  490  registers  445 . 
     As illustrated in  FIG. 4F , one embodiment of the invention employs a unified memory addressable via a common virtual memory address space used to access the physical processor memories  401 - 402  and GPU memories  420 - 423 . In this implementation, operations executed on the GPUs  410 - 413  utilize the same virtual/effective memory address space to access the processors memories  401 - 402  and vice versa, thereby simplifying programmability. In one embodiment, a first portion of the virtual/effective address space is allocated to the processor memory  401 , a second portion to the second processor memory  402 , a third portion to the GPU memory  420 , and so on. The entire virtual/effective memory space (sometimes referred to as the effective address space) is thereby distributed across each of the processor memories  401 - 402  and GPU memories  420 - 423 , allowing any processor or GPU to access any physical memory with a virtual address mapped to that memory. 
     In one embodiment, bias/coherence management circuitry  494 A- 494 E within one or more of the MMUs  439 A- 439 E ensures cache coherence between the caches of the host processors (e.g.,  405 ) and the GPUs  410 - 413  and implements biasing techniques indicating the physical memories in which certain types of data should be stored. While multiple instances of bias/coherence management circuitry  494 A- 494 E are illustrated in  FIG. 4F , the bias/coherence circuitry may be implemented within the MMU of one or more host processors  405  and/or within the accelerator integration circuit  436 . 
     One embodiment allows GPU-attached memory  420 - 423  to be mapped as part of system memory, and accessed using shared virtual memory (SVM) technology, but without suffering the typical performance drawbacks associated with full system cache coherence. The ability to GPU-attached memory  420 - 423  to be accessed as system memory without onerous cache coherence overhead provides a beneficial operating environment for GPU offload. This arrangement allows the host processor  405  software to setup operands and access computation results, without the overhead of tradition I/O DMA data copies. Such traditional copies involve driver calls, interrupts and memory mapped I/O (MMIO) accesses that are all inefficient relative to simple memory accesses. At the same time, the ability to access GPU attached memory  420 - 423  without cache coherence overheads can be critical to the execution time of an offloaded computation. In cases with substantial streaming write memory traffic, for example, cache coherence overhead can significantly reduce the effective write bandwidth seen by a GPU  410 - 413 . The efficiency of operand setup, the efficiency of results access, and the efficiency of GPU computation all play a role in determining the effectiveness of GPU offload. 
     In one implementation, the selection of between GPU bias and host processor bias is driven by a bias tracker data structure. A bias table may be used, for example, which may be a page-granular structure (i.e., controlled at the granularity of a memory page) that includes 1 or 2 bits per GPU-attached memory page. The bias table may be implemented in a stolen memory range of one or more GPU-attached memories  420 - 423 , with or without a bias cache in the GPU  410 - 413  (e.g., to cache frequently/recently used entries of the bias table). Alternatively, the entire bias table may be maintained within the GPU. 
     In one implementation, the bias table entry associated with each access to the GPU-attached memory  420 - 423  is accessed prior the actual access to the GPU memory, causing the following operations. First, local requests from the GPU  410 - 413  that find their page in GPU bias are forwarded directly to a corresponding GPU memory  420 - 423 . Local requests from the GPU that find their page in host bias are forwarded to the processor  405  (e.g., over a high-speed link as discussed above). In one embodiment, requests from the processor  405  that find the requested page in host processor bias complete the request like a normal memory read. Alternatively, requests directed to a GPU-biased page may be forwarded to the GPU  410 - 413 . The GPU may then transition the page to a host processor bias if it is not currently using the page. 
     The bias state of a page can be changed either by a software-based mechanism, a hardware-assisted software-based mechanism, or, for a limited set of cases, a purely hardware-based mechanism. 
     One mechanism for changing the bias state employs an API call (e.g. OpenCL), which, in turn, calls the GPU&#39;s device driver which, in turn, sends a message (or enqueues a command descriptor) to the GPU directing it to change the bias state and, for some transitions, perform a cache flushing operation in the host. The cache flushing operation is required for a transition from host processor  405  bias to GPU bias, but is not required for the opposite transition. 
     In one embodiment, cache coherency is maintained by temporarily rendering GPU-biased pages uncacheable by the host processor  405 . To access these pages, the processor  405  may request access from the GPU  410  which may or may not grant access right away, depending on the implementation. Thus, to reduce communication between the host processor  405  and GPU  410  it is beneficial to ensure that GPU-biased pages are those which are required by the GPU but not the host processor  405  and vice versa. 
     Graphics Processing Pipeline 
       FIG. 5  illustrates a graphics processing pipeline  500 , according to an embodiment. In one embodiment a graphics processor can implement the illustrated graphics processing pipeline  500 . The graphics processor can be included within the parallel processing subsystems as described herein, such as the parallel processor  200  of  FIG. 2A , which, in one embodiment, is a variant of the parallel processor(s)  112  of  FIG. 1 . The various parallel processing systems can implement the graphics processing pipeline  500  via one or more instances of the parallel processing unit (e.g., parallel processing unit  202  of  FIG. 2A ) as described herein. For example, a shader unit (e.g., graphics multiprocessor  234  of  FIG. 2C ) may be configured to perform the functions of one or more of a vertex processing unit  504 , a tessellation control processing unit  508 , a tessellation evaluation processing unit  512 , a geometry processing unit  516 , and a fragment/pixel processing unit  524 . The functions of data assembler  502 , primitive assemblers  506 ,  514 ,  518 , tessellation unit  510 , rasterizer  522 , and raster operations unit  526  may also be performed by other processing engines within a processing cluster (e.g., processing cluster  214  of  FIG. 2A ) and a corresponding partition unit (e.g., partition unit  220 A- 220 N of  FIG. 2A ). The graphics processing pipeline  500  may also be implemented using dedicated processing units for one or more functions. In one embodiment, one or more portions of the graphics processing pipeline  500  can be performed by parallel processing logic within a general purpose processor (e.g., CPU). In one embodiment, one or more portions of the graphics processing pipeline  500  can access on-chip memory (e.g., parallel processor memory  222  as in  FIG. 2A ) via a memory interface  528 , which may be an instance of the memory interface  218  of  FIG. 2A . 
     In one embodiment the data assembler  502  is a processing unit that collects vertex data for surfaces and primitives. The data assembler  502  then outputs the vertex data, including the vertex attributes, to the vertex processing unit  504 . The vertex processing unit  504  is a programmable execution unit that executes vertex shader programs, lighting and transforming vertex data as specified by the vertex shader programs. The vertex processing unit  504  reads data that is stored in cache, local or system memory for use in processing the vertex data and may be programmed to transform the vertex data from an object-based coordinate representation to a world space coordinate space or a normalized device coordinate space. 
     A first instance of a primitive assembler  506  receives vertex attributes from the vertex processing unit  504 . The primitive assembler  506  readings stored vertex attributes as needed and constructs graphics primitives for processing by tessellation control processing unit  508 . The graphics primitives include triangles, line segments, points, patches, and so forth, as supported by various graphics processing application programming interfaces (APIs). 
     The tessellation control processing unit  508  treats the input vertices as control points for a geometric patch. The control points are transformed from an input representation from the patch (e.g., the patch&#39;s bases) to a representation that is suitable for use in surface evaluation by the tessellation evaluation processing unit  512 . The tessellation control processing unit  508  can also compute tessellation factors for edges of geometric patches. A tessellation factor applies to a single edge and quantifies a view-dependent level of detail associated with the edge. A tessellation unit  510  is configured to receive the tessellation factors for edges of a patch and to tessellate the patch into multiple geometric primitives such as line, triangle, or quadrilateral primitives, which are transmitted to a tessellation evaluation processing unit  512 . The tessellation evaluation processing unit  512  operates on parameterized coordinates of the subdivided patch to generate a surface representation and vertex attributes for each vertex associated with the geometric primitives. 
     A second instance of a primitive assembler  514  receives vertex attributes from the tessellation evaluation processing unit  512 , reading stored vertex attributes as needed, and constructs graphics primitives for processing by the geometry processing unit  516 . The geometry processing unit  516  is a programmable execution unit that executes geometry shader programs to transform graphics primitives received from primitive assembler  514  as specified by the geometry shader programs. In one embodiment the geometry processing unit  516  is programmed to subdivide the graphics primitives into one or more new graphics primitives and calculate parameters used to rasterize the new graphics primitives. 
     In some embodiments the geometry processing unit  516  can add or delete elements in the geometry stream. The geometry processing unit  516  outputs the parameters and vertices specifying new graphics primitives to primitive assembler  518 . The primitive assembler  518  receives the parameters and vertices from the geometry processing unit  516  and constructs graphics primitives for processing by a viewport scale, cull, and clip unit  520 . The geometry processing unit  516  reads data that is stored in parallel processor memory or system memory for use in processing the geometry data. The viewport scale, cull, and clip unit  520  performs clipping, culling, and viewport scaling and outputs processed graphics primitives to a rasterizer  522 . 
     The rasterizer  522  can perform depth culling and other depth-based optimizations. The rasterizer  522  also performs scan conversion on the new graphics primitives to generate fragments and output those fragments and associated coverage data to the fragment/pixel processing unit  524 . The fragment/pixel processing unit  524  is a programmable execution unit that is configured to execute fragment shader programs or pixel shader programs. The fragment/pixel processing unit  524  transforming fragments or pixels received from rasterizer  522 , as specified by the fragment or pixel shader programs. For example, the fragment/pixel processing unit  524  may be programmed to perform operations included but not limited to texture mapping, shading, blending, texture correction and perspective correction to produce shaded fragments or pixels that are output to a raster operations unit  526 . The fragment/pixel processing unit  524  can read data that is stored in either the parallel processor memory or the system memory for use when processing the fragment data. Fragment or pixel shader programs may be configured to shade at sample, pixel, tile, or other granularities depending on the sampling rate configured for the processing units. 
     The raster operations unit  526  is a processing unit that performs raster operations including, but not limited to stencil, z-test, blending, and the like, and outputs pixel data as processed graphics data to be stored in graphics memory (e.g., parallel processor memory  222  as in  FIG. 2A , and/or system memory  104  as in  FIG. 1 ), to be displayed on the one or more display device(s)  110  or for further processing by one of the one or more processor(s)  102  or parallel processor(s)  112 . In some embodiments the raster operations unit  526  is configured to compress z or color data that is written to memory and decompress z or color data that is read from memory. 
     Machine Learning Overview 
     The architecture described above can be applied to perform training and inference operations using machine learning models. Machine learning has been successful at solving many kinds of tasks. The computations that arise when training and using machine learning algorithms (e.g., neural networks) lend themselves naturally to efficient parallel implementations. Accordingly, parallel processors such as general-purpose graphic processing units (GPGPUs) have played a significant role in the practical implementation of deep neural networks. Parallel graphics processors with single instruction, multiple thread (SIMT) architectures are designed to maximize the amount of parallel processing in the graphics pipeline. In an SIMT architecture, groups of parallel threads attempt to execute program instructions synchronously together as often as possible to increase processing efficiency. The efficiency provided by parallel machine learning algorithm implementations allows the use of high capacity networks and enables those networks to be trained on larger datasets. 
     A machine learning algorithm is an algorithm that can learn based on a set of data. Embodiments of machine learning algorithms can be designed to model high-level abstractions within a data set. For example, image recognition algorithms can be used to determine which of several categories to which a given input belong; regression algorithms can output a numerical value given an input; and pattern recognition algorithms can be used to generate translated text or perform text to speech and/or speech recognition. 
     An exemplary type of machine learning algorithm is a neural network. There are many types of neural networks; a simple type of neural network is a feedforward network. A feedforward network may be implemented as an acyclic graph in which the nodes are arranged in layers. Typically, a feedforward network topology includes an input layer and an output layer that are separated by at least one hidden layer. The hidden layer transforms input received by the input layer into a representation that is useful for generating output in the output layer. The network nodes are fully connected via edges to the nodes in adjacent layers, but there are no edges between nodes within each layer. Data received at the nodes of an input layer of a feedforward network are propagated (i.e., “fed forward”) to the nodes of the output layer via an activation function that calculates the states of the nodes of each successive layer in the network based on coefficients (“weights”) respectively associated with each of the edges connecting the layers. Depending on the specific model being represented by the algorithm being executed, the output from the neural network algorithm can take various forms. 
     Before a machine learning algorithm can be used to model a particular problem, the algorithm is trained using a training data set. Training a neural network involves selecting a network topology, using a set of training data representing a problem being modeled by the network, and adjusting the weights until the network model performs with a minimal error for all instances of the training data set. For example, during a supervised learning training process for a neural network, the output produced by the network in response to the input representing an instance in a training data set is compared to the “correct” labeled output for that instance, an error signal representing the difference between the output and the labeled output is calculated, and the weights associated with the connections are adjusted to minimize that error as the error signal is backward propagated through the layers of the network. The network is considered “trained” when the errors for each of the outputs generated from the instances of the training data set are minimized. 
     The accuracy of a machine learning algorithm can be affected significantly by the quality of the data set used to train the algorithm. The training process can be computationally intensive and may require a significant amount of time on a conventional general-purpose processor. Accordingly, parallel processing hardware is used to train many types of machine learning algorithms. This is particularly useful for optimizing the training of neural networks, as the computations performed in adjusting the coefficients in neural networks lend themselves naturally to parallel implementations. Specifically, many machine learning algorithms and software applications have been adapted to make use of the parallel processing hardware within general-purpose graphics processing devices. 
       FIG. 6  is a generalized diagram of a machine learning software stack  600 . A machine learning application  602  can be configured to train a neural network using a training dataset or to use a trained deep neural network to implement machine intelligence. The machine learning application  602  can include training and inference functionality for a neural network and/or specialized software that can be used to train a neural network before deployment. The machine learning application  602  can implement any type of machine intelligence including but not limited to image recognition, mapping and localization, autonomous navigation, speech synthesis, medical imaging, or language translation. 
     Hardware acceleration for the machine learning application  602  can be enabled via a machine learning framework  604 . The machine learning framework  604  can provide a library of machine learning primitives. Machine learning primitives are basic operations that are commonly performed by machine learning algorithms. Without the machine learning framework  604 , developers of machine learning algorithms would be required to create and optimize the main computational logic associated with the machine learning algorithm, then re-optimize the computational logic as new parallel processors are developed. Instead, the machine learning application can be configured to perform the necessary computations using the primitives provided by the machine learning framework  604 . Exemplary primitives include tensor convolutions, activation functions, and pooling, which are computational operations that are performed while training a convolutional neural network (CNN). The machine learning framework  604  can also provide primitives to implement basic linear algebra subprograms performed by many machine-learning algorithms, such as matrix and vector operations. 
     The machine learning framework  604  can process input data received from the machine learning application  602  and generate the appropriate input to a compute framework  606 . The compute framework  606  can abstract the underlying instructions provided to the GPGPU driver  608  to enable the machine learning framework  604  to take advantage of hardware acceleration via the GPGPU hardware  610  without requiring the machine learning framework  604  to have intimate knowledge of the architecture of the GPGPU hardware  610 . Additionally, the compute framework  606  can enable hardware acceleration for the machine learning framework  604  across a variety of types and generations of the GPGPU hardware  610 . 
