Patent Publication Number: US-11379229-B2

Title: Apparatus and method for adaptable and efficient lane-wise tensor processing

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of application Ser. No. 16/147,696, filed Sep. 29, 2018, which is hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The embodiments of the invention relate generally to the field of computer processors. More particularly, the embodiments relate to an apparatus and method for adaptable and efficient lane-wise tensor processing. 
     BACKGROUND ART 
     An instruction set, or instruction set architecture (ISA), is the part of the computer architecture related to programming, including the native data types, instructions, register architecture, addressing modes, memory architecture, interrupt and exception handling, and external input and output (I/O). It should be noted that the term “instruction” generally refers herein to macro-instructions—that is instructions that are provided to the processor for execution—as opposed to micro-instructions or micro-ops—that is the result of a processor&#39;s decoder decoding macro-instructions. The micro-instructions or micro-ops can be configured to instruct an execution unit on the processor to perform operations to implement the logic associated with the macro-instruction. 
     The ISA is distinguished from the microarchitecture, which is the set of processor design techniques used to implement the instruction set. Processors with different microarchitectures can share a common instruction set. For example, Intel® Pentium 4 processors, Intel® Core™ processors, and processors from Advanced Micro Devices, Inc. of Sunnyvale Calif. implement nearly identical versions of the x86 instruction set (with some extensions that have been added with newer versions), but have different internal designs. For example, the same register architecture of the ISA may be implemented in different ways in different microarchitectures using well-known techniques, including dedicated physical registers, one or more dynamically allocated physical registers using a register renaming mechanism (e.g., the use of a Register Alias Table (RAT), a Reorder Buffer (ROB) and a retirement register file). Unless otherwise specified, the phrases register architecture, register file, and register are used herein to refer to that which is visible to the software/programmer and the manner in which instructions specify registers. Where a distinction is required, the adjective “logical,” “architectural,” or “software visible” will be used to indicate registers/files in the register architecture, while different adjectives will be used to designate registers in a given microarchitecture (e.g., physical register, reorder buffer, retirement register, register pool). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A better understanding of the present invention can be obtained from the following detailed description in conjunction with the following drawings, in which: 
         FIGS. 1A and 1B  are block diagrams illustrating a generic vector friendly instruction format and instruction templates thereof according to embodiments of the invention; 
         FIGS. 2A-C  are block diagrams illustrating an exemplary VEX instruction format according to embodiments of the invention; 
         FIG. 3  is a block diagram of a register architecture according to one embodiment of the invention; and 
         FIG. 4A  is a block diagram illustrating both an exemplary in-order fetch, decode, retire pipeline and an exemplary register renaming, out-of-order issue/execution pipeline according to embodiments of the invention; 
         FIG. 4B  is a block diagram illustrating both an exemplary embodiment of an in-order fetch, decode, retire core and an exemplary register renaming, out-of-order issue/execution architecture core to be included in a processor according to embodiments of the invention; 
         FIG. 5A  is a block diagram of a single processor core, along with its connection to an on-die interconnect network; 
         FIG. 5B  illustrates an expanded view of part of the processor core in  FIG. 5A  according to embodiments of the invention; 
         FIG. 6  is a block diagram of a single core processor and a multicore processor with integrated memory controller and graphics according to embodiments of the invention; 
         FIG. 7  illustrates a block diagram of a system in accordance with one embodiment of the present invention; 
         FIG. 8  illustrates a block diagram of a second system in accordance with an embodiment of the present invention; 
         FIG. 9  illustrates a block diagram of a third system in accordance with an embodiment of the present invention; 
         FIG. 10  illustrates a block diagram of a system on a chip (SoC) in accordance with an embodiment of the present invention; 
         FIG. 11  illustrates a block diagram contrasting the use of a software instruction converter to convert binary instructions in a source instruction set to binary instructions in a target instruction set according to embodiments of the invention; 
         FIG. 12  illustrate examples of different types of code which may be used in combination with embodiments of the invention; 
         FIG. 13  illustrates one embodiment of a data parallel cluster architecture; 
         FIGS. 14A-C  illustrate different implementations for integrating a DPC with a processor; 
         FIG. 15  illustrates an example of a microthread state; 
         FIG. 16A-D  illustrate one embodiment of a DPC tile; 
         FIG. 17  illustrates an example code sequence which may be processed on one embodiment of the invention; 
         FIG. 18  illustrates an example in which different threads execute different basic blocks of code; 
         FIG. 19  illustrates reconvergence circuitry in accordance with one embodiment of the invention; 
         FIG. 20  illustrates one embodiment of an arrangement of instruction pointers; 
         FIG. 21  illustrates an example of a microarchitectural mask manipulation; 
         FIG. 22  illustrates a method in accordance with one embodiment; 
         FIG. 23  illustrates an example set of instruction fields; 
         FIG. 24  illustrates an example of an arrangement of rows and columns of a matrix and associated operations; 
         FIG. 25  illustrates operations performed on an example set of tiles; 
         FIGS. 26-28  illustrate different arrangements of processing elements; 
         FIGS. 29A-B  illustrate a processing order for different tiles; 
         FIG. 30  illustrates additional details of one embodiment of a DPC front end; 
         FIG. 31  illustrates a method for detecting and managing gang invariance within a parallel processor; 
         FIG. 32  illustrates one embodiment of coupling a host processor/core with a parallel processing engine; 
         FIG. 33  illustrates one embodiment of a method for allocating work to a parallel processing engine; 
         FIG. 34  illustrates an example parent thread spawning loop iterations which are distributed to parallel execution resources; and 
         FIG. 35  illustrates an example of parallel processing across two lanes. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention described below. It will be apparent, however, to one skilled in the art that the embodiments of the invention may be practiced without some of these specific details. In other instances, well-known structures and devices are shown in block diagram form to avoid obscuring the underlying principles of the embodiments of the invention. 
     Exemplary Processor Architectures, Instruction Formats, and Data Types 
     An instruction set includes one or more instruction formats. A given instruction format defines various fields (number of bits, location of bits) to specify, among other things, the operation to be performed (opcode) and the operand(s) on which that operation is to be performed. Some instruction formats are further broken down though the definition of instruction templates (or subformats). For example, the instruction templates of a given instruction format may be defined to have different subsets of the instruction format&#39;s fields (the included fields are typically in the same order, but at least some have different bit positions because there are less fields included) and/or defined to have a given field interpreted differently. Thus, each instruction of an ISA is expressed using a given instruction format (and, if defined, in a given one of the instruction templates of that instruction format) and includes fields for specifying the operation and the operands. For example, an exemplary ADD instruction has a specific opcode and an instruction format that includes an opcode field to specify that opcode and operand fields to select operands (source1/destination and source2); and an occurrence of this ADD instruction in an instruction stream will have specific contents in the operand fields that select specific operands. 
     Embodiments of the instruction(s) described herein may be embodied in different formats. Additionally, exemplary systems, architectures, and pipelines are detailed below. Embodiments of the instruction(s) may be executed on such systems, architectures, and pipelines, but are not limited to those detailed. 
     Generic Vector Friendly Instruction Format 
     A vector friendly instruction format is an instruction format that is suited for vector instructions (e.g., there are certain fields specific to vector operations). While embodiments are described in which both vector and scalar operations are supported through the vector friendly instruction format, alternative embodiments use only vector operations the vector friendly instruction format. 
       FIGS. 1A-1B  are block diagrams illustrating a generic vector friendly instruction format and instruction templates thereof according to embodiments of the invention.  FIG. 1A  is a block diagram illustrating a generic vector friendly instruction format and class A instruction templates thereof according to embodiments of the invention; while  FIG. 1B  is a block diagram illustrating the generic vector friendly instruction format and class B instruction templates thereof according to embodiments of the invention. Specifically, a generic vector friendly instruction format  100  for which are defined class A and class B instruction templates, both of which include no memory access  105  instruction templates and memory access  120  instruction templates. The term generic in the context of the vector friendly instruction format refers to the instruction format not being tied to any specific instruction set. 
     While embodiments of the invention will be described in which the vector friendly instruction format supports the following: a 64 byte vector operand length (or size) with 32 bit (4 byte) or 64 bit (8 byte) data element widths (or sizes) (and thus, a 64 byte vector consists of either 16 doubleword-size elements or alternatively, 8 quadword-size elements); a 64 byte vector operand length (or size) with 16 bit (2 byte) or 8 bit (1 byte) data element widths (or sizes); a 32 byte vector operand length (or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit (2 byte), or 8 bit (1 byte) data element widths (or sizes); and a 16 byte vector operand length (or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit (2 byte), or 8 bit (1 byte) data element widths (or sizes); alternative embodiments may support more, less and/or different vector operand sizes (e.g., 256 byte vector operands) with more, less, or different data element widths (e.g., 128 bit (16 byte) data element widths). 
     The class A instruction templates in  FIG. 1A  include: 1) within the no memory access  105  instruction templates there is shown a no memory access, full round control type operation  110  instruction template and a no memory access, data transform type operation  115  instruction template; and 2) within the memory access  120  instruction templates there is shown a memory access, temporal  125  instruction template and a memory access, non-temporal  130  instruction template. The class B instruction templates in  FIG. 1B  include: 1) within the no memory access  105  instruction templates there is shown a no memory access, write mask control, partial round control type operation  112  instruction template and a no memory access, write mask control, vsize type operation  117  instruction template; and 2) within the memory access  120  instruction templates there is shown a memory access, write mask control  127  instruction template. 
     The generic vector friendly instruction format  100  includes the following fields listed below in the order illustrated in  FIGS. 1A-1B . 
     Format field  140 —a specific value (an instruction format identifier value) in this field uniquely identifies the vector friendly instruction format, and thus occurrences of instructions in the vector friendly instruction format in instruction streams. As such, this field is optional in the sense that it is not needed for an instruction set that has only the generic vector friendly instruction format. 
     Base operation field  142 —its content distinguishes different base operations. Register index field  144 —its content, directly or through address generation, specifies the locations of the source and destination operands, be they in registers or in memory. These include a sufficient number of bits to select N registers from a P×Q (e.g. 32×512, 16×128, 32×1024, 64×1024) register file. While in one embodiment N may be up to three sources and one destination register, alternative embodiments may support more or less sources and destination registers (e.g., may support up to two sources where one of these sources also acts as the destination, may support up to three sources where one of these sources also acts as the destination, may support up to two sources and one destination). 
     Modifier field  146 —its content distinguishes occurrences of instructions in the generic vector instruction format that specify memory access from those that do not; that is, between no memory access  105  instruction templates and memory access  120  instruction templates. Memory access operations read and/or write to the memory hierarchy (in some cases specifying the source and/or destination addresses using values in registers), while non-memory access operations do not (e.g., the source and destinations are registers). While in one embodiment this field also selects between three different ways to perform memory address calculations, alternative embodiments may support more, less, or different ways to perform memory address calculations. 
     Augmentation operation field  150 —its content distinguishes which one of a variety of different operations to be performed in addition to the base operation. This field is context specific. In one embodiment of the invention, this field is divided into a class field  168 , an alpha field  152 , and a beta field  154 . The augmentation operation field  150  allows common groups of operations to be performed in a single instruction rather than 2, 3, or 4 instructions. 
     Scale field  160 —its content allows for the scaling of the index field&#39;s content for memory address generation (e.g., for address generation that uses 2 scale *index+base). 
     Displacement Field  162 A—its content is used as part of memory address generation (e.g., for address generation that uses 2 scale *index+base+displacement). 
     Displacement Factor Field  162 B (note that the juxtaposition of displacement field  162 A directly over displacement factor field  162 B indicates one or the other is used)—its content is used as part of address generation; it specifies a displacement factor that is to be scaled by the size of a memory access (N)—where N is the number of bytes in the memory access (e.g., for address generation that uses 2 scale *index+base+scaled displacement). Redundant low-order bits are ignored and hence, the displacement factor field&#39;s content is multiplied by the memory operands total size (N) in order to generate the final displacement to be used in calculating an effective address. The value of N is determined by the processor hardware at runtime based on the full opcode field  174  (described later herein) and the data manipulation field  154 C. The displacement field  162 A and the displacement factor field  162 B are optional in the sense that they are not used for the no memory access  105  instruction templates and/or different embodiments may implement only one or none of the two. 
     Data element width field  164 —its content distinguishes which one of a number of data element widths is to be used (in some embodiments for all instructions; in other embodiments for only some of the instructions). This field is optional in the sense that it is not needed if only one data element width is supported and/or data element widths are supported using some aspect of the opcodes. 
     Write mask field  170 —its content controls, on a per data element position basis, whether that data element position in the destination vector operand reflects the result of the base operation and augmentation operation. Class A instruction templates support merging-writemasking, while class B instruction templates support both merging- and zeroing-writemasking. When merging, vector masks allow any set of elements in the destination to be protected from updates during the execution of any operation (specified by the base operation and the augmentation operation); in other one embodiment, preserving the old value of each element of the destination where the corresponding mask bit has a 0. In contrast, when zeroing vector masks allow any set of elements in the destination to be zeroed during the execution of any operation (specified by the base operation and the augmentation operation); in one embodiment, an element of the destination is set to 0 when the corresponding mask bit has a 0 value. A subset of this functionality is the ability to control the vector length of the operation being performed (that is, the span of elements being modified, from the first to the last one); however, it is not necessary that the elements that are modified be consecutive. Thus, the write mask field  170  allows for partial vector operations, including loads, stores, arithmetic, logical, etc. While embodiments of the invention are described in which the write mask field&#39;s  170  content selects one of a number of write mask registers that contains the write mask to be used (and thus the write mask field&#39;s  170  content indirectly identifies that masking to be performed), alternative embodiments instead or additional allow the mask write field&#39;s  170  content to directly specify the masking to be performed. 
     Immediate field  172 —its content allows for the specification of an immediate. This field is optional in the sense that is it not present in an implementation of the generic vector friendly format that does not support immediate and it is not present in instructions that do not use an immediate. 
     Class field  168 —its content distinguishes between different classes of instructions. With reference to  FIGS. 1A-B , the contents of this field select between class A and class B instructions. In  FIGS. 1A-B , rounded corner squares are used to indicate a specific value is present in a field (e.g., class A  168 A and class B  168 B for the class field  168  respectively in  FIGS. 1A-B ). 
     Instruction Templates of Class A 
     In the case of the non-memory access  105  instruction templates of class A, the alpha field  152  is interpreted as an RS field  152 A, whose content distinguishes which one of the different augmentation operation types are to be performed (e.g., round  152 A. 1  and data transform  152 A. 2  are respectively specified for the no memory access, round type operation  110  and the no memory access, data transform type operation  115  instruction templates), while the beta field  154  distinguishes which of the operations of the specified type is to be performed. In the no memory access  105  instruction templates, the scale field  160 , the displacement field  162 A, and the displacement scale filed  162 B are not present. 
     No-Memory Access Instruction Templates—Full Round Control Type Operation 
     In the no memory access full round control type operation  110  instruction template, the beta field  154  is interpreted as a round control field  154 A, whose content(s) provide static rounding. While in the described embodiments of the invention the round control field  154 A includes a suppress all floating point exceptions (SAE) field  156  and a round operation control field  158 , alternative embodiments may support may encode both these concepts into the same field or only have one or the other of these concepts/fields (e.g., may have only the round operation control field  158 ). 
     SAE field  156 —its content distinguishes whether or not to disable the exception event reporting; when the SAE field&#39;s  156  content indicates suppression is enabled, a given instruction does not report any kind of floating-point exception flag and does not raise any floating point exception handler. 
     Round operation control field  158 —its content distinguishes which one of a group of rounding operations to perform (e.g., Round-up, Round-down, Round-towards-zero and Round-to-nearest). Thus, the round operation control field  158  allows for the changing of the rounding mode on a per instruction basis. In one embodiment of the invention where a processor includes a control register for specifying rounding modes, the round operation control field&#39;s  150  content overrides that register value. 
     No Memory Access Instruction Templates—Data Transform Type Operation 
     In the no memory access data transform type operation  115  instruction template, the beta field  154  is interpreted as a data transform field  154 B, whose content distinguishes which one of a number of data transforms is to be performed (e.g., no data transform, swizzle, broadcast). 
     In the case of a memory access  120  instruction template of class A, the alpha field  152  is interpreted as an eviction hint field  152 B, whose content distinguishes which one of the eviction hints is to be used (in  FIG. 1A , temporal  152 B. 1  and non-temporal  152 B. 2  are respectively specified for the memory access, temporal  125  instruction template and the memory access, non-temporal  130  instruction template), while the beta field  154  is interpreted as a data manipulation field  154 C, whose content distinguishes which one of a number of data manipulation operations (also known as primitives) is to be performed (e.g., no manipulation; broadcast; up conversion of a source; and down conversion of a destination). The memory access  120  instruction templates include the scale field  160 , and optionally the displacement field  162 A or the displacement scale field  162 B. 
     Vector memory instructions perform vector loads from and vector stores to memory, with conversion support. As with regular vector instructions, vector memory instructions transfer data from/to memory in a data element-wise fashion, with the elements that are actually transferred is dictated by the contents of the vector mask that is selected as the write mask. 
     Memory Access Instruction Templates—Temporal 
     Temporal data is data likely to be reused soon enough to benefit from caching. This is, however, a hint, and different processors may implement it in different ways, including ignoring the hint entirely. 
     Memory Access Instruction Templates—Non-Temporal 
     Non-temporal data is data unlikely to be reused soon enough to benefit from caching in the 1st-level cache and should be given priority for eviction. This is, however, a hint, and different processors may implement it in different ways, including ignoring the hint entirely. 
     Instruction Templates of Class B 
     In the case of the instruction templates of class B, the alpha field  152  is interpreted as a write mask control (Z) field  152 C, whose content distinguishes whether the write masking controlled by the write mask field  170  should be a merging or a zeroing. 
     In the case of the non-memory access  105  instruction templates of class B, part of the beta field  154  is interpreted as an RL field  157 A, whose content distinguishes which one of the different augmentation operation types are to be performed (e.g., round  157 A. 1  and vector length (VSIZE)  157 A. 2  are respectively specified for the no memory access, write mask control, partial round control type operation  112  instruction template and the no memory access, write mask control, VSIZE type operation  117  instruction template), while the rest of the beta field  154  distinguishes which of the operations of the specified type is to be performed. In the no memory access  105  instruction templates, the scale field  160 , the displacement field  162 A, and the displacement scale filed  162 B are not present. 
     In the no memory access, write mask control, partial round control type operation  110  instruction template, the rest of the beta field  154  is interpreted as a round operation field  159 A and exception event reporting is disabled (a given instruction does not report any kind of floating-point exception flag and does not raise any floating point exception handler). 
     Round operation control field  159 A—just as round operation control field  158 , its content distinguishes which one of a group of rounding operations to perform (e.g., Round-up, Round-down, Round-towards-zero and Round-to-nearest). Thus, the round operation control field  159 A allows for the changing of the rounding mode on a per instruction basis. In one embodiment of the invention where a processor includes a control register for specifying rounding modes, the round operation control field&#39;s  150  content overrides that register value. 
     In the no memory access, write mask control, VSIZE type operation  117  instruction template, the rest of the beta field  154  is interpreted as a vector length field  159 B, whose content distinguishes which one of a number of data vector lengths is to be performed on (e.g., 128, 256, or 512 byte). 
     In the case of a memory access  120  instruction template of class B, part of the beta field  154  is interpreted as a broadcast field  157 B, whose content distinguishes whether or not the broadcast type data manipulation operation is to be performed, while the rest of the beta field  154  is interpreted the vector length field  159 B. The memory access  120  instruction templates include the scale field  160 , and optionally the displacement field  162 A or the displacement scale field  162 B. 
     With regard to the generic vector friendly instruction format  100 , a full opcode field  174  is shown including the format field  140 , the base operation field  142 , and the data element width field  164 . While one embodiment is shown where the full opcode field  174  includes all of these fields, the full opcode field  174  includes less than all of these fields in embodiments that do not support all of them. The full opcode field  174  provides the operation code (opcode). 
     The augmentation operation field  150 , the data element width field  164 , and the write mask field  170  allow these features to be specified on a per instruction basis in the generic vector friendly instruction format. 
     The combination of write mask field and data element width field create typed instructions in that they allow the mask to be applied based on different data element widths. 
     The various instruction templates found within class A and class B are beneficial in different situations. In some embodiments of the invention, different processors or different cores within a processor may support only class A, only class B, or both classes. For instance, a high performance general purpose out-of-order core intended for general-purpose computing may support only class B, a core intended primarily for graphics and/or scientific (throughput) computing may support only class A, and a core intended for both may support both (of course, a core that has some mix of templates and instructions from both classes but not all templates and instructions from both classes is within the purview of the invention). Also, a single processor may include multiple cores, all of which support the same class or in which different cores support different class. For instance, in a processor with separate graphics and general purpose cores, one of the graphics cores intended primarily for graphics and/or scientific computing may support only class A, while one or more of the general purpose cores may be high performance general purpose cores with out of order execution and register renaming intended for general-purpose computing that support only class B. Another processor that does not have a separate graphics core, may include one more general purpose in-order or out-of-order cores that support both class A and class B. Of course, features from one class may also be implement in the other class in different embodiments of the invention. Programs written in a high level language would be put (e.g., just in time compiled or statically compiled) into an variety of different executable forms, including: 1) a form having only instructions of the class(es) supported by the target processor for execution; or 2) a form having alternative routines written using different combinations of the instructions of all classes and having control flow code that selects the routines to execute based on the instructions supported by the processor which is currently executing the code. 
     VEX Instruction Format 
     VEX encoding allows instructions to have more than two operands, and allows SIMD vector registers to be longer than 28 bits. The use of a VEX prefix provides for three-operand (or more) syntax. For example, previous two-operand instructions performed operations such as A=A+B, which overwrites a source operand. The use of a VEX prefix enables operands to perform nondestructive operations such as A=B+C. 
       FIG. 2A  illustrates an exemplary AVX instruction format including a VEX prefix  202 , real opcode field  230 , Mod R/M byte  240 , SIB byte  250 , displacement field  262 , and IMM8  272 .  FIG. 2B  illustrates which fields from  FIG. 2A  make up a full opcode field  274  and a base operation field  241 .  FIG. 2C  illustrates which fields from  FIG. 2A  make up a register index field  244 . 
     VEX Prefix (Bytes  0 - 2 )  202  is encoded in a three-byte form. The first byte is the Format Field  290  (VEX Byte  0 , bits [ 7 : 0 ]), which contains an explicit C4 byte value (the unique value used for distinguishing the C4 instruction format). The second-third bytes (VEX Bytes  1 - 2 ) include a number of bit fields providing specific capability. Specifically, REX field  205  (VEX Byte  1 , bits [ 7 - 5 ]) consists of a VEX.R bit field (VEX Byte  1 , bit [ 7 ]-R), VEX.X bit field (VEX byte  1 , bit [ 6 ]-X), and VEX.B bit field (VEX byte  1 , bit[ 5 ]-B). Other fields of the instructions encode the lower three bits of the register indexes as is known in the art (rrr, xxx, and bbb), so that Rrrr, Xxxx, and Bbbb may be formed by adding VEX.R, VEX.X, and VEX.B. Opcode map field  215  (VEX byte  1 , bits [ 4 : 0 ]-mmmmm) includes content to encode an implied leading opcode byte. W Field  264  (VEX byte  2 , bit [ 7 ]-W)—is represented by the notation VEX.W, and provides different functions depending on the instruction. The role of VEX.vvvv  220  (VEX Byte  2 , bits [ 6 : 3 ]-vvvv) may include the following: 1) VEX.vvvv encodes the first source register operand, specified in inverted (1s complement) form and is valid for instructions with 2 or more source operands; 2) VEX.vvvv encodes the destination register operand, specified in is complement form for certain vector shifts; or 3) VEX.vvvv does not encode any operand, the field is reserved and should contain 1111b. If VEX.L  268  Size field (VEX byte  2 , bit [ 2 ]-L)=0, it indicates 28 bit vector; if VEX.L=1, it indicates 256 bit vector. Prefix encoding field  225  (VEX byte  2 , bits [ 1 : 0 ]-pp) provides additional bits for the base operation field  241 . 
     Real Opcode Field  230  (Byte  3 ) is also known as the opcode byte. Part of the opcode is specified in this field. 
     MOD R/M Field  240  (Byte  4 ) includes MOD field  242  (bits [ 7 - 6 ]), Reg field  244  (bits [ 5 - 3 ]), and R/M field  246  (bits [ 2 - 0 ]). The role of Reg field  244  may include the following: encoding either the destination register operand or a source register operand (the rrr of Rrrr), or be treated as an opcode extension and not used to encode any instruction operand. The role of R/M field  246  may include the following: encoding the instruction operand that references a memory address, or encoding either the destination register operand or a source register operand. 
     Scale, Index, Base (SIB)—The content of Scale field  250  (Byte  5 ) includes SS252 (bits [ 7 - 6 ]), which is used for memory address generation. The contents of SIB.xxx  254  (bits [ 5 - 3 ]) and SIB.bbb  256  (bits [ 2 - 0 ]) have been previously referred to with regard to the register indexes Xxxx and Bbbb. 
     The Displacement Field  262  and the immediate field (IMM8)  272  contain data. 