     GPGPU Machine Learning Acceleration 
       FIG. 7  illustrates a general-purpose graphics processing unit  700 , according to an embodiment. In one embodiment, the general-purpose processing unit (GPGPU)  700  can be configured to be particularly efficient in processing the type of computational workloads associated with training deep neural networks. Additionally, the GPGPU  700  can be linked directly to other instances of the GPGPU to create a multi-GPU cluster to improve training speed for particularly deep neural networks. 
     The GPGPU  700  includes a host interface  702  to enable a connection with a host processor. In one embodiment the host interface  702  is a PCI Express interface. However, the host interface can also be a vendor specific communications interface or communications fabric. The GPGPU  700  receives commands from the host processor and uses a global scheduler  704  to distribute execution threads associated with those commands to a set of compute clusters  706 A- 706 H. The compute clusters  706 A- 706 H share a cache memory  708 . The cache memory  708  can serve as a higher-level cache for cache memories within the compute clusters  706 A- 706 H. 
     The GPGPU  700  includes memory  714 A-B coupled with the compute clusters  706 A-H via a set of memory controllers  712 A- 712 B. In various embodiments, the memory  714 A- 714 B can include various types of memory devices including dynamic random-access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory. In one embodiment, the memory  714 A- 714 N may also include 3D stacked memory, including but not limited to high bandwidth memory (HBM). 
     In one embodiment, each of the compute clusters  706 A- 706 H includes a set of graphics multiprocessors, such as the graphics multiprocessor  400  of  FIG. 4A . The graphics multiprocessors of the compute cluster multiple types of integer and floating-point logic units that can perform computational operations at a range of precisions including suited for machine learning computations. For example, and in one embodiment at least a subset of the floating-point units in each of the compute clusters  706 A-H can be configured to perform 16-bit or 32-bit floating point operations, while a different subset of the floating-point units can be configured to perform 64-bit floating point operations. 
     Multiple instances of the GPGPU  700  can be configured to operate as a compute cluster. The communication mechanism used by the compute cluster for synchronization and data exchange varies across embodiments. In one embodiment, the multiple instances of the GPGPU  700  communicate over the host interface  702 . In one embodiment the GPGPU  700  includes an I/O hub  709  that couples the GPGPU  700  with a GPU link  710  that enables a direct connection to other instances of the GPGPU. In one embodiment the GPU link  710  is coupled to a dedicated GPU-to-GPU bridge that enables communication and synchronization between multiple instances of the GPGPU  700 . In one embodiment the GPU link  710  couples with a high-speed interconnect to transmit and receive data to other GPGPUs or parallel processors. In one embodiment the multiple instances of the GPGPU  700  are located in separate data processing systems and communicate via a network device that is accessible via the host interface  702 . In one embodiment the GPU link  710  can be configured to enable a connection to a host processor in addition to or as an alternative to the host interface  702 . 
     While the illustrated configuration of the GPGPU  700  can be configured to train neural networks, one embodiment provides alternate configuration of the GPGPU  700  that can be configured for deployment within a high performance or low power inferencing platform. In an inferencing configuration, the GPGPU  700  includes fewer of the compute clusters  706 A- 706 H relative to the training configuration. Additionally, memory technology associated with the memory  714 A- 714 B may differ between inferencing and training configurations. In one embodiment, the inferencing configuration of the GPGPU  700  can support inferencing specific instructions. For example, an inferencing configuration can provide support for one or more 8-bit integer dot product instructions, which are commonly used during inferencing operations for deployed neural networks. 
       FIG. 8  illustrates a multi-GPU computing system  800 , according to an embodiment. The multi-GPU computing system  800  can include a processor  802  coupled to multiple GPGPUs  806 A- 806 D via a host interface switch  804 . The host interface switch  804 , in one embodiment, is a PCI express switch device that couples the processor  802  to a PCI express bus over which the processor  802  can communicate with the set of GPGPUs  806 A- 806 D. Each of the multiple GPGPUs  806 A- 806 D can be an instance of the GPGPU  700  of  FIG. 7 . The GPGPUs  806 A- 806 D can interconnect via a set of high-speed point to point GPU to GPU links  816 . The high-speed GPU to GPU links can connect to each of the GPGPUs  806 A- 806 D via a dedicated GPU link, such as the GPU link  710  as in  FIG. 7 . The P2P GPU links  816  enable direct communication between each of the GPGPUs  806 A- 806 D without requiring communication over the host interface bus to which the processor  802  is connected. With GPU-to-GPU traffic directed to the P2P GPU links, the host interface bus remains available for system memory access or to communicate with other instances of the multi-GPU computing system  800 , for example, via one or more network devices. While in the illustrated embodiment the GPGPUs  806 A-D connect to the processor  802  via the host interface switch  804 , in one embodiment the processor  802  includes direct support for the P2P GPU links  816  and can connect directly to the GPGPUs  806 A- 806 D. 
     Machine Learning Neural Network Implementations 
     The computing architecture provided by embodiments described herein can be configured to perform the types of parallel processing that is particularly suited for training and deploying neural networks for machine learning. A neural network can be generalized as a network of functions having a graph relationship. As is well-known in the art, there are a variety of types of neural network implementations used in machine learning. One exemplary type of neural network is the feedforward network, as previously described. 
     A second exemplary type of neural network is the Convolutional Neural Network (CNN). A CNN is a specialized feedforward neural network for processing data having a known, grid-like topology, such as image data. Accordingly, CNNs are commonly used for compute vision and image recognition applications, but they also may be used for other types of pattern recognition such as speech and language processing. The nodes in the CNN input layer are organized into a set of “filters” (feature detectors inspired by the receptive fields found in the retina), and the output of each set of filters is propagated to nodes in successive layers of the network. The computations for a CNN include applying the convolution mathematical operation to each filter to produce the output of that filter. Convolution is a specialized kind of mathematical operation performed by two functions to produce a third function that is a modified version of one of the two original functions. In convolutional network terminology, the first function to the convolution can be referred to as the input, while the second function can be referred to as the convolution kernel. The output may be referred to as the feature map. For example, the input to a convolution layer can be a multidimensional array of data that defines the various color components of an input image. The convolution kernel can be a multidimensional array of parameters, where the parameters are adapted by the training process for the neural network. 
     Recurrent neural networks (RNNs) are a family of feedforward neural networks that include feedback connections between layers. RNNs enable modeling of sequential data by sharing parameter data across different parts of the neural network. The architecture for an RNN includes cycles. The cycles represent the influence of a present value of a variable on its own value at a future time, as at least a portion of the output data from the RNN is used as feedback for processing subsequent input in a sequence. This feature makes RNNs particularly useful for language processing due to the variable nature in which language data can be composed. 
     The figures described below present exemplary feedforward, CNN, and RNN networks, as well as describe a general process for respectively training and deploying each of those types of networks. It will be understood that these descriptions are exemplary and non-limiting as to any specific embodiment described herein and the concepts illustrated can be applied generally to deep neural networks and machine learning techniques in general. 
     The exemplary neural networks described above can be used to perform deep learning. Deep learning is machine learning using deep neural networks. The deep neural networks used in deep learning are artificial neural networks composed of multiple hidden layers, as opposed to shallow neural networks that include only a single hidden layer. Deeper neural networks are generally more computationally intensive to train. However, the additional hidden layers of the network enable multistep pattern recognition that results in reduced output error relative to shallow machine learning techniques. 
     Deep neural networks used in deep learning typically include a front-end network to perform feature recognition coupled to a back-end network which represents a mathematical model that can perform operations (e.g., object classification, speech recognition, etc.) based on the feature representation provided to the model. Deep learning enables machine learning to be performed without requiring hand crafted feature engineering to be performed for the model. Instead, deep neural networks can learn features based on statistical structure or correlation within the input data. The learned features can be provided to a mathematical model that can map detected features to an output. The mathematical model used by the network is generally specialized for the specific task to be performed, and different models will be used to perform different task. 
     Once the neural network is structured, a learning model can be applied to the network to train the network to perform specific tasks. The learning model describes how to adjust the weights within the model to reduce the output error of the network. Backpropagation of errors is a common method used to train neural networks. An input vector is presented to the network for processing. The output of the network is compared to the desired output using a loss function and an error value is calculated for each of the neurons in the output layer. The error values are then propagated backwards until each neuron has an associated error value which roughly represents its contribution to the original output. The network can then learn from those errors using an algorithm, such as the stochastic gradient descent algorithm, to update the weights of the of the neural network. 
       FIG. 9A-9B  illustrate an exemplary convolutional neural network.  FIG. 9A  illustrates various layers within a CNN. As shown in  FIG. 9A , an exemplary CNN used to model image processing can receive input  902  describing the red, green, and blue (RGB) components of an input image. The input  902  can be processed by multiple convolutional layers (e.g., convolutional layer  904 , convolutional layer  906 ). The output from the multiple convolutional layers may optionally be processed by a set of fully connected layers  908 . Neurons in a fully connected layer have full connections to all activations in the previous layer, as previously described for a feedforward network. The output from the fully connected layers  908  can be used to generate an output result from the network. The activations within the fully connected layers  908  can be computed using matrix multiplication instead of convolution. Not all CNN implementations make use of fully connected layers  908 . For example, in some implementations the convolutional layer  906  can generate output for the CNN. 
     The convolutional layers are sparsely connected, which differs from traditional neural network configuration found in the fully connected layers  908 . Traditional neural network layers are fully connected, such that every output unit interacts with every input unit. However, the convolutional layers are sparsely connected because the output of the convolution of a field is input (instead of the respective state value of each of the nodes in the field) to the nodes of the subsequent layer, as illustrated. The kernels associated with the convolutional layers perform convolution operations, the output of which is sent to the next layer. The dimensionality reduction performed within the convolutional layers is one aspect that enables the CNN to scale to process large images. 
       FIG. 9B  illustrates exemplary computation stages within a convolutional layer of a CNN. Input to a convolutional layer  912  of a CNN can be processed in three stages of a convolutional layer  914 . The three stages can include a convolution stage  916 , a detector stage  918 , and a pooling stage  920 . The convolution layer  914  can then output data to a successive convolutional layer. The final convolutional layer of the network can generate output feature map data or provide input to a fully connected layer, for example, to generate a classification value for the input to the CNN. 
     In the convolution stage  916  performs several convolutions in parallel to produce a set of linear activations. The convolution stage  916  can include an affine transformation, which is any transformation that can be specified as a linear transformation plus a translation. Affine transformations include rotations, translations, scaling, and combinations of these transformations. The convolution stage computes the output of functions (e.g., neurons) that are connected to specific regions in the input, which can be determined as the local region associated with the neuron. The neurons compute a dot product between the weights of the neurons and the region in the local input to which the neurons are connected. The output from the convolution stage  916  defines a set of linear activations that are processed by successive stages of the convolutional layer  914 . 
     The linear activations can be processed by a detector stage  918 . In the detector stage  918 , each linear activation is processed by a non-linear activation function. The non-linear activation function increases the nonlinear properties of the overall network without affecting the receptive fields of the convolution layer. Several types of non-linear activation functions may be used. One particular type is the rectified linear unit (ReLU), which uses an activation function defined as ƒ(x)=max(0, x), such that the activation is thresholded at zero. 
     The pooling stage  920  uses a pooling function that replaces the output of the convolutional layer  906  with a summary statistic of the nearby outputs. The pooling function can be used to introduce translation invariance into the neural network, such that small translations to the input do not change the pooled outputs. Invariance to local translation can be useful in scenarios where the presence of a feature in the input data is more important than the precise location of the feature. Various types of pooling functions can be used during the pooling stage  920 , including max pooling, average pooling, and l2-norm pooling. Additionally, some CNN implementations do not include a pooling stage. Instead, such implementations substitute and additional convolution stage having an increased stride relative to previous convolution stages. 
     The output from the convolutional layer  914  can then be processed by the next layer  922 . The next layer  922  can be an additional convolutional layer or one of the fully connected layers  908 . For example, the first convolutional layer  904  of  FIG. 9A  can output to the second convolutional layer  906 , while the second convolutional layer can output to a first layer of the fully connected layers  908 . 
       FIG. 10  illustrates an exemplary recurrent neural network  1000 . In a recurrent neural network (RNN), the previous state of the network influences the output of the current state of the network. RNNs can be built in a variety of ways using a variety of functions. The use of RNNs generally revolves around using mathematical models to predict the future based on a prior sequence of inputs. For example, an RNN may be used to perform statistical language modeling to predict an upcoming word given a previous sequence of words. The illustrated RNN  1000  can be described has having an input layer  1002  that receives an input vector, hidden layers  1004  to implement a recurrent function, a feedback mechanism  1005  to enable a ‘memory’ of previous states, and an output layer  1006  to output a result. The RNN  1000  operates based on time-steps. The state of the RNN at a given time step is influenced based on the previous time step via the feedback mechanism  1005 . For a given time step, the state of the hidden layers  1004  is defined by the previous state and the input at the current time step. An initial input (x 1 ) at a first time step can be processed by the hidden layer  1004 . A second input (x 2 ) can be processed by the hidden layer  1004  using state information that is determined during the processing of the initial input (x 1 ). A given state can be computed as s t =ƒ(Ux t +Ws t−1 ), where U and W are parameter matrices. The function ƒ is generally a nonlinearity, such as the hyperbolic tangent function (Tanh) or a variant of the rectifier function ƒ(x)=max(0, x). However, the specific mathematical function used in the hidden layers  1004  can vary depending on the specific implementation details of the RNN  1000 . 
     In addition to the basic CNN and RNN networks described, variations on those networks may be enabled. One example RNN variant is the long short term memory (LSTM) RNN. LSTM RNNs are capable of learning long-term dependencies that may be necessary for processing longer sequences of language. A variant on the CNN is a convolutional deep belief network, which has a structure similar to a CNN and is trained in a manner similar to a deep belief network. A deep belief network (DBN) is a generative neural network that is composed of multiple layers of stochastic (random) variables. DBNs can be trained layer-by-layer using greedy unsupervised learning. The learned weights of the DBN can then be used to provide pre-train neural networks by determining an optimal initial set of weights for the neural network. 
       FIG. 11  illustrates training and deployment of a deep neural network. Once a given network has been structured for a task the neural network is trained using a training dataset  1102 . Various training frameworks  1104  have been developed to enable hardware acceleration of the training process. For example, the machine learning framework  604  of  FIG. 6  may be configured as a training framework  604 . The training framework  604  can hook into an untrained neural network  1106  and enable the untrained neural net to be trained using the parallel processing resources described herein to generate a trained neural net  1108 . 
     To start the training process the initial weights may be chosen randomly or by pre-training using a deep belief network. The training cycle then be performed in either a supervised or unsupervised manner. 
     Supervised learning is a learning method in which training is performed as a mediated operation, such as when the training dataset  1102  includes input paired with the desired output for the input, or where the training dataset includes input having known output and the output of the neural network is manually graded. The network processes the inputs and compares the resulting outputs against a set of expected or desired outputs. Errors are then propagated back through the system. The training framework  1104  can adjust to adjust the weights that control the untrained neural network  1106 . The training framework  1104  can provide tools to monitor how well the untrained neural network  1106  is converging towards a model suitable to generating correct answers based on known input data. The training process occurs repeatedly as the weights of the network are adjusted to refine the output generated by the neural network. The training process can continue until the neural network reaches a statistically desired accuracy associated with a trained neural net  1108 . The trained neural network  1108  can then be deployed to implement any number of machine learning operations to generate an inference result  1114  based on input of new data  1112 . 
     Unsupervised learning is a learning method in which the network attempts to train itself using unlabeled data. Thus, for unsupervised learning the training dataset  1102  will include input data without any associated output data. The untrained neural network  1106  can learn groupings within the unlabeled input and can determine how individual inputs are related to the overall dataset. Unsupervised training can be used to generate a self-organizing map, which is a type of trained neural network  1108  capable of performing operations useful in reducing the dimensionality of data. Unsupervised training can also be used to perform anomaly detection, which allows the identification of data points in an input dataset that deviate from the normal patterns of the data. 
     Variations on supervised and unsupervised training may also be employed. Semi-supervised learning is a technique in which in the training dataset  1102  includes a mix of labeled and unlabeled data of the same distribution. Incremental learning is a variant of supervised learning in which input data is continuously used to further train the model. Incremental learning enables the trained neural network  1108  to adapt to the new data  1112  without forgetting the knowledge instilled within the network during initial training. 
     Whether supervised or unsupervised, the training process for particularly deep neural networks may be too computationally intensive for a single compute node. Instead of using a single compute node, a distributed network of computational nodes can be used to accelerate the training process. 
       FIG. 12  is a block diagram illustrating distributed learning. Distributed learning is a training model that uses multiple distributed computing nodes to perform supervised or unsupervised training of a neural network. The distributed computational nodes can each include one or more host processors and one or more of the general-purpose processing nodes, such as the highly-parallel general-purpose graphics processing unit  700  as in  FIG. 700 . As illustrated, distributed learning can be performed model parallelism  1202 , data parallelism  1204 , or a combination of model and data parallelism  1204 . 
     In model parallelism  1202 , different computational nodes in a distributed system can perform training computations for different parts of a single network. For example, each layer of a neural network can be trained by a different processing node of the distributed system. The benefits of model parallelism include the ability to scale to particularly large models. Splitting the computations associated with different layers of the neural network enables the training of very large neural networks in which the weights of all layers would not fit into the memory of a single computational node. In some instances, model parallelism can be particularly useful in performing unsupervised training of large neural networks. 
     In data parallelism  1204 , the different nodes of the distributed network have a complete instance of the model and each node receives a different portion of the data. The results from the different nodes are then combined. While different approaches to data parallelism are possible, data parallel training approaches all require a technique of combining results and synchronizing the model parameters between each node. Exemplary approaches to combining data include parameter averaging and update based data parallelism. Parameter averaging trains each node on a subset of the training data and sets the global parameters (e.g., weights, biases) to the average of the parameters from each node. Parameter averaging uses a central parameter server that maintains the parameter data. Update based data parallelism is similar to parameter averaging except that instead of transferring parameters from the nodes to the parameter server, the updates to the model are transferred. Additionally, update based data parallelism can be performed in a decentralized manner, where the updates are compressed and transferred between nodes. 
     Combined model and data parallelism  1206  can be implemented, for example, in a distributed system in which each computational node includes multiple GPUs. Each node can have a complete instance of the model with separate GPUs within each node are used to train different portions of the model. 
     Distributed training has increased overhead relative to training on a single machine. However, the parallel processors and GPGPUs described herein can each implement various techniques to reduce the overhead of distributed training, including techniques to enable high bandwidth GPU-to-GPU data transfer and accelerated remote data synchronization. 
     Exemplary Machine Learning Applications 
     Machine learning can be applied to solve a variety of technological problems, including but not limited to computer vision, autonomous driving and navigation, speech recognition, and language processing. Computer vision has traditionally been one of the most active research areas for machine learning applications. Applications of computer vision range from reproducing human visual abilities, such as recognizing faces, to creating new categories of visual abilities. For example, computer vision applications can be configured to recognize sound waves from the vibrations induced in objects visible in a video. Parallel processor accelerated machine learning enables computer vision applications to be trained using significantly larger training dataset than previously feasible and enables inferencing systems to be deployed using low power parallel processors. 
     Parallel processor accelerated machine learning has autonomous driving applications including lane and road sign recognition, obstacle avoidance, navigation, and driving control. Accelerated machine learning techniques can be used to train driving models based on datasets that define the appropriate responses to specific training input. The parallel processors described herein can enable rapid training of the increasingly complex neural networks used for autonomous driving solutions and enables the deployment of low power inferencing processors in a mobile platform suitable for integration into autonomous vehicles. 
     Parallel processor accelerated deep neural networks have enabled machine learning approaches to automatic speech recognition (ASR). ASR includes the creation of a function that computes the most probable linguistic sequence given an input acoustic sequence. Accelerated machine learning using deep neural networks have enabled the replacement of the hidden Markov models (HMMs) and Gaussian mixture models (GMMs) previously used for ASR. 
     Parallel processor accelerated machine learning can also be used to accelerate natural language processing. Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to erroneous or unfamiliar input. Exemplary natural language processor applications include automatic machine translation between human languages. 
     The parallel processing platforms used for machine learning can be divided into training platforms and deployment platforms. Training platforms are generally highly parallel and include optimizations to accelerate multi-GPU single node training and multi-node, multi-GPU training. Exemplary parallel processors suited for training include the general-purpose graphics processing unit  700  of  FIG. 700  and the multi-GPU computing system  800  of  FIG. 800 . On the contrary, deployed machine learning platforms generally include lower power parallel processors suitable for use in products such as cameras, autonomous robots, and autonomous vehicles. 
       FIG. 13  illustrates an exemplary inferencing system on a chip (SOC)  1300  suitable for performing inferencing using a trained model. The SOC  1300  can integrate processing components including a media processor  1302 , a vision processor  1304 , a GPGPU  1306  and a multi-core processor  1308 . The SOC  1300  can additionally include on-chip memory  1305  that can enable a shared on-chip data pool that is accessible by each of the processing components. The processing components can be optimized for low power operation to enable deployment to a variety of machine learning platforms, including autonomous vehicles and autonomous robots. For example, one implementation of the SOC  1300  can be used as a portion of the main control system for an autonomous vehicle. Where the SOC  1300  is configured for use in autonomous vehicles the SOC is designed and configured for compliance with the relevant functional safety standards of the deployment jurisdiction. 
     During operation, the media processor  1302  and vision processor  1304  can work in concert to accelerate computer vision operations. The media processor  1302  can enable low latency decode of multiple high-resolution (e.g.,  4 K,  8 K) video streams. The decoded video streams can be written to a buffer in the on-chip-memory  1305 . The vision processor  1304  can then parse the decoded video and perform preliminary processing operations on the frames of the decoded video in preparation of processing the frames using a trained image recognition model. For example, the vision processor  1304  can accelerate convolution operations for a CNN that is used to perform image recognition on the high-resolution video data, while back end model computations are performed by the GPGPU  1306 . 
     The multi-core processor  1308  can include control logic to assist with sequencing and synchronization of data transfers and shared memory operations performed by the media processor  1302  and the vision processor  1304 . The multi-core processor  1308  can also function as an application processor to execute software applications that can make use of the inferencing compute capability of the GPGPU  1306 . For example, at least a portion of the navigation and driving logic can be implemented in software executing on the multi-core processor  1308 . Such software can directly issue computational workloads to the GPGPU  1306  or the computational workloads can be issued to the multi-core processor  1308 , which can offload at least a portion of those operations to the GPGPU  1306 . 
     The GPGPU  1306  can include compute clusters such as a low power configuration of the compute clusters  706 A- 706 H within general-purpose graphics processing unit  700 . The compute clusters within the GPGPU  1306  can support instruction that are specifically optimized to perform inferencing computations on a trained neural network. For example, the GPGPU  1306  can support instructions to perform low precision computations such as 8-bit and 4-bit integer vector operations. 
     Memory Prefetching in Multiple Graphics Processor Environment 
     In some embodiments, an apparatus, system, or process provides for improvements in memory prefetching for a multiple GPU environment. In some embodiments, rules are applied for use by a prefetcher when a multi-GPU workload is executing across a cluster of GPUs having unified virtual memory and non-unified physical memory. 
       FIG. 14  is an illustration of a multiple GPU environment according to some embodiments. As illustrated in  FIG. 14 , a computing system  1400  may include a host processor  1403 , such as a CPU, which may support an application driver  1401  that directs a workload to each of multiple GPUs for processing, illustrated in this example as workloads  1408 A- 1408 D being directed to the graphics interfaces  1402 A- 1402 D of GPUs  1404 A- 1404 D. Further, each GPU may be coupled with a memory of the illustrated physical memories  1406 A- 1406 D. 
     However, while the memory includes non-unified physical memories  1406 A- 1406 D, these memories may be part of a unified virtual memory. As a result, prefetching of data that does not recognize the physical structure of the memory can result in efficient use of system resources as prefetches for a GPU are made memory locations that are remote from the GPU. 
     In some embodiments, a prefetcher for a GPU in a multiple GPU environment provide protected prefetch for cross-GPU coherency, wherein the prefetcher is limited to prefetching: 
     (1) Only of pages that owned by the local GPU or the host processor; 
     (2) That does not cross memory page boundaries during prefetching; and 
     (3) That does not cross boundaries of allocated surfaces. 
       FIG. 15  is an illustration of protected prefetching in a multiple GPU environment according to some embodiments. As illustrated, GPU  1404 A and GPU  1404 B each include a prefetcher  1505 A and  1505 B and a cache  1503 A and  1503 B, and are respectively coupled with memory  1406 A and memory  1406 B. While memory  1406 A and memory  1406 B are separate physical memories, these are a part of unified virtual memory  1510 . As a result, a prefetch operation could potentially result in prefetches from non-local memory if there were no restrictions in place. 
     In some embodiments, the prefetchers  1505 A and  1505 B are prohibited from prefetching out of pages that are not owned by the local GPU or by the host processor, and are further prohibited from crossing memory page boundaries during a prefetch. In this manner, a prefetcher is prevented both from directing a prefetch to a page of non-local GPU, and from crossing into the page that is owned by another GPU. For example,  FIG. 15  illustrates pages  1511  and  1512  within physical memory  1406 A and owned by GPU  1404 A and pages  1513  and  1514  within physical memory  1406 B and owned by GPU  1404 B. If, for example, prefetcher  1505 A is prefetching data, then data within pages  1511  and  1512  may be prefetched, but not within  1513  and  1514 . Further, a prefetch of page  1511  would be halted at  1515 A and a prefetch of page  1512  would be halted by  1515 B. In this manner, the prefetch of page  1512  would be prevented from continuing into page  1513 , which would result in prefetching from a non-local physical memory. 
       FIG. 16  is an illustration of prefetching from memory surfaces in a multiple GPU environment according to some embodiments. In addition to the restrictions illustrated in  FIG. 15 , a prefetcher may further be prevented from a prefetch that crosses boundaries of a memory surface. As illustrated, a memory may include creation of multiple memory surfaces  1600 , such as memory surface  1  and memory surface  2 . In some embodiments, a prefetcher is prohibited from prefetching that crosses a boundary of a memory surface. For example, a prefetch of memory surface  1 , shown as prefetch  1610 , is halted at a boundary of the memory surface, shown as prefetch halt  1615 , which prevents the prefetch of a memory surface from crossing into another memory surface, such as memory surface  2 . 
       FIG. 17  is a flowchart to illustrate a process for prefetching in a multiple GPU environment according to some embodiments. In some embodiments, a process includes initiating an application  1700 , wherein the application may include processing in a computing system environment with multiple GPUs. Workloads are distributed to the multiple GPUs  1705 , and threads are processed by the GPUs receiving workloads  1710 . 
     In some embodiments, upon initiation of a prefetch by a prefetcher of a GPU  1715 , there is a determination whether the prefetch is directed to a page that is owned by the local GPU of the prefetcher or the CPU (or other host processor) of the computing system  1720 . If not, the prefetch is denied  1720 , and the processing of the threads by the GPUs may continue  1710 . If so, then the processing of the prefetch may proceed  1730 . 
     In some embodiments, the process may further include a determination whether the prefetch has reached a boundary of the page or a memory surface  1735 . If so, the prefetch is halted  1740 , and the processing of the threads by the GPUs may continue  1710 . This may continue until the prefetch is complete  1745 , with the processing of the threads by the GPUs then continuing  1710 . 
       FIG. 17  illustrates process occurring in a certain order for ease of illustration. However, these processes may occur in a different order or in an overlapping or simultaneous manner. Further, while a single prefetch is illustrated for simplicity, multiple prefetches may be occurring from each of the multiple GPUs in operation. 
       FIG. 18A  is an illustration of a gather/scatter prefetch instruction according to some embodiments. Prefetching commonly will prefetch a cache line or multiple contiguous cache lines. However, this process requires multiple prefetch instructions if there are multiple contiguous addresses that are to be subjects of prefetches. 
     In some embodiments, instead of pre-fetching contiguous cache lines, an array of different non-contiguous addresses can be requested in a single instruction in order to reduce the number of instructions to be generated and transmitted in a GPU. The single instruction, which may be referred to as a gather/scatter prefetch instruction, includes multiple prefetch addresses. An exemplary gather/scatter prefetch instruction  1800  is illustrated in  FIG. 18A , the instruction including 32 different addresses illustrated as A 0  through A 31 . 
     In some embodiments, a prefetcher of a GPU in a computing system, such as prefetcher  15105 A of GPU  1404 A illustrated in  FIG. 15 , is to issue the gather/scatter prefetch instruction  1800  to provide up to 32 addresses to be prefetched. In response to the instruction  1800 , hardware of the computing system is to parse the instruction and issue multiple batched prefetch messages to memory for each of the prefetch addresses contained in the gather/scatter prefetch instruction  1800 . 
       FIG. 18B  is an illustration of a selective prefetch instruction according to some embodiments. As indicated in  FIG. 18A , a gather/scatter prefetch instruction may identify multiple addresses to be prefetched, wherein the addresses may be noncontiguous. In some embodiments, each address may comprise a selective prefetch instruction  1850  that includes both an address and a cache level for the prefetched data. In this manner, a gather/scatter prefetch instruction  1800  illustrated in  FIG. 18A  may be utilized to provide selective prefetch to multiple different levels in the cache hierarchy, such as L1, L2, and L3, within a single instruction, thus further improving efficiency of prefetching operation in a computer system. 
       FIG. 19  is an illustration of prefetch operation with status notification according to some embodiments. In a computing system having multiple graphics processors, each processor may be providing prefetching instructions to memory. However, the multiple threads being processed in the graphics processors may potentially overwhelm a cache or otherwise create issues with numerous prefetch instructions. 
       FIG. 19  illustrates an exemplary graphics processor  1900  and memory  1930 . The graphics processor may include multiple cores, such as the illustrated shader core  1902  and other cores  1904 . The graphics processor may further include a prefetcher  1906  to provide prefetch requests to memory and one or more caches such as cache  1908 . In some embodiments, upon completion of a prefetch from memory to the cache  1908  for a thread  1910  issuing the prefetch instruction, the prefetcher is to send back an optional notification, such as a 1-bit flag, to the thread  1910  indicating that the prefetch is complete, and thus data is loaded in the cache  1908 . In some embodiments, the thread  1910  can utilize this notification to synchronize its execution with other threads, such as thread  1911 . In some embodiments, the thread may also use the notification to throttle prefetches in order to prevent prefetching too much ahead and overwhelm the cache  1908 . 