     Exemplary Register Architecture 
       FIG. 3  is a block diagram of a register architecture  300  according to one embodiment of the invention. In the embodiment illustrated, there are 32 vector registers  310  that are 512 bits wide; these registers are referenced as zmm0 through zmm31. The lower order 256 bits of the lower 6 zmm registers are overlaid on registers ymm0-15. The lower order 128 bits of the lower 6 zmm registers (the lower order 128 bits of the ymm registers) are overlaid on registers xmm0-15. 
     General-purpose registers  325 —in the embodiment illustrated, there are sixteen 64-bit general-purpose registers that are used along with the existing x86 addressing modes to address memory operands. These registers are referenced by the names RAX, RBX, RCX, RDX, RBP, RSI, RDI, RSP, and R 8  through R 15 . 
     Scalar floating point stack register file (x87 stack)  345 , on which is aliased the MMX packed integer flat register file  350 —in the embodiment illustrated, the x87 stack is an eight-element stack used to perform scalar floating-point operations on 32/64/80-bit floating point data using the x87 instruction set extension; while the MMX registers are used to perform operations on 64-bit packed integer data, as well as to hold operands for some operations performed between the MMX and XMM registers. 
     Alternative embodiments of the invention may use wider or narrower registers. Additionally, alternative embodiments of the invention may use more, less, or different register files and registers. 
     Exemplary Core Architectures, Processors, and Computer Architectures 
     Processor cores may be implemented in different ways, for different purposes, and in different processors. For instance, implementations of such cores may include: 1) a general purpose in-order core intended for general-purpose computing; 2) a high performance general purpose out-of-order core intended for general-purpose computing; 3) a special purpose core intended primarily for graphics and/or scientific (throughput) computing. Implementations of different processors may include: 1) a CPU including one or more general purpose in-order cores intended for general-purpose computing and/or one or more general purpose out-of-order cores intended for general-purpose computing; and 2) a coprocessor including one or more special purpose cores intended primarily for graphics and/or scientific (throughput). Such different processors lead to different computer system architectures, which may include: 1) the coprocessor on a separate chip from the CPU; 2) the coprocessor on a separate die in the same package as a CPU; 3) the coprocessor on the same die as a CPU (in which case, such a coprocessor is sometimes referred to as special purpose logic, such as integrated graphics and/or scientific (throughput) logic, or as special purpose cores); and 4) a system on a chip that may include on the same die the described CPU (sometimes referred to as the application core(s) or application processor(s)), the above described coprocessor, and additional functionality. Exemplary core architectures are described next, followed by descriptions of exemplary processors and computer architectures. Detailed herein are circuits (units) that comprise exemplary cores, processors, etc. 
     Exemplary Core Architectures 
       FIG. 4A  is a block diagram illustrating both an exemplary in-order pipeline and an exemplary register renaming, out-of-order issue/execution pipeline according to embodiments of the invention.  FIG. 4B  is a block diagram illustrating both an exemplary embodiment of an in-order architecture core and an exemplary register renaming, out-of-order issue/execution architecture core to be included in a processor according to embodiments of the invention. The solid lined boxes in  FIGS. 4A-B  illustrate the in-order pipeline and in-order core, while the optional addition of the dashed lined boxes illustrates the register renaming, out-of-order issue/execution pipeline and core. Given that the in-order aspect is a subset of the out-of-order aspect, the out-of-order aspect will be described. 
     In  FIG. 4A , a processor pipeline  400  includes a fetch stage  402 , a length decode stage  404 , a decode stage  406 , an allocation stage  408 , a renaming stage  410 , a scheduling (also known as a dispatch or issue) stage  412 , a register read/memory read stage  414 , an execute stage  416 , a write back/memory write stage  418 , an exception handling stage  422 , and a commit stage  424 . 
       FIG. 4B  shows processor core  490  including a front end unit  430  coupled to an execution engine unit  450 , and both are coupled to a memory unit  470 . The core  490  may be a reduced instruction set computing (RISC) core, a complex instruction set computing (CISC) core, a very long instruction word (VLIW) core, or a hybrid or alternative core type. As yet another option, the core  490  may be a special-purpose core, such as, for example, a network or communication core, compression engine, coprocessor core, general purpose computing graphics processing unit (GPGPU) core, graphics core, or the like. 
     The front end unit  430  includes a branch prediction unit  432  coupled to an instruction cache unit  434 , which is coupled to an instruction translation lookaside buffer (TLB)  436 , which is coupled to an instruction fetch unit  438 , which is coupled to a decode unit  440 . The decode unit  440  (or decoder) may decode instructions, and generate as an output one or more micro-operations, micro-code entry points, microinstructions, other instructions, or other control signals, which are decoded from, or which otherwise reflect, or are derived from, the original instructions. The decode unit  440  may be implemented using various different mechanisms. Examples of suitable mechanisms include, but are not limited to, look-up tables, hardware implementations, programmable logic arrays (PLAs), microcode read only memories (ROMs), etc. In one embodiment, the core  490  includes a microcode ROM or other medium that stores microcode for certain macroinstructions (e.g., in decode unit  440  or otherwise within the front end unit  430 ). The decode unit  440  is coupled to a rename/allocator unit  452  in the execution engine unit  450 . 
     The execution engine unit  450  includes the rename/allocator unit  452  coupled to a retirement unit  454  and a set of one or more scheduler unit(s)  456 . The scheduler unit(s)  456  represents any number of different schedulers, including reservations stations, central instruction window, etc. The scheduler unit(s)  456  is coupled to the physical register file(s) unit(s)  458 . Each of the physical register file(s) units  458  represents one or more physical register files, different ones of which store one or more different data types, such as scalar integer, scalar floating point, packed integer, packed floating point, vector integer, vector floating point, status (e.g., an instruction pointer that is the address of the next instruction to be executed), etc. In one embodiment, the physical register file(s) unit  458  comprises a vector registers unit and a scalar registers unit. These register units may provide architectural vector registers, vector mask registers, and general purpose registers. The physical register file(s) unit(s)  458  is overlapped by the retirement unit  454  to illustrate various ways in which register renaming and out-of-order execution may be implemented (e.g., using a reorder buffer(s) and a retirement register file(s); using a future file(s), a history buffer(s), and a retirement register file(s); using a register maps and a pool of registers; etc.). The retirement unit  454  and the physical register file(s) unit(s)  458  are coupled to the execution cluster(s)  460 . The execution cluster(s)  460  includes a set of one or more execution units  462  and a set of one or more memory access units  464 . The execution units  462  may perform various operations (e.g., shifts, addition, subtraction, multiplication) and on various types of data (e.g., scalar floating point, packed integer, packed floating point, vector integer, vector floating point). While some embodiments may include a number of execution units dedicated to specific functions or sets of functions, other embodiments may include only one execution unit or multiple execution units that all perform all functions. The scheduler unit(s)  456 , physical register file(s) unit(s)  458 , and execution cluster(s)  460  are shown as being possibly plural because certain embodiments create separate pipelines for certain types of data/operations (e.g., a scalar integer pipeline, a scalar floating point/packed integer/packed floating point/vector integer/vector floating point pipeline, and/or a memory access pipeline that each have their own scheduler unit, physical register file(s) unit, and/or execution cluster—and in the case of a separate memory access pipeline, certain embodiments are implemented in which only the execution cluster of this pipeline has the memory access unit(s)  464 ). It should also be understood that where separate pipelines are used, one or more of these pipelines may be out-of-order issue/execution and the rest in-order. 
     The set of memory access units  464  is coupled to the memory unit  470 , which includes a data TLB unit  472  coupled to a data cache unit  474  coupled to a level 2 (L2) cache unit  476 . In one exemplary embodiment, the memory access units  464  may include a load unit, a store address unit, and a store data unit, each of which is coupled to the data TLB unit  472  in the memory unit  470 . The instruction cache unit  434  is further coupled to a level 2 (L2) cache unit  476  in the memory unit  470 . The L2 cache unit  476  is coupled to one or more other levels of cache and eventually to a main memory. 
     By way of example, the exemplary register renaming, out-of-order issue/execution core architecture may implement the pipeline  400  as follows: 1) the instruction fetch  438  performs the fetch and length decoding stages  402  and  404 ; 2) the decode unit  440  performs the decode stage  406 ; 3) the rename/allocator unit  452  performs the allocation stage  408  and renaming stage  410 ; 4) the scheduler unit(s)  456  performs the schedule stage  412 ; 5) the physical register file(s) unit(s)  458  and the memory unit  470  perform the register read/memory read stage  414 ; the execution cluster  460  perform the execute stage  416 ; 6) the memory unit  470  and the physical register file(s) unit(s)  458  perform the write back/memory write stage  418 ; 7) various units may be involved in the exception handling stage  422 ; and 8) the retirement unit  454  and the physical register file(s) unit(s)  458  perform the commit stage  424 . 
     The core  490  may support one or more instructions sets (e.g., the x86 instruction set (with some extensions that have been added with newer versions); the MIPS instruction set of MIPS Technologies of Sunnyvale, Calif.; the ARM instruction set (with optional additional extensions such as NEON) of ARM Holdings of Sunnyvale, Calif.), including the instruction(s) described herein. In one embodiment, the core  490  includes logic to support a packed data instruction set extension (e.g., AVX1, AVX2), thereby allowing the operations used by many multimedia applications to be performed using packed data. 
     It should be understood that the core may support multithreading (executing two or more parallel sets of operations or threads), and may do so in a variety of ways including time sliced multithreading, simultaneous multithreading (where a single physical core provides a logical core for each of the threads that physical core is simultaneously multithreading), or a combination thereof (e.g., time sliced fetching and decoding and simultaneous multithreading thereafter such as in the Intel® Hyperthreading technology). 
     While register renaming is described in the context of out-of-order execution, it should be understood that register renaming may be used in an in-order architecture. While the illustrated embodiment of the processor also includes separate instruction and data cache units  434 / 474  and a shared L2 cache unit  476 , alternative embodiments may have a single internal cache for both instructions and data, such as, for example, a Level 1 (L1) internal cache, or multiple levels of internal cache. In some embodiments, the system may include a combination of an internal cache and an external cache that is external to the core and/or the processor. Alternatively, all of the cache may be external to the core and/or the processor. 
     Specific Exemplary In-Order Core Architecture 
       FIGS. 5A-B  illustrate a block diagram of a more specific exemplary in-order core architecture, which core would be one of several logic blocks (including other cores of the same type and/or different types) in a chip. The logic blocks communicate through a high-bandwidth interconnect network (e.g., a ring network) with some fixed function logic, memory I/O interfaces, and other necessary I/O logic, depending on the application. 
       FIG. 5A  is a block diagram of a single processor core, along with its connection to the on-die interconnect network  502  and with its local subset of the Level 2 (L2) cache  504 , according to embodiments of the invention. In one embodiment, an instruction decoder  500  supports the x86 instruction set with a packed data instruction set extension. An L1 cache  506  allows low-latency accesses to cache memory into the scalar and vector units. While in one embodiment (to simplify the design), a scalar unit  508  and a vector unit  510  use separate register sets (respectively, scalar registers  512  and vector registers  514 ) and data transferred between them is written to memory and then read back in from a level 1 (L1) cache  506 , alternative embodiments of the invention may use a different approach (e.g., use a single register set or include a communication path that allow data to be transferred between the two register files without being written and read back). 
     The local subset of the L2 cache  504  is part of a global L2 cache that is divided into separate local subsets, one per processor core. Each processor core has a direct access path to its own local subset of the L2 cache  504 . Data read by a processor core is stored in its L2 cache subset  504  and can be accessed quickly, in parallel with other processor cores accessing their own local L2 cache subsets. Data written by a processor core is stored in its own L2 cache subset  504  and is flushed from other subsets, if necessary. The ring network ensures coherency for shared data. The ring network is bi-directional to allow agents such as processor cores, L2 caches and other logic blocks to communicate with each other within the chip. Each ring data-path is 1024-bits wide per direction in some embodiments. 
       FIG. 5B  is an expanded view of part of the processor core in  FIG. 5A  according to embodiments of the invention.  FIG. 5B  includes an L1 data cache  506 A part of the L1 cache  504 , as well as more detail regarding the vector unit  510  and the vector registers  514 . Specifically, the vector unit  510  is a 6-wide vector processing unit (VPU) (see the 16-wide ALU  528 ), which executes one or more of integer, single-precision float, and double-precision float instructions. The VPU supports swizzling the register inputs with swizzle unit  520 , numeric conversion with numeric convert units  522 A-B, and replication with replication unit  524  on the memory input. 
     Processor with integrated memory controller and graphics 
       FIG. 6  is a block diagram of a processor  600  that may have more than one core, may have an integrated memory controller, and may have integrated graphics according to embodiments of the invention. The solid lined boxes in  FIG. 6  illustrate a processor  600  with a single core  602 A, a system agent  610 , a set of one or more bus controller units  616 , while the optional addition of the dashed lined boxes illustrates an alternative processor  600  with multiple cores  602 A-N, a set of one or more integrated memory controller unit(s)  614  in the system agent unit  610 , and special purpose logic  608 . 