       FIG. 20  is an illustration of an apparatus or system to provide for improved prefetching performance, according to some embodiments. As illustrated in  FIG. 20 , a computing system  2000 , such as, for example, system  100  illustrated in  FIG. 1 , includes one or more processors  2005  and multiple GPUs for the processing of data. The computing system  2000  further includes memory  2010  for the storage of data and one or more elements for the transfer of data, such as interface bus  2015  and transceiver  2020 . In some embodiments, the transceiver  2020  is a wireless transceiver with one or more antennas  2025  for transmission and reception of data, wherein the antennas  2025  may include a dipole antenna or other antenna structures. 
     In some embodiments, the GPUs  2030  each include circuitry to support improved prefetching operation in the multiple GPU environment, including one or more of protected prefetch optimizations for cross-GPU coherency, as illustrated in  FIGS. 15-17 , gather/scatter prefetch instructions, as illustrated in  FIGS. 18A and 18B ; and prefetch operation with status notification, as illustrated in  FIG. 19 . 
     System Overview 
       FIG. 21  is a block diagram of a processing system  2100 , according to an embodiment. System  2100  may be used in a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors  2102  or processor cores  2107 . In one embodiment, the system  2100  is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices such as within Internet-of-things (IoT) devices with wired or wireless connectivity to a local or wide area network. 
     In one embodiment, system  2100  can include, couple with, or be integrated within: a server-based gaming platform; a game console, including a game and media console; a mobile gaming console, a handheld game console, or an online game console. In some embodiments the system  2100  is part of a mobile phone, smart phone, tablet computing device or mobile Internet-connected device such as a laptop with low internal storage capacity. Processing system  2100  can also include, couple with, or be integrated within: a wearable device, such as a smart watch wearable device; smart eyewear or clothing enhanced with augmented reality (AR) or virtual reality (VR) features to provide visual, audio or tactile outputs to supplement real world visual, audio or tactile experiences or otherwise provide text, audio, graphics, video, holographic images or video, or tactile feedback; other augmented reality (AR) device; or other virtual reality (VR) device. In some embodiments, the processing system  2100  includes or is part of a television or set top box device. 
     In some embodiments, system  2100  can include, couple with, or be integrated within a self-driving vehicle such as a bus, tractor trailer, car, motor or electric power cycle, plane or glider (or any combination thereof). The self-driving vehicle may use system  2100  to process the environment sensed around the vehicle. 
     In some embodiments, the one or more processors  2102  each include one or more processor cores  2107  to process instructions which, when executed, perform operations for system or user software. In some embodiments, at least one of the one or more processor cores  2107  is configured to process a specific instruction set  2109 . In some embodiments, instruction set  2109  may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). One or more processor cores  2107  may process a different instruction set  2109 , which may include instructions to facilitate the emulation of other instruction sets. Processor core  2107  may also include other processing devices, such as a Digital Signal Processor (DSP). 
     In some embodiments, the processor  2102  includes cache memory  2104 . Depending on the architecture, the processor  2102  can have a single internal cache or multiple levels of internal cache. In some embodiments, the cache memory is shared among various components of the processor  2102 . In some embodiments, the processor  2102  also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores  2107  using known cache coherency techniques. A register file  2106  can be additionally included in processor  2102  and may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). Some registers may be general-purpose registers, while other registers may be specific to the design of the processor  2102 . 
     In some embodiments, one or more processor(s)  2102  are coupled with one or more interface bus(es)  2110  to transmit communication signals such as address, data, or control signals between processor  2102  and other components in the system  2100 . The interface bus  2110 , in one embodiment, can be a processor bus, such as a version of the Direct Media Interface (DMI) bus. However, processor busses are not limited to the DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In one embodiment the processor(s)  2102  include an integrated memory controller  2116  and a platform controller hub  2130 . The memory controller  2116  facilitates communication between a memory device and other components of the system  2100 , while the platform controller hub (PCH)  2130  provides connections to I/O devices via a local I/O bus. 
     The memory device  2120  can be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In one embodiment the memory device  2120  can operate as system memory for the system  2100 , to store data  2122  and instructions  2121  for use when the one or more processors  2102  executes an application or process. Memory controller  2116  also couples with an optional external graphics processor  2112 , which may communicate with the one or more graphics processors  2108  in processors  2102  to perform graphics and media operations. In some embodiments, graphics, media, and or compute operations may be assisted by an accelerator  2112 , which is a coprocessor that can be configured to perform a specialized set of graphics, media, or compute operations. For example, in one embodiment the accelerator  2112  is a matrix multiplication accelerator used to optimize machine learning or compute operations. In one embodiment the accelerator  2112  is a ray-tracing accelerator that can be used to perform ray-tracing operations in concert with the graphics processor  2108 . In some embodiments a display device  2111  can connect to the processor(s)  2102 . The display device  2111  can be one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In one embodiment the display device  2111  can be a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications. 
     In some embodiments the platform controller hub  2130  enables peripherals to connect to memory device  2120  and processor  2102  via a high-speed I/O bus. The I/O peripherals include, but are not limited to, an audio controller  2146 , a network controller  2134 , a firmware interface  2128 , a wireless transceiver  2126 , touch sensors  2125 , a data storage device  2124  (e.g., non-volatile memory, volatile memory, hard disk drive, flash memory, NAND, 3D NAND, 3D XPoint, etc.). The data storage device  2124  can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). The touch sensors  2125  can include touch screen sensors, pressure sensors, or fingerprint sensors. The wireless transceiver  2126  can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, 5G, or Long Term Evolution (LTE) transceiver. The firmware interface  2128  enables communication with system firmware, and can be, for example, a unified extensible firmware interface (UEFI). The network controller  2134  can enable a network connection to a wired network. In some embodiments, a high-performance network controller (not shown) couples with the interface bus  2110 . The audio controller  2146 , in one embodiment, is a multi-channel high definition audio controller. In one embodiment the system  2100  includes an optional legacy I/O controller  2140  for coupling legacy (e.g., Personal System  2  (PS/2)) devices to the system. The platform controller hub  2130  can also connect to one or more Universal Serial Bus (USB) controllers  2142  connect input devices, such as keyboard and mouse  2143  combinations, a camera  2144 , or other USB input devices. 
     It will be appreciated that the system  2100  shown is exemplary and not limiting, as other types of data processing systems that are differently configured may also be used. For example, an instance of the memory controller  2116  and platform controller hub  2130  may be integrated into a discreet external graphics processor, such as the external graphics processor  2112 . In one embodiment the platform controller hub  2130  and/or memory controller  2116  may be external to the one or more processor(s)  2102 . For example, the system  2100  can include an external memory controller  2116  and platform controller hub  2130 , which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with the processor(s)  2102 . 
     For example, circuit boards (“sleds”) can be used on which components such as CPUs, memory, and other components are placed are designed for increased thermal performance. In some examples, processing components such as the processors are located on a top side of a sled while near memory, such as DIMMs, are located on a bottom side of the sled. As a result of the enhanced airflow provided by this design, the components may operate at higher frequencies and power levels than in typical systems, thereby increasing performance. Furthermore, the sleds are configured to blindly mate with power and data communication cables in a rack, thereby enhancing their ability to be quickly removed, upgraded, reinstalled, and/or replaced. Similarly, individual components located on the sleds, such as processors, accelerators, memory, and data storage drives, are configured to be easily upgraded due to their increased spacing from each other. In the illustrative embodiment, the components additionally include hardware attestation features to prove their authenticity. 
     A data center can utilize a single network architecture (“fabric”) that supports multiple other network architectures including Ethernet and Omni-Path. The sleds can be coupled to switches via optical fibers, which provide higher bandwidth and lower latency than typical twisted pair cabling (e.g., Category 5, Category 5e, Category 6, etc.). Due to the high bandwidth, low latency interconnections and network architecture, the data center may, in use, pool resources, such as memory, accelerators (e.g., GPUs, graphics accelerators, FPGAs, ASICs, neural network and/or artificial intelligence accelerators, etc.), and data storage drives that are physically disaggregated, and provide them to compute resources (e.g., processors) on an as needed basis, enabling the compute resources to access the pooled resources as if they were local. 
     A power supply or source can provide voltage and/or current to system  2100  or any component or system described herein. In one example, the power supply includes an AC to DC (alternating current to direct current) adapter to plug into a wall outlet. Such AC power can be renewable energy (e.g., solar power) power source. In one example, power source includes a DC power source, such as an external AC to DC converter. In one example, power source or power supply includes wireless charging hardware to charge via proximity to a charging field. In one example, power source can include an internal battery, alternating current supply, motion-based power supply, solar power supply, or fuel cell source. 
       FIG. 22  is a block diagram of an embodiment of a processor  2200  having one or more processor cores  2202 A- 2202 N, an integrated memory controller  2214 , and an integrated graphics processor  2208 . Those elements of  FIG. 22  having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. Processor  2200  can include additional cores up to and including additional core  2202 N represented by the dashed lined boxes. Each of processor cores  2202 A- 2202 N includes one or more internal cache units  2204 A- 2204 N. In some embodiments each processor core also has access to one or more shared cached units  2206 . 
     The internal cache units  2204 A- 2204 N and shared cache units  2206  represent a cache memory hierarchy within the processor  2200 . The cache memory hierarchy may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where the highest level of cache before external memory is classified as the LLC. In some embodiments, cache coherency logic maintains coherency between the various cache units  2206  and  2204 A- 2204 N. 
     In some embodiments, processor  2200  may also include a set of one or more bus controller units  2216  and a system agent core  2210 . The one or more bus controller units  2216  manage a set of peripheral buses, such as one or more PCI or PCI express busses. System agent core  2210  provides management functionality for the various processor components. In some embodiments, system agent core  2210  includes one or more integrated memory controllers  2214  to manage access to various external memory devices (not shown). 
     In some embodiments, one or more of the processor cores  2202 A- 2202 N include support for simultaneous multi-threading. In such embodiment, the system agent core  2210  includes components for coordinating and operating cores  2202 A- 2202 N during multi-threaded processing. System agent core  2210  may additionally include a power control unit (PCU), which includes logic and components to regulate the power state of processor cores  2202 A- 2202 N and graphics processor  2208 . 
     In some embodiments, processor  2200  additionally includes graphics processor  2208  to execute graphics processing operations. In some embodiments, the graphics processor  2208  couples with the set of shared cache units  2206 , and the system agent core  2210 , including the one or more integrated memory controllers  2214 . In some embodiments, the system agent core  2210  also includes a display controller  2211  to drive graphics processor output to one or more coupled displays. In some embodiments, display controller  2211  may also be a separate module coupled with the graphics processor via at least one interconnect, or may be integrated within the graphics processor  2208 . 
     In some embodiments, a ring based interconnect unit  2212  is used to couple the internal components of the processor  2200 . However, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques, including techniques well known in the art. In some embodiments, graphics processor  2208  couples with the ring interconnect  2212  via an I/O link  2213 . 
     The exemplary I/O link  2213  represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module  2218 , such as an eDRAM module. In some embodiments, each of the processor cores  2202 A- 2202 N and graphics processor  2208  can use embedded memory modules  2218  as a shared Last Level Cache. 
     In some embodiments, processor cores  2202 A- 2202 N are homogenous cores executing the same instruction set architecture. In another embodiment, processor cores  2202 A- 2202 N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores  2202 A- 2202 N execute a first instruction set, while at least one of the other cores executes a subset of the first instruction set or a different instruction set. In one embodiment, processor cores  2202 A- 2202 N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In one embodiment, processor cores  2202 A- 2202 N are heterogeneous in terms of computational capability. Additionally, processor  2200  can be implemented on one or more chips or as an SoC integrated circuit having the illustrated components, in addition to other components. 
       FIG. 23  is a block diagram of a graphics processor  2300 , which may be a discrete graphics processing unit, or may be a graphics processor integrated with a plurality of processing cores, or other semiconductor devices such as, but not limited to, memory devices or network interfaces. In some embodiments, the graphics processor communicates via a memory mapped I/O interface to registers on the graphics processor and with commands placed into the processor memory. In some embodiments, graphics processor  2300  includes a memory interface  2314  to access memory. Memory interface  2314  can be an interface to local memory, one or more internal caches, one or more shared external caches, and/or to system memory. 
     In some embodiments, graphics processor  2300  also includes a display controller  2302  to drive display output data to a display device  2320 . Display controller  2302  includes hardware for one or more overlay planes for the display and composition of multiple layers of video or user interface elements. The display device  2320  can be an internal or external display device. In one embodiment the display device  2320  is a head mounted display device, such as a virtual reality (VR) display device or an augmented reality (AR) display device. In some embodiments, graphics processor  2300  includes a video codec engine  2306  to encode, decode, or transcode media to, from, or between one or more media encoding formats, including, but not limited to Moving Picture Experts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, H.265/HEVC, Alliance for Open Media (AOMedia) VP8, VP9, as well as the Society of Motion Picture &amp; Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats. 
     In some embodiments, graphics processor  2300  includes a block image transfer (BLIT) engine  2304  to perform two-dimensional (2D) rasterizer operations including, for example, bit-boundary block transfers. However, in one embodiment, 2D graphics operations are performed using one or more components of graphics processing engine (GPE)  2310 . In some embodiments, GPE  2310  is a compute engine for performing graphics operations, including three-dimensional (3D) graphics operations and media operations. 
     In some embodiments, GPE  2310  includes a 3D pipeline  2312  for performing 3D operations, such as rendering three-dimensional images and scenes using processing functions that act upon 3D primitive shapes (e.g., rectangle, triangle, etc.). The 3D pipeline  2312  includes programmable and fixed function elements that perform various tasks within the element and/or spawn execution threads to a 3D/Media sub-system  2315 . While 3D pipeline  2312  can be used to perform media operations, an embodiment of GPE  2310  also includes a media pipeline  2316  that is specifically used to perform media operations, such as video post-processing and image enhancement. 
     In some embodiments, media pipeline  2316  includes fixed function or programmable logic units to perform one or more specialized media operations, such as video decode acceleration, video de-interlacing, and video encode acceleration in place of, or on behalf of video codec engine  2306 . In some embodiments, media pipeline  2316  additionally includes a thread spawning unit to spawn threads for execution on 3D/Media sub-system  2315 . The spawned threads perform computations for the media operations on one or more graphics execution units included in 3D/Media sub-system  2315 . 
     In some embodiments, 3D/Media subsystem  2315  includes logic for executing threads spawned by 3D pipeline  2312  and media pipeline  2316 . In one embodiment, the pipelines send thread execution requests to 3D/Media subsystem  2315 , which includes thread dispatch logic for arbitrating and dispatching the various requests to available thread execution resources. The execution resources include an array of graphics execution units to process the 3D and media threads. In some embodiments, 3D/Media subsystem  2315  includes one or more internal caches for thread instructions and data. In some embodiments, the subsystem also includes shared memory, including registers and addressable memory, to share data between threads and to store output data. 
     Graphics Processing Engine 
       FIG. 24  is a block diagram of a graphics processing engine  2410  of a graphics processor in accordance with some embodiments. In one embodiment, the graphics processing engine (GPE)  2410  is a version of the GPE  2310  shown in  FIG. 23 . Elements of  FIG. 24  having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. For example, the 3D pipeline  2312  and media pipeline  2316  of  FIG. 23  are illustrated. The media pipeline  2316  is optional in some embodiments of the GPE  2410  and may not be explicitly included within the GPE  2410 . For example and in at least one embodiment, a separate media and/or image processor is coupled to the GPE  2410 . 
     In some embodiments, GPE  2410  couples with or includes a command streamer  2403 , which provides a command stream to the 3D pipeline  2312  and/or media pipelines  2316 . In some embodiments, command streamer  2403  is coupled with memory, which can be system memory, or one or more of internal cache memory and shared cache memory. In some embodiments, command streamer  2403  receives commands from the memory and sends the commands to 3D pipeline  2312  and/or media pipeline  2316 . The commands are directives fetched from a ring buffer, which stores commands for the 3D pipeline  2312  and media pipeline  2316 . In one embodiment, the ring buffer can additionally include batch command buffers storing batches of multiple commands. The commands for the 3D pipeline  2312  can also include references to data stored in memory, such as but not limited to vertex and geometry data for the 3D pipeline  2312  and/or image data and memory objects for the media pipeline  2316 . The 3D pipeline  2312  and media pipeline  2316  process the commands and data by performing operations via logic within the respective pipelines or by dispatching one or more execution threads to a graphics core array  2414 . In one embodiment the graphics core array  2414  include one or more blocks of graphics cores (e.g., graphics core(s)  2415 A, graphics core(s)  2415 B), each block including one or more graphics cores. Each graphics core includes a set of graphics execution resources that includes general-purpose and graphics specific execution logic to perform graphics and compute operations, as well as fixed function texture processing and/or machine learning and artificial intelligence acceleration logic. 
     In various embodiments the 3D pipeline  2312  can include fixed function and programmable logic to process one or more shader programs, such as vertex shaders, geometry shaders, pixel shaders, fragment shaders, compute shaders, or other shader programs, by processing the instructions and dispatching execution threads to the graphics core array  2414 . The graphics core array  2414  provides a unified block of execution resources for use in processing these shader programs. Multi-purpose execution logic (e.g., execution units) within the graphics core(s)  2415 A- 2414 B of the graphic core array  2414  includes support for various 3D API shader languages and can execute multiple simultaneous execution threads associated with multiple shaders. 
     In some embodiments, the graphics core array  2414  includes execution logic to perform media functions, such as video and/or image processing. In one embodiment, the execution units include general-purpose logic that is programmable to perform parallel general-purpose computational operations, in addition to graphics processing operations. The general-purpose logic can perform processing operations in parallel or in conjunction with general-purpose logic within the processor core(s)  2107  of  FIG. 21  or core  2202 A- 2202 N as in  FIG. 22 . 
     Output data generated by threads executing on the graphics core array  2414  can output data to memory in a unified return buffer (URB)  2418 . The URB  2418  can store data for multiple threads. In some embodiments the URB  2418  may be used to send data between different threads executing on the graphics core array  2414 . In some embodiments the URB  2418  may additionally be used for synchronization between threads on the graphics core array and fixed function logic within the shared function logic  2420 . 
     In some embodiments, graphics core array  2414  is scalable, such that the array includes a variable number of graphics cores, each having a variable number of execution units based on the target power and performance level of GPE  2410 . In one embodiment the execution resources are dynamically scalable, such that execution resources may be enabled or disabled as needed. 
     The graphics core array  2414  couples with shared function logic  2420  that includes multiple resources that are shared between the graphics cores in the graphics core array. The shared functions within the shared function logic  2420  are hardware logic units that provide specialized supplemental functionality to the graphics core array  2414 . In various embodiments, shared function logic  2420  includes but is not limited to sampler  2421 , math  2422 , and inter-thread communication (ITC)  2423  logic. Additionally, some embodiments implement one or more cache(s)  2425  within the shared function logic  2420 . 
     A shared function is implemented at least in a case where the demand for a given specialized function is insufficient for inclusion within the graphics core array  2414 . Instead a single instantiation of that specialized function is implemented as a stand-alone entity in the shared function logic  2420  and shared among the execution resources within the graphics core array  2414 . The precise set of functions that are shared between the graphics core array  2414  and included within the graphics core array  2414  varies across embodiments. In some embodiments, specific shared functions within the shared function logic  2420  that are used extensively by the graphics core array  2414  may be included within shared function logic  2416  within the graphics core array  2414 . In various embodiments, the shared function logic  2416  within the graphics core array  2414  can include some or all logic within the shared function logic  2420 . In one embodiment, all logic elements within the shared function logic  2420  may be duplicated within the shared function logic  2416  of the graphics core array  2414 . In one embodiment the shared function logic  2420  is excluded in favor of the shared function logic  2416  within the graphics core array  2414 . 
       FIG. 25  is a block diagram of hardware logic of a graphics processor core  2500 , according to some embodiments described herein. Elements of  FIG. 25  having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. The illustrated graphics processor core  2500 , in some embodiments, is included within the graphics core array  2414  of  FIG. 24 . The graphics processor core  2500 , sometimes referred to as a core slice, can be one or multiple graphics cores within a modular graphics processor. The graphics processor core  2500  is exemplary of one graphics core slice, and a graphics processor as described herein may include multiple graphics core slices based on target power and performance envelopes. Each graphics processor core  2500  can include a fixed function block  2530  coupled with multiple sub-cores  2501 A- 2501 F, also referred to as sub-slices, that include modular blocks of general-purpose and fixed function logic. 
     In some embodiments, the fixed function block  2530  includes a geometry/fixed function pipeline  2536  that can be shared by all sub-cores in the graphics processor core  2500 , for example, in lower performance and/or lower power graphics processor implementations. In various embodiments, the geometry/fixed function pipeline  2536  includes a 3D fixed function pipeline (e.g., 3D pipeline  2312  as in  FIG. 23  and  FIG. 24 ) a video front-end unit, a thread spawner and thread dispatcher, and a unified return buffer manager, which manages unified return buffers, such as the unified return buffer  2418  of  FIG. 24 . 
     In one embodiment the fixed function block  2530  also includes a graphics SoC interface  2537 , a graphics microcontroller  2538 , and a media pipeline  2539 . The graphics SoC interface  2537  provides an interface between the graphics processor core  2500  and other processor cores within a system on a chip integrated circuit. The graphics microcontroller  2538  is a programmable sub-processor that is configurable to manage various functions of the graphics processor core  2500 , including thread dispatch, scheduling, and pre-emption. The media pipeline  2539  (e.g., media pipeline  2316  of  FIG. 23  and  FIG. 24 ) includes logic to facilitate the decoding, encoding, pre-processing, and/or post-processing of multimedia data, including image and video data. The media pipeline  2539  implement media operations via requests to compute or sampling logic within the sub-cores  2501 - 2501 F. 
     In one embodiment the SoC interface  2537  enables the graphics processor core  2500  to communicate with general-purpose application processor cores (e.g., CPUs) and/or other components within an SoC, including memory hierarchy elements such as a shared last level cache memory, the system RAM, and/or embedded on-chip or on-package DRAM. The SoC interface  2537  can also enable communication with fixed function devices within the SoC, such as camera imaging pipelines, and enables the use of and/or implements global memory atomics that may be shared between the graphics processor core  2500  and CPUs within the SoC. The SoC interface  2537  can also implement power management controls for the graphics processor core  2500  and enable an interface between a clock domain of the graphic core  2500  and other clock domains within the SoC. In one embodiment the SoC interface  2537  enables receipt of command buffers from a command streamer and global thread dispatcher that are configured to provide commands and instructions to each of one or more graphics cores within a graphics processor. The commands and instructions can be dispatched to the media pipeline  2539 , when media operations are to be performed, or a geometry and fixed function pipeline (e.g., geometry and fixed function pipeline  2536 , geometry and fixed function pipeline  2514 ) when graphics processing operations are to be performed. 
     The graphics microcontroller  2538  can be configured to perform various scheduling and management tasks for the graphics processor core  2500 . In one embodiment the graphics microcontroller  2538  can perform graphics and/or compute workload scheduling on the various graphics parallel engines within execution unit (EU) arrays  2502 A- 2502 F,  2504 A- 2504 F within the sub-cores  2501 A- 2501 F. In this scheduling model, host software executing on a CPU core of an SoC including the graphics processor core  2500  can submit workloads one of multiple graphic processor doorbells, which invokes a scheduling operation on the appropriate graphics engine. Scheduling operations include determining which workload to run next, submitting a workload to a command streamer, pre-empting existing workloads running on an engine, monitoring progress of a workload, and notifying host software when a workload is complete. In one embodiment the graphics microcontroller  2538  can also facilitate low-power or idle states for the graphics processor core  2500 , providing the graphics processor core  2500  with the ability to save and restore registers within the graphics processor core  2500  across low-power state transitions independently from the operating system and/or graphics driver software on the system. 
     The graphics processor core  2500  may have greater than or fewer than the illustrated sub-cores  2501 A- 2501 F, up to N modular sub-cores. For each set of N sub-cores, the graphics processor core  2500  can also include shared function logic  2510 , shared and/or cache memory  2512 , a geometry/fixed function pipeline  2514 , as well as additional fixed function logic  2516  to accelerate various graphics and compute processing operations. The shared function logic  2510  can include logic units associated with the shared function logic  2420  of  FIG. 24  (e.g., sampler, math, and/or inter-thread communication logic) that can be shared by each N sub-cores within the graphics processor core  2500 . The shared and/or cache memory  2512  can be a last-level cache for the set of N sub-cores  2501 A- 2501 F within the graphics processor core  2500 , and can also serve as shared memory that is accessible by multiple sub-cores. The geometry/fixed function pipeline  2514  can be included instead of the geometry/fixed function pipeline  2536  within the fixed function block  2530  and can include the same or similar logic units. 
     In one embodiment the graphics processor core  2500  includes additional fixed function logic  2516  that can include various fixed function acceleration logic for use by the graphics processor core  2500 . In one embodiment the additional fixed function logic  2516  includes an additional geometry pipeline for use in position only shading. In position-only shading, two geometry pipelines exist, the full geometry pipeline within the geometry/fixed function pipeline  2516 ,  2536 , and a cull pipeline, which is an additional geometry pipeline which may be included within the additional fixed function logic  2516 . In one embodiment the cull pipeline is a trimmed down version of the full geometry pipeline. The full pipeline and the cull pipeline can execute different instances of the same application, each instance having a separate context. Position only shading can hide long cull runs of discarded triangles, enabling shading to be completed earlier in some instances. For example and in one embodiment the cull pipeline logic within the additional fixed function logic  2516  can execute position shaders in parallel with the main application and generally generates critical results faster than the full pipeline, as the cull pipeline fetches and shades only the position attribute of the vertices, without performing rasterization and rendering of the pixels to the frame buffer. The cull pipeline can use the generated critical results to compute visibility information for all the triangles without regard to whether those triangles are culled. The full pipeline (which in this instance may be referred to as a replay pipeline) can consume the visibility information to skip the culled triangles to shade only the visible triangles that are finally passed to the rasterization phase. 
     In one embodiment the additional fixed function logic  2516  can also include machine-learning acceleration logic, such as fixed function matrix multiplication logic, for implementations including optimizations for machine learning training or inferencing. 
     Within each graphics sub-core  2501 A- 2501 F includes a set of execution resources that may be used to perform graphics, media, and compute operations in response to requests by graphics pipeline, media pipeline, or shader programs. The graphics sub-cores  2501 A- 2501 F include multiple EU arrays  2502 A- 2502 F,  2504 A- 2504 F, thread dispatch and inter-thread communication (TD/IC) logic  2503 A- 2503 F, a 3D (e.g., texture) sampler  2505 A- 2505 F, a media sampler  2506 A- 2506 F, a shader processor  2507 A- 2507 F, and shared local memory (SLM)  2508 A- 2508 F. The EU arrays  2502 A- 2502 F,  2504 A- 2504 F each include multiple execution units, which are general-purpose graphics processing units capable of performing floating-point and integer/fixed-point logic operations in service of a graphics, media, or compute operation, including graphics, media, or compute shader programs. The TD/IC logic  2503 A- 2503 F performs local thread dispatch and thread control operations for the execution units within a sub-core and facilitate communication between threads executing on the execution units of the sub-core. The 3D sampler  2505 A- 2505 F can read texture or other 3D graphics related data into memory. The 3D sampler can read texture data differently based on a configured sample state and the texture format associated with a given texture. The media sampler  2506 A- 2506 F can perform similar read operations based on the type and format associated with media data. In one embodiment, each graphics sub-core  2501 A- 2501 F can alternately include a unified 3D and media sampler. Threads executing on the execution units within each of the sub-cores  2501 A- 2501 F can make use of shared local memory  2508 A- 2508 F within each sub-core, to enable threads executing within a thread group to execute using a common pool of on-chip memory. \ 
     Execution Units 
       FIG. 26A-26B  illustrate thread execution logic  2600  including an array of processing elements employed in a graphics processor core according to embodiments described herein. Elements of  FIG. 26A-26B  having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.  FIG. 26A  illustrates an overview of thread execution logic  2600 , which can include a variant of the hardware logic illustrated with each sub-core  2501 A- 2501 F of  FIG. 25 .  FIG. 26B  illustrates exemplary internal details of an execution unit. 
     As illustrated in  FIG. 26A , in some embodiments thread execution logic  2600  includes a shader processor  2602 , a thread dispatcher  2604 , instruction cache  2606 , a scalable execution unit array including a plurality of execution units  2608 A- 2608 N, a sampler  2610 , a data cache  2612 , and a data port  2614 . In one embodiment the scalable execution unit array can dynamically scale by enabling or disabling one or more execution units (e.g., any of execution unit  2608 A,  2608 B,  2608 C,  2608 D, through  2608 N- 1  and  2608 N) based on the computational requirements of a workload. In one embodiment the included components are interconnected via an interconnect fabric that links to each of the components. In some embodiments, thread execution logic  2600  includes one or more connections to memory, such as system memory or cache memory, through one or more of instruction cache  2606 , data port  2614 , sampler  2610 , and execution units  2608 A- 2608 N. In some embodiments, each execution unit (e.g.  2608 A) is a stand-alone programmable general-purpose computational unit that is capable of executing multiple simultaneous hardware threads while processing multiple data elements in parallel for each thread. In various embodiments, the array of execution units  2608 A- 2608 N is scalable to include any number individual execution units. 
     In some embodiments, the execution units  2608 A- 2608 N are primarily used to execute shader programs. A shader processor  2602  can process the various shader programs and dispatch execution threads associated with the shader programs via a thread dispatcher  2604 . In one embodiment the thread dispatcher includes logic to arbitrate thread initiation requests from the graphics and media pipelines and instantiate the requested threads on one or more execution unit in the execution units  2608 A- 2608 N. For example, a geometry pipeline can dispatch vertex, tessellation, or geometry shaders to the thread execution logic for processing. In some embodiments, thread dispatcher  2604  can also process runtime thread spawning requests from the executing shader programs. 