     Thus, different implementations of the processor  600  may include: 1) a CPU with the special purpose logic  608  being integrated graphics and/or scientific (throughput) logic (which may include one or more cores), and the cores  602 A-N being one or more general purpose cores (e.g., general purpose in-order cores, general purpose out-of-order cores, a combination of the two); 2) a coprocessor with the cores  602 A-N being a large number of special purpose cores intended primarily for graphics and/or scientific (throughput); and 3) a coprocessor with the cores  602 A-N being a large number of general purpose in-order cores. Thus, the processor  600  may be a general-purpose processor, coprocessor or special-purpose processor, such as, for example, a network or communication processor, compression engine, graphics processor, GPGPU (general purpose graphics processing unit), a high-throughput many integrated core (MIC) coprocessor (including 30 or more cores), embedded processor, or the like. The processor may be implemented on one or more chips. The processor  600  may be a part of and/or may be implemented on one or more substrates using any of a number of process technologies, such as, for example, BiCMOS, CMOS, or NMOS. 
     The memory hierarchy includes one or more levels of cache within the cores  604 A-N, a set or one or more shared cache units  606 , and external memory (not shown) coupled to the set of integrated memory controller units  614 . The set of shared cache units  606  may include one or more mid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), or other levels of cache, a last level cache (LLC), and/or combinations thereof. While in one embodiment a ring based interconnect unit  612  interconnects the integrated graphics logic  608 , the set of shared cache units  606 , and the system agent unit  610 /integrated memory controller unit(s)  614 , alternative embodiments may use any number of well-known techniques for interconnecting such units. In one embodiment, coherency is maintained between one or more cache units  606  and cores  602 -A-N. 
     In some embodiments, one or more of the cores  602 A-N are capable of multi-threading. The system agent  610  includes those components coordinating and operating cores  602 A-N. The system agent unit  610  may include for example a power control unit (PCU) and a display unit. The PCU may be or include logic and components needed for regulating the power state of the cores  602 A-N and the integrated graphics logic  608 . The display unit is for driving one or more externally connected displays. 
     The cores  602 A-N may be homogenous or heterogeneous in terms of architecture instruction set; that is, two or more of the cores  602 A-N may be capable of execution the same instruction set, while others may be capable of executing only a subset of that instruction set or a different instruction set. 
     Exemplary Computer Architectures 
       FIGS. 7-10  are block diagrams of exemplary computer architectures. Other system designs and configurations known in the arts for laptops, desktops, handheld PCs, personal digital assistants, engineering workstations, servers, network devices, network hubs, switches, embedded processors, digital signal processors (DSPs), graphics devices, video game devices, set-top boxes, micro controllers, cell phones, portable media players, hand held devices, and various other electronic devices, are also suitable. In general, a huge variety of systems or electronic devices capable of incorporating a processor and/or other execution logic as disclosed herein are generally suitable. 
     Referring now to  FIG. 7 , shown is a block diagram of a system  700  in accordance with one embodiment of the present invention. The system  700  may include one or more processors  710 ,  715 , which are coupled to a controller hub  720 . In one embodiment, the controller hub  720  includes a graphics memory controller hub (GMCH)  790  and an Input/Output Hub (IOH)  750  (which may be on separate chips); the GMCH  790  includes memory and graphics controllers to which are coupled memory  740  and a coprocessor  745 ; the IOH  750  is couples input/output (I/O) devices  760  to the GMCH  790 . Alternatively, one or both of the memory and graphics controllers are integrated within the processor (as described herein), the memory  740  and the coprocessor  745  are coupled directly to the processor  710 , and the controller hub  720  in a single chip with the IOH  750 . 
     The optional nature of additional processors  715  is denoted in  FIG. 7  with broken lines. Each processor  710 ,  715  may include one or more of the processing cores described herein and may be some version of the processor  600 . 
     The memory  740  may be, for example, dynamic random access memory (DRAM), phase change memory (PCM), or a combination of the two. For at least one embodiment, the controller hub  720  communicates with the processor(s)  710 ,  715  via a multi-drop bus, such as a frontside bus (FSB), point-to-point interface, or similar connection  795 . 
     In one embodiment, the coprocessor  745  is a special-purpose processor, such as, for example, a high-throughput MIC processor, a network or communication processor, compression engine, graphics processor, GPGPU, embedded processor, or the like. In one embodiment, controller hub  720  may include an integrated graphics accelerator. 
     There can be a variety of differences between the physical resources  710 ,  7155  in terms of a spectrum of metrics of merit including architectural, microarchitectural, thermal, power consumption characteristics, and the like. 
     In one embodiment, the processor  710  executes instructions that control data processing operations of a general type. Embedded within the instructions may be coprocessor instructions. The processor  710  recognizes these coprocessor instructions as being of a type that should be executed by the attached coprocessor  745 . Accordingly, the processor  710  issues these coprocessor instructions (or control signals representing coprocessor instructions) on a coprocessor bus or other interconnect, to coprocessor  745 . Coprocessor(s)  745  accept and execute the received coprocessor instructions. 
     Referring now to  FIG. 8 , shown is a block diagram of a first more specific exemplary system  800  in accordance with an embodiment of the present invention. As shown in  FIG. 8 , multiprocessor system  800  is a point-to-point interconnect system, and includes a first processor  870  and a second processor  880  coupled via a point-to-point interconnect  850 . Each of processors  870  and  880  may be some version of the processor  600 . In one embodiment of the invention, processors  870  and  880  are respectively processors  710  and  715 , while coprocessor  838  is coprocessor  745 . In another embodiment, processors  870  and  880  are respectively processor  710  coprocessor  745 . 
     Processors  870  and  880  are shown including integrated memory controller (IMC) units  872  and  882 , respectively. Processor  870  also includes as part of its bus controller units point-to-point (P-P) interfaces  876  and  878 ; similarly, second processor  880  includes P-P interfaces  886  and  888 . Processors  870 ,  880  may exchange information via a point-to-point (P-P) interface  850  using P-P interface circuits  878 ,  888 . As shown in  FIG. 8 , IMCs  872  and  882  couple the processors to respective memories, namely a memory  832  and a memory  834 , which may be portions of main memory locally attached to the respective processors. 
     Processors  870 ,  880  may each exchange information with a chipset  890  via individual P-P interfaces  852 ,  854  using point to point interface circuits  876 ,  894 ,  886 ,  898 . Chipset  890  may optionally exchange information with the coprocessor  838  via a high-performance interface  892 . In one embodiment, the coprocessor  838  is a special-purpose processor, such as, for example, a high-throughput MIC processor, a network or communication processor, compression engine, graphics processor, GPGPU, embedded processor, or the like. 
     A shared cache (not shown) may be included in either processor or outside of both processors, yet connected with the processors via P-P interconnect, such that either or both processors&#39; local cache information may be stored in the shared cache if a processor is placed into a low power mode. 
     Chipset  890  may be coupled to a first bus  816  via an interface  896 . In one embodiment, first bus  816  may be a Peripheral Component Interconnect (PCI) bus, or a bus such as a PCI Express bus or another I/O interconnect bus, although the scope of the present invention is not so limited. 
     As shown in  FIG. 8 , various I/O devices  814  may be coupled to first bus  816 , along with a bus bridge  818  which couples first bus  816  to a second bus  820 . In one embodiment, one or more additional processor(s)  815 , such as coprocessors, high-throughput MIC processors, GPGPU&#39;s, accelerators (such as, e.g., graphics accelerators or digital signal processing (DSP) units), field programmable gate arrays, or any other processor, are coupled to first bus  816 . In one embodiment, second bus  820  may be a low pin count (LPC) bus. Various devices may be coupled to a second bus  820  including, for example, a keyboard and/or mouse  822 , communication devices  827  and a storage unit  828  such as a disk drive or other mass storage device which may include instructions/code and data  830 , in one embodiment. Further, an audio I/O  824  may be coupled to the second bus  816 . Note that other architectures are possible. For example, instead of the point-to-point architecture of  FIG. 8 , a system may implement a multi-drop bus or other such architecture. 
     Referring now to  FIG. 9 , shown is a block diagram of a second more specific exemplary system  900  in accordance with an embodiment of the present invention. Like elements in  FIGS. 8 and 9  bear like reference numerals, and certain aspects of  FIG. 8  have been omitted from  FIG. 9  in order to avoid obscuring other aspects of  FIG. 9 . 
       FIG. 9  illustrates that the processors  870 ,  880  may include integrated memory and I/O control logic (“CL”)  972  and  982 , respectively. Thus, the CL  972 ,  982  include integrated memory controller units and include I/O control logic.  FIG. 9  illustrates that not only are the memories  832 ,  834  coupled to the CL  872 ,  882 , but also that I/O devices  914  are also coupled to the control logic  872 ,  882 . Legacy I/O devices  915  are coupled to the chipset  890 . 
     Referring now to  FIG. 10 , shown is a block diagram of a SoC  1000  in accordance with an embodiment of the present invention. Similar elements in  FIG. 6  bear like reference numerals. Also, dashed lined boxes are optional features on more advanced SoCs. In  FIG. 10 , an interconnect unit(s)  1002  is coupled to: an application processor  1010  which includes a set of one or more cores  102 A-N, cache units  604 A-N, and shared cache unit(s)  606 ; a system agent unit  610 ; a bus controller unit(s)  616 ; an integrated memory controller unit(s)  614 ; a set or one or more coprocessors  1020  which may include integrated graphics logic, an image processor, an audio processor, and a video processor; an static random access memory (SRAM) unit  1030 ; a direct memory access (DMA) unit  1032 ; and a display unit  1040  for coupling to one or more external displays. In one embodiment, the coprocessor(s)  1020  include a special-purpose processor, such as, for example, a network or communication processor, compression engine, GPGPU, a high-throughput MIC processor, embedded processor, or the like. 
     Embodiments of the mechanisms disclosed herein may be implemented in hardware, software, firmware, or a combination of such implementation approaches. Embodiments of the invention may be implemented as computer programs or program code executing on programmable systems comprising at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. 
     Program code, such as code  830  illustrated in  FIG. 8 , may be applied to input instructions to perform the functions described herein and generate output information. The output information may be applied to one or more output devices, in known fashion. For purposes of this application, a processing system includes any system that has a processor, such as, for example; a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor. 
     The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. The program code may also be implemented in assembly or machine language, if desired. In fact, the mechanisms described herein are not limited in scope to any particular programming language. In any case, the language may be a compiled or interpreted language. 
     One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that actually make the logic or processor. 
     Such machine-readable storage media may include, without limitation, non-transitory, tangible arrangements of articles manufactured or formed by a machine or device, including storage media such as hard disks, any other type of disk including floppy disks, optical disks, compact disk read-only memories (CD-ROMs), compact disk rewritable&#39;s (CD-RWs), and magneto-optical disks, semiconductor devices such as read-only memories (ROMs), random access memories (RAMs) such as dynamic random access memories (DRAMs), static random access memories (SRAMs), erasable programmable read-only memories (EPROMs), flash memories, electrically erasable programmable read-only memories (EEPROMs), phase change memory (PCM), magnetic or optical cards, or any other type of media suitable for storing electronic instructions. 
     Accordingly, embodiments of the invention also include non-transitory, tangible machine-readable media containing instructions or containing design data, such as Hardware Description Language (HDL), which defines structures, circuits, apparatuses, processors and/or system features described herein. Such embodiments may also be referred to as program products. 
     Emulation (including Binary Translation, Code Morphing, etc.) 
     In some cases, an instruction converter may be used to convert an instruction from a source instruction set to a target instruction set. For example, the instruction converter may translate (e.g., using static binary translation, dynamic binary translation including dynamic compilation), morph, emulate, or otherwise convert an instruction to one or more other instructions to be processed by the core. The instruction converter may be implemented in software, hardware, firmware, or a combination thereof. The instruction converter may be on processor, off processor, or part on and part off processor. 
       FIG. 11  is a block diagram contrasting the use of a software instruction converter to convert binary instructions in a source instruction set to binary instructions in a target instruction set according to embodiments of the invention. In the illustrated embodiment, the instruction converter is a software instruction converter, although alternatively the instruction converter may be implemented in software, firmware, hardware, or various combinations thereof.  FIG. 11  shows a program in a high level language  1102  may be compiled using an first compiler  1104  to generate a first binary code (e.g., x86)  1106  that may be natively executed by a processor with at least one first instruction set core  1116 . In some embodiments, the processor with at least one first instruction set core  1116  represents any processor that can perform substantially the same functions as an Intel processor with at least one x86 instruction set core by compatibly executing or otherwise processing (1) a substantial portion of the instruction set of the Intel x86 instruction set core or (2) object code versions of applications or other software targeted to run on an Intel processor with at least one x86 instruction set core, in order to achieve substantially the same result as an Intel processor with at least one x86 instruction set core. The first compiler  1104  represents a compiler that is operable to generate binary code of the first instruction set  1106  (e.g., object code) that can, with or without additional linkage processing, be executed on the processor with at least one first instruction set core  1116 . Similarly,  FIG. 11  shows the program in the high level language  1102  may be compiled using an alternative instruction set compiler  1108  to generate alternative instruction set binary code  1110  that may be natively executed by a processor without at least one first instruction set core  1114  (e.g., a processor with cores that execute the MIPS instruction set of MIPS Technologies of Sunnyvale, Calif. and/or that execute the ARM instruction set of ARM Holdings of Sunnyvale, Calif.). The instruction converter  1112  is used to convert the first binary code  1106  into code that may be natively executed by the processor without an first instruction set core  1114 . This converted code is not likely to be the same as the alternative instruction set binary code  1110  because an instruction converter capable of this is difficult to make; however, the converted code will accomplish the general operation and be made up of instructions from the alternative instruction set. Thus, the instruction converter  1112  represents software, firmware, hardware, or a combination thereof that, through emulation, simulation or any other process, allows a processor or other electronic device that does not have a first instruction set processor or core to execute the first binary code  1106 . 
     Architecture and Method for Data Parallel Single Program Multiple Data (SPMD) Execution 
     Instruction set architecture (ISA) extensions for accelerating data parallel workloads require explicit vector word lengths encoded in the machine representation. One embodiment of the invention extends an existing ISA (e.g., such as an x86 ISA) with a scalar microthreaded instruction processing architecture. In particular, a data parallel single program multiple data (SPMD) microarchitecture may be used to provide for scalable execution datapath sizes beyond the limitations of existing instructions, achieving greater instruction execution throughput with reduced energy consumption. 
     Current CPU architectures have used multiple generations of sub-word single instruction multiple data (SIMD) extensions for accelerating data parallel operations (e.g., including SSE2, SSE4, AVX, and AVX-512 in the x86 architecture). Each successive generation extends the state and instruction set of the CPU, creating legacy performance upside issues and requiring recompilation of old codes. 
     Graphics processing units (GPUs) have implemented SPMD architectures using hardware divergence stacks to handle divergent control flow cases. The hardware divergence stack is manipulated via explicit instructions and/or control codes as statically implemented by the finalizer agent for existing GPUs. 
     One embodiment of the invention includes a SPMD data parallel execution engine that uses a scalar microthread abstraction, similar to programming an array of scalar processors with no architected divergence instructions or control codes. As discussed below, these embodiments are particularly suitable for implementation in an existing ISA which includes a predefined Application Binary Interface (ABI). 
     Embodiments described below are agnostic to the programming paradigm used to encode a data-parallel kernel by providing the abstraction of scalar microthreads executing on an efficient vector-style hardware.  FIG. 12  illustrates four examples of programming paradigms for a Sparse Matrix-Vector Multiplication with immediate post-dominator reconvergence including two manually coded examples (ninja-style and pragma-driven)  1201 - 1202 , an implicitly coded example (compiler discovered)  1203 , and an example with explicit coding (using CUDA/OpenCL in the example). 
     The embodiments of the invention allow a programmer to develop data-parallel software using a parallel threaded programming model. The resulting threads are then efficiently executed on vector/SIMD style execution hardware. A larger number of instructions executed per clock is achieved with significantly reduced energy per operation while also providing a highly accessible software abstraction. 
       FIG. 13  illustrates one example of a data parallel cluster (DPC)  1300  which may be integrated within a microarchitecture of a processor and/or may be used as an acceleration engine to execute a particular set of instructions uops  1314 . In one embodiment, front end circuitry  1307  comprises a gang scheduler  1301  to schedule ganged execution of scalar microthreads within a plurality of scalar lanes such as lane  1310 . The number of scalar lanes in the data parallel cluster  1300  can be varied without impacting software. In the illustrated implementation, 16 lanes are shown; however, any number of lanes may be used, depending on the implementation. In one embodiment discussed below, 32 lanes are used. 
     In one embodiment, the gang scheduler  1301  schedules the same instruction on multiple active lanes. A microarchitectural mask  1313  (e.g., read from a mask register) disables those lanes that are not required to be active. In one embodiment, the gang scheduler  1301  reads the mask values to determine which lanes are to be active for which instructions/uops. 
     In one embodiment, an Instruction Decode Queue (IDQ)  1305  within the front end  1307  stores microoperations (uops) of decoded macroinstructions which are added to the IDQ in program order (e.g., in a FIFO implementation). As mentioned, the IDQ  1305  may be partitioned for multiple gangs of operation. 
     Various arrangements for coupling the DPC  1300  to a host processor are described below. In an implementation in which instructions are decoded by a host processor, the DPC  1300  does not include a decoder to generate the uops prior to execution on the lanes. Alternatively, in an implementation in which macroinstructions are forwarded from a host processor or read directly from memory by the DPC, the front end of the DPC (e.g., the gang scheduler  1301 ) includes a decoder to generate sequences of uops which are then stored in the IDQ prior to execution. 
     Each lane in the data parallel cluster  1300  is coupled to the IDQ  1305  from which it receives uops to be executed in parallel. In one embodiment, each lane includes an integer register file (IRF)  1320  and a floating-point register file (FRF)  1330  for storing integer and floating point operands, respectively. Each lane also includes a tensor arithmetic logic unit (ALU)  1340  to perform adaptive lane-wise tensor processing (as described in greater detail below), a per-microthread scalar ALU  1350 , and a per-microthread, independent address generation unit  1360 . In one embodiment, the independent AGU  1360  provides high throughput address generation for codes with gather/scatter memory access patterns. Other independent functional units may also be allocated to each lane. For example, in one embodiment, each lane is equipped with an independent Jump Execution Unit (JEU) which allows the lanes to diverge and interact with the microarchitectural mask to provide the illusion of independent threads. 
     The illustrated architecture also includes a shared data cache  1380  to store local copies of data for each of the lanes. In one embodiment, if the data parallel cluster  1300  is integrated in a chip or system with a host processor, it participates in the cache coherency protocol implemented by the host processor. A page miss handler  1384  performs page walk operations to translate virtual addresses to physical (system memory) addresses and a data translation lookaside buffer (DTLB) caches the virtual-to-physical translations. 
     As illustrated in  FIGS. 14A-C , the data parallel cluster  1300  may be integrated in a computer system in a variety of ways. In  FIG. 14A , the DPC  1300  is integral to a core  1701   a ; in  FIG. 14B , the DPC  1300  is on the same chip and shared by a plurality of cores; and in  FIG. 14C , the DPC  1300  is on a different chip (but potentially in the same package) as the cores  1401   a - b.    
     Turning first to  FIG. 14A , The illustrated architectures include a core region  1401  and a shared, or “uncore” region  1410 . The shared region  1410  includes data structures and circuitry shared by all or a subset of the cores  1401   a - b . In the illustrated embodiment, the plurality of cores  1401   a - b  are simultaneous multithreaded cores capable of concurrently executing multiple instruction streams or threads. Although only two cores  1401   a - b  are illustrated in  FIG. 14A  for simplicity, it will be appreciated that the core region  1401  may include any number of cores, each of which may include the same architecture as shown for Core  1401   a . Another embodiment includes heterogeneous cores which may have different instruction set architectures and/or different power and performance characteristics (e.g., low power cores combined with high power/performance cores). 
     The various components illustrated in  FIG. 14A  may be implemented in the same manner as corresponding components in  FIGS. 1-11 . For example, the core  1401   a  may execute the tile gather and scatter instructions using one of the instruction formats in  FIGS. 1 a   - b  and  2   a - c , and/or using the register architecture illustrated in  FIG. 3 . In addition, the cores  1401   a  may include the components of core  490  shown in  FIG. 4 b   , and may include any of the other processor/core components described herein (e.g.,  FIGS. 5 a - b   ,  FIG. 6 , etc). 
     Each of the cores  1401   a - b  include instruction pipeline components for performing simultaneous execution of instruction streams including instruction fetch circuitry  1418  which fetches instructions from system memory  1460  or the L1 instruction cache  1410  and decoder  1409  to decode the instructions. Execution circuitry  1408  executes the decoded instructions to perform the underlying operations, as specified by the instruction operands, opcodes, and any immediate values. 
     In the illustrated embodiment, the decoder  1409  includes DPC instruction decode circuitry  1499  to decode certain instructions into uops for execution by the DPC  1300  (integrated within the execution circuitry  1408  in this embodiment). Although illustrated as separate blocks in  FIG. 14A , the DPC decode circuitry  1499  and DPC  1300  may be distributed as functional circuits spread throughout the decoder  1409  and execution circuitry  1408 . 
     In an alternate embodiment, illustrated in  FIG. 14B , the DPC  1300  is tightly coupled to the processor cores  1401   a - b  over a cache coherent interconnect (e.g., in which the data cache  1380  participates in the same set of cache coherent memory transactions as the cores). The DPC  1300  is configured as a peer of the cores, participating in the same set of cache coherent memory transactions as the cores. In this embodiment, the decoders  1409  decode the instructions which are to be executed DPC  1300  and the resulting microoperations are passed for execution to the DPC  1300  over the interconnect  1406 . In another embodiment, the DPC  1300   1491  includes its own fetch and decode circuitry to fetch and decode instructions, respectively, from a particular region of system memory  1460 . In either implementation, after executing the instructions, the matrix accelerator  1491  may store the results to the region in system memory  1460  to be accessed by the cores  1401   a - b.    
       FIG. 14C  illustrates another embodiment in which the DPC is on a different chip from the cores  1401   a - b  but coupled to the cores over a cache coherent interface  1496 . In one embodiment, the cache coherent interface  1496  uses packet-based transactions to ensure that the data cache  1380  of the DPC  1300  is coherent with the cache hierarchy of the cores  1401   a - c.    
     Also illustrated in  FIGS. 14A-C  are general purpose registers (GPRs)  1418   d , a set of vector/tile registers  1418   b , a set of mask registers  1418   a  (which may include tile mask registers as described below), and a set of control registers  1418   c . In one embodiment, multiple vector data elements are packed into each vector register which may have a 512 bit width for storing two 256 bit values, four 128 bit values, eight 64 bit values, sixteen 32 bit values, etc. Groups of vector registers may be combined to form the tile registers described herein. Alternatively, a separate set of 2-D tile registers may be used. However, the underlying principles of the invention are not limited to any particular size/type of vector/tile data. In one embodiment, the mask registers  1407  include eight 64-bit operand mask registers used for performing bit masking operations on the values stored in the vector registers  1406  (e.g., implemented as mask registers k 0 -k 7  described above). However, the underlying principles of the invention are not limited to any particular mask register size/type. A set of one or more mask registers  1418   a  may implement the tile mask registers described herein. 
     The control registers  1418   c  store various types of control bits or “flags” which are used by executing instructions to determine the current state of the processor core  1401   a . By way of example, and not limitation, in an x86 architecture, the control registers include the EFLAGS register. 
     An interconnect  1406  such as an in-die interconnect (IDI) or memory fabric implementing an IDI/coherence protocol communicatively couples the cores  1401   a - b  (and potentially a the DPC  1300 ) to one another and to various components within the shared region  1410 . For example, the interconnect  1406  couples core  1401   a  via interface  1407  to a level 3 (L3) cache and an integrated memory controller  1430 . In addition, the interconnect  1406  may be used to couple the cores  1401   a - b  to the DPC  1300 . 
     The integrated memory controller  1430  provides access to a system memory  1460 . One or more input/output (I/O) circuits (not shown) such as PCI express circuitry may also be included in the shared region  1410 . 
     An instruction pointer register  1412  stores an instruction pointer address identifying the next instruction to be fetched, decoded, and executed. Instructions may be fetched or prefetched from system memory  1460  and/or one or more shared cache levels such as an L2 cache  1413 , the shared L3 cache  1420 , or the L1 instruction cache  1410 . In addition, an L1 data cache  1402  stores data loaded from system memory  1460  and/or retrieved from one of the other cache levels  1413 ,  1420  which cache both instructions and data. An instruction TLB (ITLB)  1411  stores virtual address to physical address translations for the instructions fetched by the fetch circuitry  1418  and a data TLB (DTLB)  1403  stores virtual-to-physical address translations for the data processed by the decode circuitry  1409  and execution circuitry  1408 . 
     A branch prediction unit  1421  speculatively predicts instruction branch addresses and branch target buffers (BTBs)  1422  for storing branch addresses and target addresses. In one embodiment, a branch history table (not shown) or other data structure is maintained and updated for each branch prediction/misprediction and is used by the branch prediction unit  1402  to make subsequent branch predictions. 