     In some embodiments, the execution units  2608 A- 2608 N support an instruction set that includes native support for many standard 3D graphics shader instructions, such that shader programs from graphics libraries (e.g., Direct 3D and OpenGL) are executed with a minimal translation. The execution units support vertex and geometry processing (e.g., vertex programs, geometry programs, vertex shaders), pixel processing (e.g., pixel shaders, fragment shaders) and general-purpose processing (e.g., compute and media shaders). Each of the execution units  2608 A- 2608 N is capable of multi-issue single instruction multiple data (SIMD) execution and multi-threaded operation enables an efficient execution environment in the face of higher latency memory accesses. Each hardware thread within each execution unit has a dedicated high-bandwidth register file and associated independent thread-state. Execution is multi-issue per clock to pipelines capable of integer, single and double precision floating point operations, SIMD branch capability, logical operations, transcendental operations, and other miscellaneous operations. While waiting for data from memory or one of the shared functions, dependency logic within the execution units  2608 A- 2608 N causes a waiting thread to sleep until the requested data has been returned. While the waiting thread is sleeping, hardware resources may be devoted to processing other threads. For example, during a delay associated with a vertex shader operation, an execution unit can perform operations for a pixel shader, fragment shader, or another type of shader program, including a different vertex shader. Various embodiments can apply to use execution by use of Single Instruction Multiple Thread (SIMT) as an alternate to use of SIMD or in addition to use of SIMD. Reference to a SIMD core or operation can apply also to SIMT or apply to SIMD in combination with SIMT. 
     Each execution unit in execution units  2608 A- 2608 N operates on arrays of data elements. The number of data elements is the “execution size,” or the number of channels for the instruction. An execution channel is a logical unit of execution for data element access, masking, and flow control within instructions. The number of channels may be independent of the number of physical Arithmetic Logic Units (ALUs) or Floating Point Units (FPUs) for a particular graphics processor. In some embodiments, execution units  2608 A- 2608 N support integer and floating-point data types. 
     The execution unit instruction set includes SIMD instructions. The various data elements can be stored as a packed data type in a register and the execution unit will process the various elements based on the data size of the elements. For example, when operating on a 256-bit wide vector, the 256 bits of the vector are stored in a register and the execution unit operates on the vector as four separate 64-bit packed data elements (Quad-Word (QW) size data elements), eight separate 32-bit packed data elements (Double Word (DW) size data elements), sixteen separate 16-bit packed data elements (Word (W) size data elements), or thirty-two separate 8-bit data elements (byte (B) size data elements). However, different vector widths and register sizes are possible. 
     In one embodiment one or more execution units can be combined into a fused execution unit  2609 A- 2609 N having thread control logic ( 2607 A- 2607 N) that is common to the fused EUs. Multiple EUs can be fused into an EU group. Each EU in the fused EU group can be configured to execute a separate SIMD hardware thread. The number of EUs in a fused EU group can vary according to embodiments. Additionally, various SIMD widths can be performed per-EU, including but not limited to SIMD8, SIMD16, and SIMD32. Each fused graphics execution unit  2609 A- 2609 N includes at least two execution units. For example, fused execution unit  2609 A includes a first EU  2608 A, second EU  2608 B, and thread control logic  2607 A that is common to the first EU  2608 A and the second EU  2608 B. The thread control logic  2607 A controls threads executed on the fused graphics execution unit  2609 A, allowing each EU within the fused execution units  2609 A- 2609 N to execute using a common instruction pointer register. 
     One or more internal instruction caches (e.g.,  2606 ) are included in the thread execution logic  2600  to cache thread instructions for the execution units. In some embodiments, one or more data caches (e.g.,  2612 ) are included to cache thread data during thread execution. In some embodiments, a sampler  2610  is included to provide texture sampling for 3D operations and media sampling for media operations. In some embodiments, sampler  2610  includes specialized texture or media sampling functionality to process texture or media data during the sampling process before providing the sampled data to an execution unit. 
     During execution, the graphics and media pipelines send thread initiation requests to thread execution logic  2600  via thread spawning and dispatch logic. Once a group of geometric objects has been processed and rasterized into pixel data, pixel processor logic (e.g., pixel shader logic, fragment shader logic, etc.) within the shader processor  2602  is invoked to further compute output information and cause results to be written to output surfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). In some embodiments, a pixel shader or fragment shader calculates the values of the various vertex attributes that are to be interpolated across the rasterized object. In some embodiments, pixel processor logic within the shader processor  2602  then executes an application programming interface (API)-supplied pixel or fragment shader program. To execute the shader program, the shader processor  2602  dispatches threads to an execution unit (e.g.,  2608 A) via thread dispatcher  2604 . In some embodiments, shader processor  2602  uses texture sampling logic in the sampler  2610  to access texture data in texture maps stored in memory. Arithmetic operations on the texture data and the input geometry data compute pixel color data for each geometric fragment, or discards one or more pixels from further processing. 
     In some embodiments, the data port  2614  provides a memory access mechanism for the thread execution logic  2600  to output processed data to memory for further processing on a graphics processor output pipeline. In some embodiments, the data port  2614  includes or couples to one or more cache memories (e.g., data cache  2612 ) to cache data for memory access via the data port. 
     As illustrated in  FIG. 26B , a graphics execution unit  2608  can include an instruction fetch unit  2637 , a general register file array (GRF)  2624 , an architectural register file array (ARF)  2626 , a thread arbiter  2622 , a send unit  2630 , a branch unit  2632 , a set of SIMD floating point units (FPUs)  2634 , and in one embodiment a set of dedicated integer SIMD ALUs  2635 . The GRF  2624  and ARF  2626  includes the set of general register files and architecture register files associated with each simultaneous hardware thread that may be active in the graphics execution unit  2608 . In one embodiment, per thread architectural state is maintained in the ARF  2626 , while data used during thread execution is stored in the GRF  2624 . The execution state of each thread, including the instruction pointers for each thread, can be held in thread-specific registers in the ARF  2626 . 
     In one embodiment the graphics execution unit  2608  has an architecture that is a combination of Simultaneous Multi-Threading (SMT) and fine-grained Interleaved Multi-Threading (IMT). The architecture has a modular configuration that can be fine-tuned at design time based on a target number of simultaneous threads and number of registers per execution unit, where execution unit resources are divided across logic used to execute multiple simultaneous threads. 
     In one embodiment, the graphics execution unit  2608  can co-issue multiple instructions, which may each be different instructions. The thread arbiter  2622  of the graphics execution unit thread  2608  can dispatch the instructions to one of the send unit  2630 , branch unit  2632 , or SIMD FPU(s)  2634  for execution. Each execution thread can access  128  general-purpose registers within the GRF  2624 , where each register can store 32 bytes, accessible as an 8-element vector of 32-bit data elements. In one embodiment, each execution unit thread has access to 4 Kbytes within the GRF  2624 , although embodiments are not so limited, and greater or fewer register resources may be provided in other embodiments. In one embodiment up to seven threads can execute simultaneously, although the number of threads per execution unit can also vary according to embodiments. In an embodiment in which seven threads may access 4 Kbytes, the GRF  2624  can store a total of 28 Kbytes. Flexible addressing modes can permit registers to be addressed together to build effectively wider registers or to represent strided rectangular block data structures. 
     In one embodiment, memory operations, sampler operations, and other longer-latency system communications are dispatched via “send” instructions that are executed by the message passing send unit  2630 . In one embodiment, branch instructions are dispatched to a dedicated branch unit  2632  to facilitate SIMD divergence and eventual convergence. 
     In one embodiment the graphics execution unit  2608  includes one or more SIMD floating point units (FPU(s))  2634  to perform floating-point operations. In one embodiment, the FPU(s)  2634  also support integer computation. In one embodiment the FPU(s)  2634  can SIMD execute up to M number of 32-bit floating-point (or integer) operations, or SIMD execute up to 2M 16-bit integer or 16-bit floating-point operations. In one embodiment, at least one of the FPU(s) provides extended math capability to support high-throughput transcendental math functions and double precision 64-bit floating-point. In some embodiments, a set of 8-bit integer SIMD ALUs  2635  are also present and may be specifically optimized to perform operations associated with machine learning computations. 
     In one embodiment, arrays of multiple instances of the graphics execution unit  2608  can be instantiated in a graphics sub-core grouping (e.g., a sub-slice). For scalability, product architects can choose the exact number of execution units per sub-core grouping. In one embodiment the execution unit  2608  can execute instructions across a plurality of execution channels. In a further embodiment, each thread executed on the graphics execution unit  2608  is executed on a different channel. 
       FIG. 27  is a block diagram illustrating a graphics processor instruction formats  2700  according to some embodiments. In one or more embodiment, the graphics processor execution units support an instruction set having instructions in multiple formats. The solid lined boxes illustrate the components that are generally included in an execution unit instruction, while the dashed lines include components that are optional or that are only included in a sub-set of the instructions. In some embodiments, instruction format  2700  described and illustrated are macro-instructions, in that they are instructions supplied to the execution unit, as opposed to micro-operations resulting from instruction decode once the instruction is processed. 
     In some embodiments, the graphics processor execution units natively support instructions in a 128-bit instruction format  2710 . A 64-bit compacted instruction format  2730  is available for some instructions based on the selected instruction, instruction options, and number of operands. The native 128-bit instruction format  2710  provides access to all instruction options, while some options and operations are restricted in the 64-bit format  2730 . The native instructions available in the 64-bit format  2730  vary by embodiment. In some embodiments, the instruction is compacted in part using a set of index values in an index field  2713 . The execution unit hardware references a set of compaction tables based on the index values and uses the compaction table outputs to reconstruct a native instruction in the 128-bit instruction format  2710 . Other sizes and formats of instruction can be used. 
     For each format, instruction opcode  2712  defines the operation that the execution unit is to perform. The execution units execute each instruction in parallel across the multiple data elements of each operand. For example, in response to an add instruction the execution unit performs a simultaneous add operation across each color channel representing a texture element or picture element. By default, the execution unit performs each instruction across all data channels of the operands. In some embodiments, instruction control field  2714  enables control over certain execution options, such as channels selection (e.g., predication) and data channel order (e.g., swizzle). For instructions in the 128-bit instruction format  2710  an exec-size field  2716  limits the number of data channels that will be executed in parallel. In some embodiments, exec-size field  2716  is not available for use in the 64-bit compact instruction format  2730 . 
     Some execution unit instructions have up to three operands including two source operands, src0  2720 , src1  2722 , and one destination  2718 . In some embodiments, the execution units support dual destination instructions, where one of the destinations is implied. Data manipulation instructions can have a third source operand (e.g., SRC2  2724 ), where the instruction opcode  2712  determines the number of source operands. An instruction&#39;s last source operand can be an immediate (e.g., hard-coded) value passed with the instruction. 
     In some embodiments, the 128-bit instruction format  2710  includes an access/address mode field  2726  specifying, for example, whether direct register addressing mode or indirect register addressing mode is used. When direct register addressing mode is used, the register address of one or more operands is directly provided by bits in the instruction. 
     In some embodiments, the 128-bit instruction format  2710  includes an access/address mode field  2726 , which specifies an address mode and/or an access mode for the instruction. In one embodiment the access mode is used to define a data access alignment for the instruction. Some embodiments support access modes including a 16-byte aligned access mode and a 1-byte aligned access mode, where the byte alignment of the access mode determines the access alignment of the instruction operands. For example, when in a first mode, the instruction may use byte-aligned addressing for source and destination operands and when in a second mode, the instruction may use 16-byte-aligned addressing for all source and destination operands. 
     In one embodiment, the address mode portion of the access/address mode field  2726  determines whether the instruction is to use direct or indirect addressing. When direct register addressing mode is used bits in the instruction directly provide the register address of one or more operands. When indirect register addressing mode is used, the register address of one or more operands may be computed based on an address register value and an address immediate field in the instruction. 
     In some embodiments, instructions are grouped based on opcode  2712  bit-fields to simplify Opcode decode  2740 . For an 8-bit opcode, bits  4 ,  5 , and  6  allow the execution unit to determine the type of opcode. The precise opcode grouping shown is merely an example. In some embodiments, a move and logic opcode group  2742  includes data movement and logic instructions (e.g., move (mov), compare (cmp)). In some embodiments, move and logic group  2742  shares the five most significant bits (MSB), where move (mov) instructions are in the form of 0000xxxxb and logic instructions are in the form of 0001xxxxb. A flow control instruction group  2744  (e.g., call, jump (jmp)) includes instructions in the form of 0010xxxxb (e.g., 0x20). A miscellaneous instruction group  2746  includes a mix of instructions, including synchronization instructions (e.g., wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instruction group  2748  includes component-wise arithmetic instructions (e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel math group  2748  performs the arithmetic operations in parallel across data channels. The vector math group  2750  includes arithmetic instructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math group performs arithmetic such as dot product calculations on vector operands. 
     Graphics Pipeline 
       FIG. 28  is a block diagram of another embodiment of a graphics processor  2800 . Elements of  FIG. 28  having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. 
     In some embodiments, graphics processor  2800  includes a geometry pipeline  2820 , a media pipeline  2830 , a display engine  2840 , thread execution logic  2850 , and a render output pipeline  2870 . In some embodiments, graphics processor  2800  is a graphics processor within a multi-core processing system that includes one or more general-purpose processing cores. The graphics processor is controlled by register writes to one or more control registers (not shown) or via commands issued to graphics processor  2800  via a ring interconnect  2802 . In some embodiments, ring interconnect  2802  couples graphics processor  2800  to other processing components, such as other graphics processors or general-purpose processors. Commands from ring interconnect  2802  are interpreted by a command streamer  2803 , which supplies instructions to individual components of the geometry pipeline  2820  or the media pipeline  2830 . 
     In some embodiments, command streamer  2803  directs the operation of a vertex fetcher  2805  that reads vertex data from memory and executes vertex-processing commands provided by command streamer  2803 . In some embodiments, vertex fetcher  2805  provides vertex data to a vertex shader  2807 , which performs coordinate space transformation and lighting operations to each vertex. In some embodiments, vertex fetcher  2805  and vertex shader  2807  execute vertex-processing instructions by dispatching execution threads to execution units  2852 A- 2852 B via a thread dispatcher  2831 . 
     In some embodiments, execution units  2852 A- 2852 B are an array of vector processors having an instruction set for performing graphics and media operations. In some embodiments, execution units  2852 A- 2852 B have an attached L1 cache  2851  that is specific for each array or shared between the arrays. The cache can be configured as a data cache, an instruction cache, or a single cache that is partitioned to contain data and instructions in different partitions. 
     In some embodiments, geometry pipeline  2820  includes tessellation components to perform hardware-accelerated tessellation of 3D objects. In some embodiments, a programmable hull shader  2811  configures the tessellation operations. A programmable domain shader  2817  provides back-end evaluation of tessellation output. A tessellator  2813  operates at the direction of hull shader  2811  and contains special purpose logic to generate a set of detailed geometric objects based on a coarse geometric model that is provided as input to geometry pipeline  2820 . In some embodiments, if tessellation is not used, tessellation components (e.g., hull shader  2811 , tessellator  2813 , and domain shader  2817 ) can be bypassed. 
     In some embodiments, complete geometric objects can be processed by a geometry shader  2819  via one or more threads dispatched to execution units  2852 A- 2852 B, or can proceed directly to the clipper  2829 . In some embodiments, the geometry shader operates on entire geometric objects, rather than vertices or patches of vertices as in previous stages of the graphics pipeline. If the tessellation is disabled, the geometry shader  2819  receives input from the vertex shader  2807 . In some embodiments, geometry shader  2819  is programmable by a geometry shader program to perform geometry tessellation if the tessellation units are disabled. 