     Note that  FIGS. 14A-C  are not intended to provide a comprehensive view of all circuitry and interconnects employed within a processor. Rather, components which are not pertinent to the embodiments of the invention are not shown. Conversely, some components are shown merely for the purpose of providing an example architecture in which embodiments of the invention may be implemented. 
     Returning to  FIG. 13 , the processing cluster  1300  is arranged into a plurality of lanes  1310  that encapsulate execution resources (e.g., an IRF  1320 , an FRF  1330 , a tensor ALU  1340 , an ALU  1350 , and an AGU  1360 ) for several microthreads. Multiple threads share a given lane&#39;s execution resources in order to tolerate pipeline and memory latency. The per-microthread state for one implementation is a subset of a modern processor state. 
       FIG. 15  illustrates one example of a microthread state  1500  which is a subset of a scalar x86 state. The microthread state  1500  includes state from general purpose registers  1501  (e.g., sixteen 64-bit registers), XMM registers  1502  (e.g., thirty-two 64-bit registers), an RFLAGS register  1504 , an instruction pointer register  1505 , segment selectors  1506 , and the MXCSR register  1503 . Using a subset of a scalar x86 is convenient for programmers, is software compatible with existing x86 codes, and requires minimal changes to current compilers and software toolchains. The lanes of this embodiment execute scalar, user-level instructions. Of course, the underlying principles of the invention are not limited to this particular arrangement. 
     In one embodiment, illustrated in  FIGS. 16A-D , multiple data parallel clusters  1300 A-D are collocated into a larger unit of scaling referred to as a “DPC tile”  1600 . The various data parallel clusters  1300 A-D may be coupled to one another over a high speed interconnect of fabric. The DPC tile  1600  may be integrated within a processor or computer system using any of the microarchitectural implementations described above with respect to the single DPC  1300  in  FIGS. 14A-C  (i.e., DPC Tile  1600  may be substituted for the DPC  1300  in these figures). 
     The DPC tile  1600  includes a shared cache  1601  and relies on the existing fetch  1418  and decoder  1409  of one or more cores. A prefetcher  1602  prefetches data from system memory and/or the cache hierarchy in anticipation of uops executed on the data parallel clusters  1300 A-D. Although not illustrated, the shared cache  1601  may be coupled between the data parallel clusters  1300 A-D and each DPC  1300 A-D may be coupled to the on-chip interconnection network (e.g., IDI). 
     Sharing the execution resources of a processor across a whole cluster amortizes the relatively complex decode process performed by decoder  1409 . One embodiment of the invention can support hundreds of microthreads executing instructions using a tiny fraction of the fetch  1418  and decoder  1409  resources of a conventional processor design. 
     To highlight certain embodiments of the invention,  FIG. 17  illustrates sparse matrix-vector multiplication. The sparse-matrix vector multiply computation assigns a microthread to each row of a sparse matrix. The outer loop (loop  0 ) distributes rows among the microthreads while the inner loop (loop  1 ) performs a sparse dot product. As the number of non-zeros per sparse row is highly variable in sparse matrices, the trip count of the inner loop will vary across microthreads. At beginning of computation (before loop  0 ), all the microthreads begin execution at the same instruction pointer. While all microthreads are executing the same instruction pointer, providing the illusion of microthreads using a SIMD data-path is trivial. As execution proceeds, the variable trip count of the inner loop will result in divergence. 
     Divergence occurs when some microthreads execute a different instruction pointer. In the above example, the conditional jumps at 0x400d47 and 0x400d79 can induce divergence. As divergence implies multiple instruction pointers, the microarchitecture must keep track of the mapping between microthreads and their associated instruction pointers. An instruction pointer with a collection of associated threads is referred to here as a “fragment.” Performance on a data-parallel machine is highly dependent on reconverging fragments in order to amortize instruction fetch across the largest possible number of microthreads. 
     The immediate post-dominator of a divergent branch is the “closest” instruction where divergent execution paths can be guaranteed to re-converge. That said, microthread reconvergence can occur before or after the immediate post-dominator. In the example shown in  FIG. 17 , the basic block labeled “obb_0x400d7b” is the immediate post-dominator of the basic blocks terminated by the conditional jumps at 0x400d47 and 0x400d79. If the conditional jump at 0x400d47 or 0x400d79 causes microthreads to diverge, the instruction at 0x400d7b is the first time it can be guaranteed that the execution paths will reconverge. 
     An existing method to build a microthreaded machine using a SIMD data-path is to explicitly augment branches with a reconvergence instruction pointer (IP) and then place an instruction or control code at the immediate post-dominator. This approach exploits the static reconvergence guarantee made by the immediate-post dominator and is commonly performed by a compiler. Attempting a compiler-driven approach is a non-starter in current ecosystems. More importantly, augmented branches/reconvergence tokens do not have a sematic meaning outside of the data-parallel extension described herein and would make the extension incompatible with existing software. 
     One embodiment of the invention includes circuitry to dynamically manage microthread reconvergence. This approach allows for the execution of legacy instructions in a data-parallel fashion and can provide higher performance than the alternative, statically marked reconvergence mechanism used by prior systems. As this approach does not rely on compiler analysis for reconvergence, the hardware is in complete control of scheduling the microthreads in order induce reconvergence of execution fragments. 
     In one embodiment, the gang scheduler  1301  finds microthreads at the same instruction pointer, gangs the microthreads together into execution fragments, chooses one of the available fragments, and then executes it on a SIMD data-path. The task of the gang scheduler  1301  is similar to an associative search over all of the instruction pointers of microthreads resident in a processing cluster (e.g., scheduling at least one fragment every cycle). The gang scheduler  1301  may rely on various detectable properties to make efficient scheduling decisions. For example, in one embodiment, the gang scheduler  1301  performs scheduling by focusing on conditional branches that induce control divergence, based on the number of divergent instruction pointers bounded by the total number of microthreads, and/or in accordance with the fact that microthread reconvergence is likely to occur at instruction locations near the immediate post-dominator of the divergence branch. Finally, one embodiment of the gang scheduler heuristically selects the minimum IP fragment when multiple fragments are available for selection. 
       FIG. 18  provides an example in which some threads execute basic block  1  (BB 1 ), while others execute BB 2 . Both reconverge at BB 3 . Thus, BB 3  is post-dominator of {BB 0 , BB 1 , BB 2 }. In one embodiment, the gang scheduler  1301  performs scheduling based on the fact that the post-dominator is likely found at the larger future address when the control flow graph (CFG) is linearized. Thus, it may schedule the lower PC addresses first in order to induce improved mask coherence. In this specific example, basic blocks BB 1  and BB 2  should be executed before BB 3  to induce reconvergence. 
     To exploit the above properties, one embodiment of the invention builds a data structure (e.g., a table or similar structure) to keep track of fragments (e.g., an instruction pointer (IP) and associated collection of threads), using a number of entries sufficient to hold a fully divergent gang (e.g. either 16 or 32 entries). The structure is managed such that a partial-order invariant is maintained to provide the ability to select the fragment with the minimum IP quickly. 
     One implementation comprises a binary matrix-based scheme where each fragment is allocated a row and column in the binary matrix. One example of reconvergence circuitry  1900  including a matrix  1901  is illustrated in  FIG. 19 . In this matrix  1901 , a dependency bit (D) is set in entry(i,j) to indicate that fragment i is at a larger IP than the fragment corresponding to row j. When a new fragment is inserted into the gang scheduler  1300 , the reconvergence circuitry  1900  compares its NIP with the NIPs of existing fragments in the matrix and the row dependency bits are set accordingly. The minimum IP fragment is found by computing veto bits  1905  on the associated columns. The veto value will only affect entries with the dependency bit set (higher IPs). This process ensures that the row with the minimum IP will selected, as indicated by one of the select signals  1906 , as it is in the only row that has not been vetoed. 
     The illustrated example of matrix-based minimum IP selection using matrix  1901  is defined by the following code sequence: 
     Select 0 =NOR(Veto 1  &amp; DependencyBit( 0 ,  1 ), [=1] 
     Veto 2  &amp; DependencyBit( 0 , 2 ), 
     Veton &amp; DependencyBit ( 0 , n )) 
     Select 1 =NOR(Veto 0  &amp; DependencyBit( 0 , 0 ), [=0] 
     Veto 2  &amp; DependencyBit ( 0 , 2 ), 
     Veton &amp; DependencyBit ( 0 , n )) 
     Select 2 =NOR(Veto 0  &amp; DependencyBit( 0 , 0 ), [=0] 
     Veto  1  &amp; DependencyBit ( 0 ,  1 ), 
     Veton &amp; DependencyBit ( 0 , n )) 
     In summary, ready fragments competing for scheduling transmit veto signals  1905  down their associated columns. The veto signal only impacts entries with the dependency bit set (i.e., those with larger IPs). The row with the minimum IP is not vetoed and will therefore be selected by the reconvergence circuitry  1900  (Select 0 ). 
     An alternative implementation for minimum IP selection uses a binary heap (also known a priority queue) to manage the fragments. A binary heap is a linearization of a binary tree in an array structure. The array structure enforces the invariant that a parent node is smaller than both of its children. Therefore, the root of the tree (the first entry in the array) has the minimum IP and can be accessed in O( 1 ) gates, as shown in  FIG. 20 . Insertion and deletion into the heap take O(lg 2 (microthreads)) gates in the worst case. In this example, the highest priority IP is in the leftmost entry and can be read directly. The insertion or deletion of new IPs requires a number of gates that fits with a cycle or two for many interesting tuples of total microthreads (and frequency based on prototyping). 
     An implementation may augment the cluster&#39;s instruction cache (or decoded uop cache, if present) with reconvergence hints. These hints provide significantly higher mask density when the latency to resolve the divergence event is longer than the latency for the front-end to fetch to the next fragment switch for this gang. By storing reconvergence locations in the I-cache or uop cache (DSB), embodiments of the invention significantly improve performance on divergent codes. In one embodiment, when a reconvergence event occurs, the location (IP) in the cache is marked as a reconvergence point. If an execution fragment later hits the reconvergence IP with a partial mask, execution is stalled for a small number of cycles in order to provide an opportunity for reconvergence. To guarantee forward progress, the number of stall cycles is limited in order to prevent deadlock. Using these techniques, the discovered reconvergence points closely approximate the points that a compiler would insert with reconvergence instructions (e.g., in a static reconvergence scheme). As most data-parallel codes have a relatively small instruction footprint, a conventionally sized instruction cache (32 kBytes) or uop cache (6144 uop) can capture all of the important reconvergence IPs. 
     It is also possible to generate reconvergence uops using hardware-based techniques. In this approach, the instruction front-end augments branch uops with the reconvergence UIP and generates explicit reconvergence uops at the reconvergence IP. Augmenting branch uops and adding uops to the uop stream is a straightforward extension to a uop cache. In one embodiment, however, hardware is used to discover the {branch ip, reconvergence ip} pairs used for reconvergence as follows:
         (a) Each microthread manages a small list that contains {branch IP, branch mask} pairs. The branch IP is the IP of the branch and the branch mask is the mask of all active threads at the given branch.   (b) When threads hit a divergent branch, every active microthread records the current {branch IP, mask} pair and saves it to its thread-local list of divergence history.   (c) When threads reconverge, the circuitry computes a new “active mask” that reflects the reconverged mask. Using the newly computed reconverged mask, all microthreads traverse their local divergence history list (walk over the entries) until the following invariant is satisfied “reconvergence_mask AND {IP, branch_mask}_i==reconvergence_mask”. This process discovers the previous branch where the microthreads initially diverged.   (d) After discovering the different branch, the hardware saves the {branch IP, reconvergence IP} in a table for later use.       

     In addition, one embodiment of the invention includes a new branch predictor. Instead of predicting per-microthread branches, the branch predictor of this embodiment makes predictions for an entire execution fragment. As the branch behavior of microthreads are heavily correlated in practice, this implementation significantly reduces the hardware requirements for the branch predictor. 
       FIG. 21  illustrates an example of a microarchitectural mask manipulation applied to a microprocessor pipeline. The illustrated pipeline includes the gang scheduler  1301  for scheduling instructions, an instruction fetch unit  1418 , and a decoder  1409  for decoding macroinstructions into uops. Allocation circuitry  2110  allocates execution resources including registers and functional units, execution circuitry  1408  executes the uops, and retirement circuitry  2111  retires the instructions, storing the architectural state and deallocating the execution resources. 
     When a fragment is selected, the associated fetch mask and IP are passed from the fetch circuitry  1418  to the decoder  1409 . The decoder  1409  generates micro-ops (uops) with an implicit additional register dependency on the microarchitectural mask which is written by all conditional branch and indirect jump instructions. To support load divergence, loads can also write the microarchitectural mask. All uops read the microarchitectural mask. Thus, the microarchitectural mask is treated much like a conventional register dependency from the perspective of forwarding and hazard detection. As shown in  FIG. 21 , the DPC microarchitecture performs a logical AND (conjunction)  2120  for in-between dependent masks. 
     In one embodiment, the gang scheduler  1301  attempts to schedule a fragment every cycle by examining the available execution fragments and selecting the “best” one (by either minimum IP or some other heuristic). Once the fragment has been selected, the gang scheduler  1301  sends the fragment, including the IP and microarchitectural mask, to the instruction fetch circuitry  1418 . The instruction fetch circuitry  1418  produces uops and a microarchitectural mask. Note that the microarchitectural mask produced by the instruction fetch circuitry  1418  may not be the same as the one dispatched. The instruction fetch circuitry  1418  includes several mechanisms to detect re-convergence and may increase mask density. When a re-convergence event occurs, the microarchitectural mask density (the population count of the bits in the microarchitectural mask) increases. 
     As a fragment “owns” the instruction fetch circuitry  1418  for several cycles, it is possible that the IP of the fragment will match another fragment already in the gang scheduler  1301 . In one embodiment, as the previously mentioned reconvergence circuitry  1900  operates non-speculatively (e.g., within the retirement stage  2111 ), another mechanism is implemented to exploit dynamic reconvergence detected in the front-end, referred to herein as “front-end fragment merging.” In one embodiment, front-end fragment merging provides significant benefits when used with a non-speculative gang scheduler and a long instruction-fetch-to-retirement latency. 
     One embodiment of the pipeline performs implicit microarchitectural masking. For example, a first instruction (e.g., movq) may have an implicit dependency on a second instruction (e.g., jc). By treating the mask register as an explicit dependence, proper behavior after divergent instructions is ensured. 
     In one embodiment, the decoder  1409  augments every uop with an implicit additional dependency on the producer of the microarchitectural mask. The microarchitectural mask and associated manipulation circuitry allows hardware to convert the control dependency of a conditional branch into a data-dependence dynamically. This improves efficiency when converting thread-level parallelism into a form suitable for execution on SIMD-style hardware. 
     When the instruction fetch circuitry  1418  produces uops to the back end of the machine, allocation proceeds in similar fashion to a conventional out-of-order microprocessor; however, the key difference is that the microarchitectural mask is now an explicit dependency (e.g., like another register field in the uop). All instructions read the microarchitectural mask; however, only a small subset of instructions write the microarchitectural mask. Conditional branches and indirect jumps must write the microarchitectural mask. An implementation may choose to implement “load-divergence” by making loads to memory also write the microarchitectural mask register. Therefore, when a uop reads its operands in a reservation station, it will also do so for the microarchitectural mask. However, the microarchitectural mask is treated differently than a conventional operand. The new microarchitectural mask is computed taking the AND of the mask presented to the reservation station with the forwarded mask. This ensures microthreads behind a divergence event (branch or load) will properly execute. 
     This data-dependence based on the control dependency of a conditional branch could be subject to speculation. An implementation using a reorder buffer (ROB) may choose to speculatively dispatch instructions in the shadow of a mask producer to increase utilization in execution regimes with low occupancy or implementations with small numbers of microthreads per lane. Once the mask producer has been resolved, the instructions belonging to this fragment in the shadow of the mask producer may be cleared within the pipeline or from the reorder buffer (ROB). 
     The retirement circuitry  2111  updates the gang scheduler  1301  with new fragments. The microarchitectural mask is retired non-speculatively; consequently, all gang scheduler updates are non-speculative. The gang scheduler  1301  issues instructions from a given fragment until a specific divergence event occurs (e.g., divergent branch, cache miss, minimum IP fragment switch, priority inversion fragment switch, livelock breaker fragment switch). When this occurs, one or more fragments must be written back to the gang scheduler. A new fragment generated by a divergent instruction (e.g. a conditional branch) is handled slightly differently than a fragment switch event. 
     When a fragment switch operation occurs, the associated uop is tagged by the front-end that it is the last uop for a given fragment. In retirement, the uop will update the gang scheduler  1301  with its mask and IP, removing it from the execution state of the machine. 
     Other types of fragment switches may include inverting priority of the gang scheduler heap to allow forward progress on fragments that are otherwise not live in the machine, livelock breaking when a given fragment has been consuming all resources but not making forward progress, call/return stack fragment switches for indirect branches, and predictor-based fragment switches. 
     In one embodiment, divergent branches compute two {masks, IP} tuples. The execution hardware  1408  chooses the execution path with the minimum IP to execute. The current fragment assumes the mask for the appropriate branch direction and forwards the updated mask to any dependent uops. When the divergent branch retires, it updates the gang scheduler with the not-taken fragment. In both cases, retirement updates to the gang scheduler  1301  will cause the gang scheduler to attempt to reconverge fragments. 
     An implementation may employ a hardware mechanism to assign speculative mask state to uops allowing it, in effect, to have a longer latency to resolve a divergence event and still have the most full mask possible at dispatch because the mask updates happens later in the pipeline. This requires adding a table of fragment masks which each uop references. There is one entry for every unique fragment that is allowed to be live within the backend of the machine. Each table entry corresponds to a different fragment sequence ID. 
     The above techniques are useful for shutting off the instruction fetch cluster of a data parallel machine and executing out of the IDQ  1305 . The new IDQ mask table is separate from the heap state for at least two reasons: (1) If a fragment push occurs after the fragment switch uop for this sequence ID is allocated, then this table cannot perform a fragment merge without potentially violating program order; and (2) any fragment ending event that removes a fragment from the heap is still a candidate for merge. 
     On a fragment push, if the fragment sequence ID is resident in the IDQ  1305  and the fragment switch operation has not yet allocated, then a merge is performed on the IDQ mask table and the gang scheduler heap. This merged mask is copied into every uop complete mask as they allocate. 
     A method in accordance with one embodiment is illustrated in  FIG. 22 . The method may be implemented on the processor and system architectures described above, but is not limited to any particular architecture. 
     At  2201  instructions of one or more threads are fetched and, at  2202 , the instructions are decoded to generate uops. As mentioned, in one embodiment, the fetch and decode are performed by a host processor (e.g., such as an x86 processor with a simultaneous multithreading/multi-core architecture). In another embodiment, the DPC includes fetch and decode circuitry to fetch and decode its own instructions to generate uops. 
     At  2203  a subset of uops are identified which are to be executed on the DPC. These uops are then forwarded to the DPC (e.g., over an on-chip interconnect if the DPC is on-chip or an off-chip interconnect if the DPC is off-chip). 
     At  2204  the DPC scheduler evaluates microthreads of uops based on associated variables associated with the microthreads. As mentioned, in one embodiment, the variables include the instruction pointer (IP) values associated with the microthreads. At  2205  the DPC scheduler gangs the microthreads together into fragments and schedules the fragments for execution on DPC lanes based on the evaluation from  2204 . As previously described, the DPC scheduler schedules the fragments with the goal of inducing microthread reconvergence. 
     Adaptable and Efficient Lane-wise Tensor Processing 
     As mentioned above, one embodiment of the data parallel cluster  1300  includes a tensor ALU  1340  for processing tensor data within its designated lane. One particular embodiment of the tensor ALU  1340  is described below. Since previous solutions have not paired SPMD with tensor processing, they are less adaptable and less efficient than tensor ALU  1340  described here. 
     In particular, one embodiment of the tensor ALU (TALU)  1340  is highly adaptable and uses a 2D-broadcast implementation that achieves highly efficient Tensor Matrix Multiplication (TGEMM) in a SPMD architecture. In addition, the TALU  1340  is reconfigurable to handle various matrix dimensions and includes support structures (e.g., register file read ports, cache bandwidth requirements, etc) to allow the TALU  1340  to operate at a high efficiency. 
     i. Tensor ALU (TALU) Instruction Embodiments 
     As illustrated in  FIG. 23 , one embodiment of a TALU matrix instruction  2300  comprises an opcode field  2301  to specify the operation to be performed, operand size fields  2302 - 2304  to specify a size for each of the operands, two 4-register group operand fields  2304 - 2305 , and an operand field  2306  identifying four elements in a memory location. The ‘4’ at the start of opcode  2301  indicates the number of elements of A used in the operation. The DBB portion of the opcode ( 2302 - 2304 ) indicates a doubleword size (D) for operand C, and Byte sizes (B) for operands A and B. Thus, four elements of srcA come from memory at a unit stride of 1 Byte. 
     In one embodiment, each TALU  1340  includes matrix multiplication circuitry to perform the matrix multiplication operation: [1×8] C +=[1×4] A *[4×8] B . The microarchitecture of the TALU  1340  in this embodiment may be a 4×8 INT8*INT8 multiplier which accumulates into an INT32 unit. For example, existing values stored in an accumulator tile/vector register may be added to the products generated by the multiplier. The resulting sums may then be stored back to the accumulator tile/vector register. 
     In one embodiment, four rows of eight 1-Byte elements of srcB are loaded in four register operands. The instruction may specify a register group as the source for this (e.g., 4 register group  2304 ). One row (8 elements, 4 Bytes each) of C is read and written (accumulated) with this instruction. The size of a C element is decoded from D (stands for Double Word) in the instruction. 
     Thus, register and memory usage for this embodiment is as follows: 
     (a) 4 rows×8 columns of 1 Byte operands in B require 32 Bytes in total (each row requires 8 Bytes). In one embodiment, this is stored using 4 DPC registers, with each register being 8 Bytes/64 bits in size. 
     (b) 1 row×8 columns of 4 Byte operands in C also require 32 Bytes total, again consuming 4 DPC registers. Note that C is read and written in unit stride of 4 Byte contiguous memory. 
     (c) For specifying the start of srcA from memory an INT register is used; srcA access is at stride of 1 Byte contiguous memory. 
     (d) Using the instruction format in  FIG. 23 , 4 registers as a group are specified for C and B in fields  2304  and  2305 , respectively. In one embodiment, the last two bits of the register operand are masked out and 0b00, 0b01, 0b10 and 0b11 are added to identify the 4 registers to be used. 
     While 4TFMADBB is shown as an example for a 4×8 TALU, the underlying principles of the invention are not limited to any particular operand size or register arrangement. By way of example, and not limitation, a tensor instruction with a 8TFMADBB opcode may use an 8×4 TALU and a tensor instruction with a 16TFMADBB opcode may use a 16×2 TALU. 
     ii. 2D-Broadcast Embodiments 
     As mentioned, one embodiment of a DPC  1300  includes a 32-lane implementation with a 4×8 TALU in each lane. Prior to a 4TFMADBB instruction, loads may be performed to move the 4×8 B tiles of data (e.g., 4 loads of 8 Bytes each) into four adjacent XMM registers. For example, the prefetcher  1602  mentioned above may use hints or other techniques to anticipate and prefetch the data into the shared cache  1601 . Similarly, in an implementation with one DPC  1300  (rather than a DPC tile  1600 ), a prefetcher may prefetch the data into the data cache  1380  so that it will be available locally to all of the lanes. 
     In one embodiment, all 32 lanes execute these loads and fetch adjacent B tiles into their corresponding register files. Each lane includes register files to hold one set of architectural registers per Gang. To improve throughput, the load of a B tile in each lane will be marked as gang invariant within the lane. As such, the same B-tile will be broadcast and written into each Gang&#39;s register files within the lane. This constitutes one of the dimensions of the 2D-broadcast. 
     In one embodiment, loads are also performed to move the C tile (1 row of 8 elements of 4 Bytes each=32 Bytes total per lane) into four adjacent XMM registers. Since four out of 32 XMM registers are taken for the B tile, 28 XMM registers are available for the C tiles. Since each 4TFMADBB instruction needs 4 XMM registers for the C tile, 7 such 4TFMADBB instructions may be executed in a Gang (i.e., before XMM registers are fully consumed). Since there may be eight Gangs in one implementation of the DPC, there may be 7×8=56 4TFMADBB registers before all XMM registers are used in all eight Gangs. 
     In one embodiment, these 56 4TFMADBB instructions are used to determine a unit of Blocking. Since each 4TFMADBB instruction produces 8 elements of C tile per lane and since there are 32 lanes, the blocking size that can be achieved by a 32 lane implementation of a DPC running 4TFMADBB instructions is 56×256. As another example, the Blocking size that can be achieved by a 32 lane implementation of DPC running 8TFMADBB instructions will be 112×128. The bigger the Blocking size, the higher the data reuse and hence, fewer the number of times the same data element needs to be read in order to complete a matrix multiplication. 