     Before rasterization, a clipper  2829  processes vertex data. The clipper  2829  may be a fixed function clipper or a programmable clipper having clipping and geometry shader functions. In some embodiments, a rasterizer and depth test component  2873  in the render output pipeline  2870  dispatches pixel shaders to convert the geometric objects into per pixel representations. In some embodiments, pixel shader logic is included in thread execution logic  2850 . In some embodiments, an application can bypass the rasterizer and depth test component  2873  and access un-rasterized vertex data via a stream out unit  2823 . 
     The graphics processor  2800  has an interconnect bus, interconnect fabric, or some other interconnect mechanism that allows data and message passing amongst the major components of the processor. In some embodiments, execution units  2852 A- 2852 B and associated logic units (e.g., L1 cache  2851 , sampler  2854 , texture cache  2858 , etc.) interconnect via a data port  2856  to perform memory access and communicate with render output pipeline components of the processor. In some embodiments, sampler  2854 , L1 cache  2851 , texture cache  2858 , and execution units  2852 A- 2852 B each have separate memory access paths. In one embodiment the texture cache  2858  can also be configured as a sampler cache. 
     In some embodiments, render output pipeline  2870  contains a rasterizer and depth test component  2873  that converts vertex-based objects into an associated pixel-based representation. In some embodiments, the rasterizer logic includes a windower/masker unit to perform fixed function triangle and line rasterization. An associated render cache  2878  and depth cache  2879  are also available in some embodiments. A pixel operations component  2877  performs pixel-based operations on the data, though in some instances, pixel operations associated with 2D operations (e.g. bit block image transfers with blending) are performed by the 2D engine  2841 , or substituted at display time by the display controller  2843  using overlay display planes. In some embodiments, a shared L3 cache  2875  is available to all graphics components, allowing the sharing of data without the use of main system memory. 
     In some embodiments, graphics processor media pipeline  2830  includes a media engine  2837  and a video front-end  2834 . In some embodiments, video front-end  2834  receives pipeline commands from the command streamer  2803 . In some embodiments, media pipeline  2830  includes a separate command streamer. In some embodiments, video front-end  2834  processes media commands before sending the command to the media engine  2837 . In some embodiments, media engine  2837  includes thread spawning functionality to spawn threads for dispatch to thread execution logic  2850  via thread dispatcher  2831 . 
     In some embodiments, graphics processor  2800  includes a display engine  2840 . In some embodiments, display engine  2840  is external to processor  2800  and couples with the graphics processor via the ring interconnect  2802 , or some other interconnect bus or fabric. In some embodiments, display engine  2840  includes a 2D engine  2841  and a display controller  2843 . In some embodiments, display engine  2840  contains special purpose logic capable of operating independently of the 3D pipeline. In some embodiments, display controller  2843  couples with a display device (not shown), which may be a system integrated display device, as in a laptop computer, or an external display device attached via a display device connector. 
     In some embodiments, the geometry pipeline  2820  and media pipeline  2830  are configurable to perform operations based on multiple graphics and media programming interfaces and are not specific to any one application programming interface (API). In some embodiments, driver software for the graphics processor translates API calls that are specific to a particular graphics or media library into commands that can be processed by the graphics processor. In some embodiments, support is provided for the Open Graphics Library (OpenGL), Open Computing Language (OpenCL), and/or Vulkan graphics and compute API, all from the Khronos Group. In some embodiments, support may also be provided for the Direct3D library from the Microsoft Corporation. In some embodiments, a combination of these libraries may be supported. Support may also be provided for the Open Source Computer Vision Library (OpenCV). A future API with a compatible 3D pipeline would also be supported if a mapping can be made from the pipeline of the future API to the pipeline of the graphics processor. 
     Graphics Pipeline Programming 
       FIG. 29A  is a block diagram illustrating a graphics processor command format  2900  according to some embodiments.  FIG. 29B  is a block diagram illustrating a graphics processor command sequence  2910  according to an embodiment. The solid lined boxes in  FIG. 29A  illustrate the components that are generally included in a graphics command while the dashed lines include components that are optional or that are only included in a sub-set of the graphics commands. The exemplary graphics processor command format  2900  of  FIG. 29A  includes data fields to identify a client  2902 , a command operation code (opcode)  2904 , and data  2906  for the command. A sub-opcode  2905  and a command size  2908  are also included in some commands. 
     In some embodiments, client  2902  specifies the client unit of the graphics device that processes the command data. In some embodiments, a graphics processor command parser examines the client field of each command to condition the further processing of the command and route the command data to the appropriate client unit. In some embodiments, the graphics processor client units include a memory interface unit, a render unit, a 2D unit, a 3D unit, and a media unit. Each client unit has a corresponding processing pipeline that processes the commands. Once the command is received by the client unit, the client unit reads the opcode  2904  and, if present, sub-opcode  2905  to determine the operation to perform. The client unit performs the command using information in data field  2906 . For some commands an explicit command size  2908  is expected to specify the size of the command. In some embodiments, the command parser automatically determines the size of at least some of the commands based on the command opcode. In some embodiments, commands are aligned via multiples of a double word. Other command formats can be used. 
     The flow diagram in  FIG. 29B  illustrates an exemplary graphics processor command sequence  2910 . In some embodiments, software or firmware of a data processing system that features an embodiment of a graphics processor uses a version of the command sequence shown to set up, execute, and terminate a set of graphics operations. A sample command sequence is shown and described for purposes of example only as embodiments are not limited to these specific commands or to this command sequence. Moreover, the commands may be issued as batch of commands in a command sequence, such that the graphics processor will process the sequence of commands in at least partially concurrence. 
     In some embodiments, the graphics processor command sequence  2910  may begin with a pipeline flush command  2912  to cause any active graphics pipeline to complete the currently pending commands for the pipeline. In some embodiments, the 3D pipeline  2922  and the media pipeline  2924  do not operate concurrently. The pipeline flush is performed to cause the active graphics pipeline to complete any pending commands. In response to a pipeline flush, the command parser for the graphics processor will pause command processing until the active drawing engines complete pending operations and the relevant read caches are invalidated. Optionally, any data in the render cache that is marked ‘dirty’ can be flushed to memory. In some embodiments, pipeline flush command  2912  can be used for pipeline synchronization or before placing the graphics processor into a low power state. 
     In some embodiments, a pipeline select command  2913  is used when a command sequence requires the graphics processor to explicitly switch between pipelines. In some embodiments, a pipeline select command  2913  is required only once within an execution context before issuing pipeline commands unless the context is to issue commands for both pipelines. In some embodiments, a pipeline flush command  2912  is required immediately before a pipeline switch via the pipeline select command  2913 . 
     In some embodiments, a pipeline control command  2914  configures a graphics pipeline for operation and is used to program the 3D pipeline  2922  and the media pipeline  2924 . In some embodiments, pipeline control command  2914  configures the pipeline state for the active pipeline. In one embodiment, the pipeline control command  2914  is used for pipeline synchronization and to clear data from one or more cache memories within the active pipeline before processing a batch of commands. 
     In some embodiments, commands to configure the return buffer state  2916  are used to configure a set of return buffers for the respective pipelines to write data. Some pipeline operations require the allocation, selection, or configuration of one or more return buffers into which the operations write intermediate data during processing. In some embodiments, the graphics processor also uses one or more return buffers to store output data and to perform cross thread communication. In some embodiments, the return buffer state  2916  includes selecting the size and number of return buffers to use for a set of pipeline operations. 
     The remaining commands in the command sequence differ based on the active pipeline for operations. Based on a pipeline determination  2920 , the command sequence is tailored to the 3D pipeline  2922  beginning with the 3D pipeline state  2930  or the media pipeline  2924  beginning at the media pipeline state  2940 . 
     The commands to configure the 3D pipeline state  2930  include 3D state setting commands for vertex buffer state, vertex element state, constant color state, depth buffer state, and other state variables that are to be configured before 3D primitive commands are processed. The values of these commands are determined at least in part based on the particular 3D API in use. In some embodiments, 3D pipeline state  2930  commands are also able to selectively disable or bypass certain pipeline elements if those elements will not be used. 
     In some embodiments, 3D primitive  2932  command is used to submit 3D primitives to be processed by the 3D pipeline. Commands and associated parameters that are passed to the graphics processor via the 3D primitive  2932  command are forwarded to the vertex fetch function in the graphics pipeline. The vertex fetch function uses the 3D primitive  2932  command data to generate vertex data structures. The vertex data structures are stored in one or more return buffers. In some embodiments, 3D primitive  2932  command is used to perform vertex operations on 3D primitives via vertex shaders. To process vertex shaders, 3D pipeline  2922  dispatches shader execution threads to graphics processor execution units. 
     In some embodiments, 3D pipeline  2922  is triggered via an execute  2934  command or event. In some embodiments, a register write triggers command execution. In some embodiments, execution is triggered via a ‘go’ or ‘kick’ command in the command sequence. In one embodiment, command execution is triggered using a pipeline synchronization command to flush the command sequence through the graphics pipeline. The 3D pipeline will perform geometry processing for the 3D primitives. Once operations are complete, the resulting geometric objects are rasterized and the pixel engine colors the resulting pixels. Additional commands to control pixel shading and pixel back end operations may also be included for those operations. 
     In some embodiments, the graphics processor command sequence  2910  follows the media pipeline  2924  path when performing media operations. In general, the specific use and manner of programming for the media pipeline  2924  depends on the media or compute operations to be performed. Specific media decode operations may be offloaded to the media pipeline during media decode. In some embodiments, the media pipeline can also be bypassed and media decode can be performed in whole or in part using resources provided by one or more general-purpose processing cores. In one embodiment, the media pipeline also includes elements for general-purpose graphics processor unit (GPGPU) operations, where the graphics processor is used to perform SIMD vector operations using computational shader programs that are not explicitly related to the rendering of graphics primitives. 
     In some embodiments, media pipeline  2924  is configured in a similar manner as the 3D pipeline  2922 . A set of commands to configure the media pipeline state  2940  are dispatched or placed into a command queue before the media object commands  2942 . In some embodiments, commands for the media pipeline state  2940  include data to configure the media pipeline elements that will be used to process the media objects. This includes data to configure the video decode and video encode logic within the media pipeline, such as encode or decode format. In some embodiments, commands for the media pipeline state  2940  also support the use of one or more pointers to “indirect” state elements that contain a batch of state settings. 
     In some embodiments, media object commands  2942  supply pointers to media objects for processing by the media pipeline. The media objects include memory buffers containing video data to be processed. In some embodiments, all media pipeline states must be valid before issuing a media object command  2942 . Once the pipeline state is configured and media object commands  2942  are queued, the media pipeline  2924  is triggered via an execute command  2944  or an equivalent execute event (e.g., register write). Output from media pipeline  2924  may then be post processed by operations provided by the 3D pipeline  2922  or the media pipeline  2924 . In some embodiments, GPGPU operations are configured and executed in a similar manner as media operations. 
     Graphics Software Architecture 
       FIG. 30  illustrates an exemplary graphics software architecture for a data processing system  3000  according to some embodiments. In some embodiments, software architecture includes a 3D graphics application  3010 , an operating system  3020 , and at least one processor  3030 . In some embodiments, processor  3030  includes a graphics processor  3032  and one or more general-purpose processor core(s)  3034 . The graphics application  3010  and operating system  3020  each execute in the system memory  3050  of the data processing system. 
     In some embodiments, 3D graphics application  3010  contains one or more shader programs including shader instructions  3012 . The shader language instructions may be in a high-level shader language, such as the High-Level Shader Language (HLSL) of Direct3D, the OpenGL Shader Language (GLSL), and so forth. The application also includes executable instructions  3014  in a machine language suitable for execution by the general-purpose processor core  3034 . The application also includes graphics objects  3016  defined by vertex data. 
     In some embodiments, operating system  3020  is a Microsoft® Windows® operating system from the Microsoft Corporation, a proprietary UNIX-like operating system, or an open source UNIX-like operating system using a variant of the Linux kernel. The operating system  3020  can support a graphics API  3022  such as the Direct3D API, the OpenGL API, or the Vulkan API. When the Direct3D API is in use, the operating system  3020  uses a front-end shader compiler  3024  to compile any shader instructions  3012  in HLSL into a lower-level shader language. The compilation may be a just-in-time (JIT) compilation or the application can perform shader pre-compilation. In some embodiments, high-level shaders are compiled into low-level shaders during the compilation of the 3D graphics application  3010 . In some embodiments, the shader instructions  3012  are provided in an intermediate form, such as a version of the Standard Portable Intermediate Representation (SPIR) used by the Vulkan API. 
     In some embodiments, user mode graphics driver  3026  contains a back-end shader compiler  3027  to convert the shader instructions  3012  into a hardware specific representation. When the OpenGL API is in use, shader instructions  3012  in the GLSL high-level language are passed to a user mode graphics driver  3026  for compilation. In some embodiments, user mode graphics driver  3026  uses operating system kernel mode functions  3028  to communicate with a kernel mode graphics driver  3029 . In some embodiments, kernel mode graphics driver  3029  communicates with graphics processor  3032  to dispatch commands and instructions. 
     IP Core Implementations 
     One or more aspects of at least one embodiment may be implemented by representative code stored on a machine-readable medium which represents and/or defines logic within an integrated circuit such as a processor. For example, the machine-readable medium may include instructions which represent various logic within the processor. When read by a machine, the instructions may cause the machine to fabricate the logic to perform the techniques described herein. Such representations, known as “IP cores,” are reusable units of logic for an integrated circuit that may be stored on a tangible, machine-readable medium as a hardware model that describes the structure of the integrated circuit. The hardware model may be supplied to various customers or manufacturing facilities, which load the hardware model on fabrication machines that manufacture the integrated circuit. The integrated circuit may be fabricated such that the circuit performs operations described in association with any of the embodiments described herein. 
       FIG. 31A  is a block diagram illustrating an IP core development system  3100  that may be used to manufacture an integrated circuit to perform operations according to an embodiment. The IP core development system  3100  may be used to generate modular, re-usable designs that can be incorporated into a larger design or used to construct an entire integrated circuit (e.g., an SOC integrated circuit). A design facility  3130  can generate a software simulation  3110  of an IP core design in a high-level programming language (e.g., C/C++). The software simulation  3110  can be used to design, test, and verify the behavior of the IP core using a simulation model  3112 . The simulation model  3112  may include functional, behavioral, and/or timing simulations. A register transfer level (RTL) design  3115  can then be created or synthesized from the simulation model  3112 . The RTL design  3115  is an abstraction of the behavior of the integrated circuit that models the flow of digital signals between hardware registers, including the associated logic performed using the modeled digital signals. In addition to an RTL design  3115 , lower-level designs at the logic level or transistor level may also be created, designed, or synthesized. Thus, the particular details of the initial design and simulation may vary. 
     The RTL design  3115  or equivalent may be further synthesized by the design facility into a hardware model  3120 , which may be in a hardware description language (HDL), or some other representation of physical design data. The HDL may be further simulated or tested to verify the IP core design. The IP core design can be stored for delivery to a 3 rd  party fabrication facility  3165  using non-volatile memory  3140  (e.g., hard disk, flash memory, or any non-volatile storage medium). Alternatively, the IP core design may be transmitted (e.g., via the Internet) over a wired connection  3150  or wireless connection  3160 . The fabrication facility  3165  may then fabricate an integrated circuit that is based at least in part on the IP core design. The fabricated integrated circuit can be configured to perform operations in accordance with at least one embodiment described herein. 
       FIG. 31B  illustrates a cross-section side view of an integrated circuit package assembly  3170 , according to some embodiments described herein. The integrated circuit package assembly  3170  illustrates an implementation of one or more processor or accelerator devices as described herein. The package assembly  3170  includes multiple units of hardware logic  3172 ,  3174  connected to a substrate  3180 . The logic  3172 ,  3174  may be implemented at least partly in configurable logic or fixed-functionality logic hardware, and can include one or more portions of any of the processor core(s), graphics processor(s), or other accelerator devices described herein. Each unit of logic  3172 ,  3174  can be implemented within a semiconductor die and coupled with the substrate  3180  via an interconnect structure  3173 . The interconnect structure  3173  may be configured to route electrical signals between the logic  3172 ,  3174  and the substrate  3180 , and can include interconnects such as, but not limited to bumps or pillars. In some embodiments, the interconnect structure  3173  may be configured to route electrical signals such as, for example, input/output (I/O) signals and/or power or ground signals associated with the operation of the logic  3172 ,  3174 . In some embodiments, the substrate  3180  is an epoxy-based laminate substrate. The substrate  3180  may include other suitable types of substrates in other embodiments. The package assembly  3170  can be connected to other electrical devices via a package interconnect  3183 . The package interconnect  3183  may be coupled to a surface of the substrate  3180  to route electrical signals to other electrical devices, such as a motherboard, other chipset, or multi-chip module. 
     In some embodiments, the units of logic  3172 ,  3174  are electrically coupled with a bridge  3182  that is configured to route electrical signals between the logic  3172 ,  3174 . The bridge  3182  may be a dense interconnect structure that provides a route for electrical signals. The bridge  3182  may include a bridge substrate composed of glass or a suitable semiconductor material. Electrical routing features can be formed on the bridge substrate to provide a chip-to-chip connection between the logic  3172 ,  3174 . 
     Although two units of logic  3172 ,  3174  and a bridge  3182  are illustrated, embodiments described herein may include more or fewer logic units on one or more dies. The one or more dies may be connected by zero or more bridges, as the bridge  3182  may be excluded when the logic is included on a single die. Alternatively, multiple dies or units of logic can be connected by one or more bridges. Additionally, multiple logic units, dies, and bridges can be connected together in other possible configurations, including three-dimensional configurations. 
     Exemplary System on a Chip Integrated Circuit 
       FIG. 32-33  illustrated exemplary integrated circuits and associated graphics processors that may be fabricated using one or more IP cores, according to various embodiments described herein. In addition to what is illustrated, other logic and circuits may be included, including additional graphics processors/cores, peripheral interface controllers, or general-purpose processor cores. 
       FIG. 32  is a block diagram illustrating an exemplary system on a chip integrated circuit  3200  that may be fabricated using one or more IP cores, according to an embodiment. Exemplary integrated circuit  3200  includes one or more application processor(s)  3205  (e.g., CPUs), at least one graphics processor  3210 , and may additionally include an image processor  3215  and/or a video processor  3220 , any of which may be a modular IP core from the same or multiple different design facilities. Integrated circuit  3200  includes peripheral or bus logic including a USB controller  3225 , UART controller  3230 , an SPI/SDIO controller  3235 , and an I 2 S/I 2 C controller  3240 . Additionally, the integrated circuit can include a display device  3245  coupled to one or more of a high-definition multimedia interface (HDMI) controller  3250  and a mobile industry processor interface (MIPI) display interface  3255 . Storage may be provided by a flash memory subsystem  3260  including flash memory and a flash memory controller. Memory interface may be provided via a memory controller  3265  for access to SDRAM or SRAM memory devices. Some integrated circuits additionally include an embedded security engine  3270 . 
       FIG. 33A-33B  are block diagrams illustrating exemplary graphics processors for use within an SoC, according to embodiments described herein.  FIG. 33A  illustrates an exemplary graphics processor  3310  of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment.  FIG. 33B  illustrates an additional exemplary graphics processor  3340  of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. Graphics processor  3310  of  FIG. 33A  is an example of a low power graphics processor core. Graphics processor  3340  of  FIG. 33B  is an example of a higher performance graphics processor core. Each of the graphics processors  3310 ,  3340  can be variants of the graphics processor  3210  of  FIG. 32 . 
     As shown in  FIG. 33A , graphics processor  3310  includes a vertex processor  3305  and one or more fragment processor(s)  3315 A- 3315 N (e.g.,  3315 A,  3315 B,  3315 C,  3315 D, through  3315 N- 1 , and  3315 N). Graphics processor  3310  can execute different shader programs via separate logic, such that the vertex processor  3305  is optimized to execute operations for vertex shader programs, while the one or more fragment processor(s)  3315 A- 3315 N execute fragment (e.g., pixel) shading operations for fragment or pixel shader programs. The vertex processor  3305  performs the vertex processing stage of the 3D graphics pipeline and generates primitives and vertex data. The fragment processor(s)  3315 A- 3315 N use the primitive and vertex data generated by the vertex processor  3305  to produce a framebuffer that is displayed on a display device. In one embodiment, the fragment processor(s)  3315 A- 3315 N are optimized to execute fragment shader programs as provided for in the OpenGL API, which may be used to perform similar operations as a pixel shader program as provided for in the Direct 3D API. 
     Graphics processor  3310  additionally includes one or more memory management units (MMUs)  3320 A- 3320 B, cache(s)  3325 A- 3325 B, and circuit interconnect(s)  3330 A- 3330 B. The one or more MMU(s)  3320 A- 3320 B provide for virtual to physical address mapping for the graphics processor  3310 , including for the vertex processor  3305  and/or fragment processor(s)  3315 A- 3315 N, which may reference vertex or image/texture data stored in memory, in addition to vertex or image/texture data stored in the one or more cache(s)  3325 A- 3325 B. In one embodiment the one or more MMU(s)  3320 A- 3320 B may be synchronized with other MMUs within the system, including one or more MMUs associated with the one or more application processor(s)  3205 , image processor  3215 , and/or video processor  3220  of  FIG. 32 , such that each processor  3205 - 3220  can participate in a shared or unified virtual memory system. The one or more circuit interconnect(s)  3330 A- 3330 B enable graphics processor  3310  to interface with other IP cores within the SoC, either via an internal bus of the SoC or via a direct connection, according to embodiments. 
     As shown  FIG. 33B , graphics processor  3340  includes the one or more MMU(s)  3320 A- 3320 B, cache(s)  3325 A- 3325 B, and circuit interconnect(s)  3330 A- 3330 B of the graphics processor  3310  of  FIG. 33A . Graphics processor  3340  includes one or more shader cores  3355 A- 3355 N (e.g.,  3355 A,  3355 B,  3355 C,  3355 D,  3355 E,  3355 F, through  3355 N- 1 , and  3355 N), which provides for a unified shader core architecture in which a single core or type or core can execute all types of programmable shader code, including shader program code to implement vertex shaders, fragment shaders, and/or compute shaders. The exact number of shader cores present can vary among embodiments and implementations. Additionally, graphics processor  3340  includes an inter-core task manager  3345 , which acts as a thread dispatcher to dispatch execution threads to one or more shader cores  3355 A- 3355 N and a tiling unit  3358  to accelerate tiling operations for tile-based rendering, in which rendering operations for a scene are subdivided in image space, for example to exploit local spatial coherence within a scene or to optimize use of internal caches. 
     In some embodiments, an apparatus includes a plurality of processors including a host processor and a plurality of graphics processing units (GPUs) to process data, each of the plurality of GPUs including a prefetcher and a cache; and a memory for storage of data, the memory including a plurality of memory elements, wherein the prefetcher of each of the plurality of GPUs is to prefetch data from the memory to the cache of the respective GPU; and wherein the prefetcher of a GPU of the plurality of GPUs is prohibited from prefetching from a page that is not owned by the GPU or by the host processor. 
     In some embodiments, the memory includes a unified virtual memory. 
     In some embodiments, a prefetch of a page by a prefetcher of a GPU of the plurality of GPUs is halted upon reaching a boundary of the page. 
     In some embodiments, a prefetch of a page by a prefetcher of a GPU of the plurality of GPUs is halted upon reaching a boundary of a memory surface. 
     In some embodiments, a prefetch instruction from a prefetcher of a GPU of the plurality of GPUs is a gather/scatter prefetch message including a plurality of prefetch addresses. 
     In some embodiments, the apparatus is to parse the gather/scatter prefetch message and issue a prefetch message for each of the plurality of prefetch addresses. 
     In some embodiments, the gather/scatter prefetch message further includes an entry for each of the plurality of addresses to indicate a cache level for prefetching. 
     In some embodiments, a prefetcher of a GPU of the plurality of GPUs is to send a flag to a thread in a core of the GPU when a prefetch for the thread is complete. 
     In some embodiments, one or more non-transitory computer-readable storage mediums having stored thereon executable computer program instructions that, when executed by one or more processors, cause the one or more processors to perform operations including generating a prefetch instruction by a prefetcher of a first graphics processing unit (GPU), the first GPU being one GPU of a plurality of GPUs in a computing system, the prefetch instruction being directed to a memory including a plurality of memory elements; and caching prefetched data in a cache of the first GPU, wherein the prefetcher of the first GPU is prohibited from prefetching from a page that is not owned by the first GPU or by a host processor of the computing system. 
     In some embodiments, the memory includes a unified virtual memory. 
     In some embodiments, the instructions further include instructions for halting a prefetch of a page by the prefetcher of the first GPU upon reaching a boundary of the page. 
     In some embodiments, the instructions further include instructions for halting a prefetch of a page by the prefetcher of the first GPU upon reaching a boundary of a memory surface. 
     In some embodiments, a prefetch instruction from the prefetcher of the first GPU is a gather/scatter prefetch message including a plurality of prefetch addresses. 
     In some embodiments, the instructions further include instructions for parsing the gather/scatter prefetch message and issuing a prefetch message for each of the plurality of prefetch addresses. 
     In some embodiments, the gather/scatter prefetch message further includes an entry for each of the plurality of addresses to indicate a cache level for prefetching. 
     In some embodiments, the instructions further include instructions for sending a flag from the prefetcher of the first GPU to a thread in a core of the first GPU when a prefetch for the thread is complete. 
     In some embodiments, a method includes generating a prefetch instruction by a prefetcher of a first graphics processing unit (GPU), the first GPU being one GPU of a plurality of GPUs in a computing system, the prefetch instruction being directed to a memory including a plurality of memory elements; and caching prefetched data in a cache of the first GPU, wherein the prefetcher of the first GPU is prohibited from prefetching from a page that is not owned by the first GPU or by a host processor of the computing system. 
     In some embodiments, the memory includes a unified virtual memory. 
     In some embodiments, the method further includes halting a prefetch of a page by the prefetcher of the first GPU upon reaching a boundary of the page. 
     In some embodiments, the method further includes halting a prefetch of a page by the prefetcher of the first GPU upon reaching a boundary of a memory surface. 
     In some embodiments, a prefetch instruction from the prefetcher of the first GPU is a gather/scatter prefetch message including a plurality of prefetch addresses. 
     In some embodiments, the method further includes parsing the gather/scatter prefetch message and issuing a prefetch message for each of the plurality of prefetch addresses. 
     In some embodiments, the gather/scatter prefetch message further includes an entry for each of the plurality of addresses to indicate a cache level for prefetching. 
     In some embodiments, the method further includes sending a flag from the prefetcher of the first GPU to a thread in a core of the first GPU when a prefetch for the thread is complete. 
     In some embodiments, a apparatus includes means for generating a prefetch instruction by a prefetcher of a first graphics processing unit (GPU), the first GPU being one GPU of a plurality of GPUs in a computing system, the prefetch instruction being directed to a memory including a plurality of memory elements; and means for caching prefetched data in a cache of the first GPU, wherein the prefetcher of the first GPU is prohibited from prefetching from a page that is not owned by the first GPU or by a host processor of the computing system. 
     In some embodiments, the memory includes a unified virtual memory. 
     In some embodiments, the apparatus further includes means for halting a prefetch of a page by the prefetcher of the first GPU upon reaching a boundary of the page. 
     In some embodiments, the apparatus further includes means for halting a prefetch of a page by the prefetcher of the first GPU upon reaching a boundary of a memory surface. 
     In some embodiments, a prefetch instruction from the prefetcher of the first GPU is a gather/scatter prefetch message including a plurality of prefetch addresses. 
     In some embodiments, the apparatus further includes means for parsing the gather/scatter prefetch message and issuing a prefetch message for each of the plurality of prefetch addresses. 
     In some embodiments, the gather/scatter prefetch message further includes an entry for each of the plurality of addresses to indicate a cache level for prefetching. 
     In some embodiments, the apparatus further includes means for sending a flag from the prefetcher of the first GPU to a thread in a core of the first GPU when a prefetch for the thread is complete. 
     In the description above, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the described embodiments. It will be apparent, however, to one skilled in the art that embodiments may be practiced without some of these specific details. In other instances, well-known structures and devices are shown in block diagram form. There may be intermediate structure between illustrated components. The components described or illustrated herein may have additional inputs or outputs that are not illustrated or described. 
     Various embodiments may include various processes. These processes may be performed by hardware components or may be embodied in computer program or machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the processes. Alternatively, the processes may be performed by a combination of hardware and software. 
     Portions of various embodiments may be provided as a computer program product, which may include a computer-readable medium having stored thereon computer program instructions, which may be used to program a computer (or other electronic devices) for execution by one or more processors to perform a process according to certain embodiments. The computer-readable medium may include, but is not limited to, magnetic disks, optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically-erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or other type of computer-readable medium suitable for storing electronic instructions. Moreover, embodiments may also be downloaded as a computer program product, wherein the program may be transferred from a remote computer to a requesting computer. In some embodiments, a non-transitory computer-readable storage medium has stored thereon data representing sequences of instructions that, when executed by a processor, cause the processor to perform certain operations. 
     Many of the methods are described in their most basic form, but processes can be added to or deleted from any of the methods and information can be added or subtracted from any of the described messages without departing from the basic scope of the present embodiments. It will be apparent to those skilled in the art that many further modifications and adaptations can be made. The particular embodiments are not provided to limit the concept but to illustrate it. The scope of the embodiments is not to be determined by the specific examples provided above but only by the claims below. 
     If it is said that an element “A” is coupled to or with element “B,” element A may be directly coupled to element B or be indirectly coupled through, for example, element C. When the specification or claims state that a component, feature, structure, process, or characteristic A “causes” a component, feature, structure, process, or characteristic B, it means that “A” is at least a partial cause of “B” but that there may also be at least one other component, feature, structure, process, or characteristic that assists in causing “B.” If the specification indicates that a component, feature, structure, process, or characteristic “may”, “might”, or “could” be included, that particular component, feature, structure, process, or characteristic is not required to be included. If the specification or claim refers to “a” or “an” element, this does not mean there is only one of the described elements. 
     An embodiment is an implementation or example. Reference in the specification to “an embodiment,” “one embodiment,” “some embodiments,” or “other embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments. The various appearances of “an embodiment,” “one embodiment,” or “some embodiments” are not necessarily all referring to the same embodiments. It should be appreciated that in the foregoing description of exemplary embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various novel aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed embodiments requires more features than are expressly recited in each claim. Rather, as the following claims reflect, novel aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims are hereby expressly incorporated into this description, with each claim standing on its own as a separate embodiment.