     Once the B and C tiles are loaded into registers in each lane, the 4 elements of srcA are loaded from memory. In one embodiment, this load is fused with the 4TFMADBB instruction such that the load writes to an FTMP register (e.g., a temporary or non-architectural register) and the 4TFMADBB instruction reads this FTMP register for srcA. The same A tile is read by all 32 lanes, in effect, broadcasting the same A tile data to all the lanes. This constitutes the 2nd dimension of the 2D-broadcast scheme (A data reuse). Both the A and B broadcasts increase data reuse and enable the 56×256 blocking size for 4TFMADBB. Note that the same B tiles are reused for each of the 56 A tile reads (B data reuse). Furthermore, once the partial products of a 56×256 block of C are done, the K dimension is processed (i.e., input matrix A is M×K dimension, input matrix B is K×N dimension, and output matrix C is M×N dimension) and the results are accumulated into the same C tile (C data reuse). 
       FIG. 24  illustrates operations that happen in each lane in one embodiment. In particular, a 1×4 A tile  2401  is multiplied with a 4×8 B tile  2302  to produce the partial product of a 1×8 C tile  2403 . In one embodiment, multipliers  2404  in the lane multiply the first element of A with each of the 8 elements in the top row of B to produce the 8 elements in the top row of C. Similarly, the second, third, and fourth elements of A are multiplied with the second from the top, third from the top, and bottom rows of B, respectively, to produce corresponding rows of C. These partial product rows of C are added by adders/accumulators  2405  within the lane. 
       FIG. 25  illustrates how the tiles of A, B, C are moved to complete the entire matrix multiplication in one embodiment. These operations are sufficient to generate one 56×256 Block of the C matrix. These operations are repeated by moving along the M and N dimensions of the C matrix to complete the instruction. Each lane is first loaded with 7*G tiles of C (the accumulator operand) where there are 7 accumulators in each Gang and G is the number of Gangs per lane. Each lane is loaded with 1 tile of B (a Gang invariant load). Loading the 1 tile of B copies elements into registers of all Gangs in a lane. The TS_W elements from A are broadcast every cycle across all lanes and multiply-accumulate operations (e.g., FMA operations) are performed to produce new TS_W elements of C each cycle. After 7 A loads, one embodiment switches between the Gangs in a lane. The inner loop C[56R*8C]+=A[56R*4C]*B[4R*8C] where this 56Row*8Col C-block is re-used across the K dimension. In particular, one embodiment moves in the K direction of A and B. 
     iii. Adaptable Tensor ALU Design 
     In order to achieve high hardware utilization with widely varying matrix dimensions, one embodiment of each TALU  1340  uses the same circuitry to implement different blocking shapes using different configurations of the 32 multipliers. Consider two separate implementations of the TALU in 4×8 ( FIG. 26 ) and 8×4 ( FIG. 27 ) respectively, which illustrate 8-bit A-term processing elements  2601 - 2701 , 8-bit B-term processing elements (with multipliers)  2602 - 2702 , and 32-bit C accumulate processing elements  2603 - 2703 . The various processing elements in  FIGS. 26-27  are identified using different fill patterns. 
     If the B tile is stored in column-first format, a base 4×8 configuration of multipliers can be used to implement an 8×4 configuration by adding the adjacent even and odd columns as shown in  FIG. 28 . A set of 2-input 32-bit multiplexers  2804   a - h  are included in this embodiment to select from different input options. 
     In the 4×8 configuration of this implementation, the first 4 bytes of A  2701  are broadcast to all 8 dot-product columns  2802  (all muxes  2804   a - d  steer their left input in this configuration). At the accumulate stage at the bottom, the C inputs are directly available to the accumulator (even columns), or selected through a multiplexer  2804   e - h  (odd columns), thereby implementing the same function as shown in  FIG. 26  (i.e., 4TFMADBB). 
     In the 8×4 configuration, the low 4 bytes of A  2701  are supplied to the even columns and the high 4 bytes of A  2701  are supplied to the odd columns. As illustrated, the input multiplexers  2804   a - d  steer the bytes of A  2701  to the correct columns  2802 . At the accumulator stage  2803 , the C input is added to the dot-product at each even column, and the resulting sum is steered via multiplexers  2804   e - h  to be added to the dot-product of the neighboring odd column, producing the final result at the output of each adder in the odd columns Thus, this configuration implements the same function as shown in  FIG. 27  (i.e., 8TFMADBB). 
     Reconfiguration of the initial matrix of multipliers as described above can be readily extended to a 16×2 matrix computation. The need for such reconfiguration stems from the need to handle different matrix sizes efficiently (e.g., from a square 2048×2048 matrix to a skewed 2048×128 or 128×2048 matrix). 
     iv. Support Structures to Sustain High Efficiency 
     1. Register Banking: 
     When the 4TFMADBB instruction is in steady state operation, it needs to read and write 4 XMM registers for the C Tile. In one embodiment, in order to avoid adding 4 Read and 4 Write ports to the register file, the register file is banked into Odd and Even banks. XMM0, XMM2, XMM4, etc., are in the even bank and XMM1, XMM3, XMM5, etc., are in the odd bank. Since the C Tile is restricted to span 4 adjacent registers (such as XMM0-XMM3 or XMM4-XMM7 etc.), 2 read ports and 2 write ports in each bank are sufficient. 
     2. B Tile Broadcast across Gangs: 
     In one embodiment, the register file supports writing/broadcasting the results of a B Tile load operation into the same registers of each Gang. For example, if the first gang invariant load fetches the first row of the B Tile into XMM0, XMM0 registers of all 8 gangs are written with the same data. 
     3. A Tile Broadcast across Lanes: 
     In one embodiment, the Data Cache  1380  supports broadcasting the same data to all 32 lanes of the data parallel cluster  1300 . In one embodiment, the data cache  1380  supports high speed parallel access of the B Tile and C Tile by all 32 lanes. 
     DPS Gang Invariant Operation Optimizations 
     In a Single Program Multiple Data (SPMD) model such as those described above, the same instruction is executed on many lanes with different data on each lane. As mentioned, the different micro-threads (uthreads) that are executing the same instruction in all the lanes  1310  form a Gang. Sometimes all or a subset of the uthreads within a Gang or even all uthreads within all Gangs may operate on the same data to perform the same operations. Such operations are called Gang Invariant Operations (GIOs). Having all the uthreads separately execute GIOs results in wasted power and wasted execution bandwidth. 
       FIG. 30  illustrates additional detail of one embodiment of a DPC front end  1307  which includes dynamic GIO detection circuitry  3005  for identifying GIOs based on information associated with the uops (e.g., inserted into the instruction stream by the compiler) and/or execution feedback from the various lanes  3030 . Examples of the analysis performed by the dynamic GIO detection circuitry  3005  are provided below. 
     In addition,  FIG. 30  illustrates allocate &amp; rename circuitry  1301  for allocating execution resources within the lanes  3030  (e.g., ALUs, TALUs, etc) and performing register mapping/renaming within the lanes  3030  (e.g., mapping physical registers to logical registers to be used during execution) for the various microthreads. An ALU reservation station  3010  then dispatches uops to free ALU/TALU execution resources and a memory reservation station  3020  dispatches uops for memory operations (e.g., load/store operations). 
     The embodiments of the invention described below detect and convey GIOs to the execution circuitry and provide hardware mechanisms to complete GIOs with minimal resource consumption. In particular, these embodiments: 
     (i) classify the types of GIOs; 
     (ii) detect GIOs statically or dynamically; 
     (iii) convey GIOs to the execution hardware; and 
     (iv) include circuitry to minimally complete GIOs. 
     (i) Classifying the Types of GIOs 
     There are two dimensions along which GIOs can be classified. The first dimension of classification is based on the condition of the invariance. For example, an instruction may be an Always Invariant Operation (AIO) or only a Conditionally Invariant Operation (CIO). An AIO always performs the same work across all the uthreads (i.e., every time that instruction is encountered, such as when part of a loop). However, a CIO does the same work across uthreads only when a certain condition is met. 
     The following code snippet of a 2D OpenCL application includes AIOs and CIOs: 
                                __kernel void sgemm_knh(__global float *C, __ global float *A,          __global float *B, int n, int mm, int _k) {       1: const int m = 16 * I_BLK;       2: int ii = get_global_id(0);       3: int i = ii * I_BLK;       4: int j = get_global id(1);       . . .        for (int k = 0; k &lt; _k; k++) {         float vb = B[k * m +j];         NUM_OPS(DOFMA)        }        NUM_OPS(STOREC);                    
The operation in Line  1  generates the same m value across all uthreads because the operation does not depend on any variables that are different across different uthreads (i.e., the outcome depends only on thread-invariant variables). We refer this operation as an AIO.
 
     In contrast, the operation in Line  3  depends on the x-dimension thread index (i.e., get_global_id( 0 )). This operation will generate different values among different uthreads within a Gang. Across Gangs, however, if the x-dimension thread block size is less than or equal to the Gang size, every Gang produces the same value for each corresponding thread since each thread sees the same ii value. As such, Line  3  becomes a GIO. However, if the x-dimension thread block size is greater than the Gang size, the threads in different Gangs running on the same lane will have different ii values. Line  3  in this case is not a GIO. Because it is sometimes Gang invariant and sometimes not, this operation is a Conditionally Invariant Operation (CIO). 
     The second dimension of classification is from a hardware perspective taking lanes into account and consists of the following types: (a) Within Lane Across Gangs; and (b) Across Lanes Across Gangs. 
     An example of invariance that is within Lane and across Gangs is found when a Matrix Multiplication (A*B=C) is implemented in SPMD. In this implementation, each lane loads a different B matrix tile, as shown in  FIG. 29A  (e.g., in response to load uops dispatched by MEM RS  3020 ). A single A Tile is broadcast to all the lanes. This A Tile is multiplied with the different B Tiles in each Lane to produce different C Tiles as illustrated. 
     Multiple Gangs may also cooperate to get the same matrix multiplication done in an efficient manner. To do, so, a second Gang fetches a different A Tile, multiplies with the same B Tile as the first Gang and produces a different B Tile. The new A Tile and C Tile operated by the second Gang are shown as the shaded boxes in  FIG. 29B . In one embodiment, to achieve this, the same B Tiles are needed in respective lanes for both Gangs  1  and  2 . Since Gangs  1  and  2  have separate register files, instead of having separate loads bringing in the same B Tiles twice for the two Gangs, the same loads can bring the B Tiles once and deposit them into the register files of both Gangs. 
     ii) Detecting GIOs Statically or Dynamically 
     In one embodiment, GIOs are identified based on both compiler analysis and runtime analysis performed by the dynamic GIO detection circuitry  3005 . All types of invariance (AIO or CIO) are detected statically during the compilation phase, and AIOs are always treated as GIOs. However, in one embodiment, CIOs are evaluated as GIOs (or not) by the dynamic GIO detection circuitry  3005  depending on the information at kernel launch and from feedback from the execution lanes  3030 . 
     To identify GIOs, the compiler first identifies intrinsic thread-invariant values (AIO) in the SIMT programming model. For example, constant values, kernel parameters, thread block dimensions are the same across different threads in a thread block. The compiler then identifies intrinsic conditionally-invariant variables (CIO). For example, in the current Gang mapping scheme, these are thread index functions/registers (e.g., get_global_id( 0 ) or threadIdx.x). 
     After marking the initial AIO and CIO information, the compiler generates a program dependence graph, portions of which may be propagated the information through registers and instructions. At each instruction/uop, the destination operand is assigned to a stricter invariance definition from source operands; for example, if the source operands are AIO and CIO, the destination operand is assigned as CIO. In one embodiment, the information propagation will be performed in an iterative manner until the type of invariance does not change for each instruction. After this phase, all static instructions are classified with AIO, CIO or NIO (No Invariant Operation). 
     As previously discussed, CIOs can only become GIOs at runtime (e.g., based on the kernel&#39;s thread block size). In one embodiment, when the dynamic GIO detection circuitry  3005  detects that the number of microthreads is below a threshold, it converts CIOs to GIOs. For example, in one implementation, if the number of uthreads in the x-dimension is less than the Gang size, the dynamic GIO detection circuitry converts the CIOs to GIOs. In one embodiment, if no such triggering condition is detected, the dynamic GIO detection circuitry  3005  treats CIOs as regular SIMT operations with no invariance. However, the exact condition can be changed depending on the architectural definition. 
     iii) Convey GIOs to the Execution Circuitry 
     GIOs can be conveyed to the execution hardware in the lanes  3030  by assigning instruction prefixes or utilizing instruction control codes. For example, in an ISA that has instruction prefixes (e.g., x86), one prefix such as 0XF1 can be assigned the value of Conditionally Invariant Operation Prefix. Additionally, if the invariant operation identified was the memory operand of an x86 ModR/M byte, for example, the invariant nature of the implied load can be encoded in the reserved values of the segment register field (0x6 and 0x7). In an ISA that has control codes, control code fields may be used to convey the same information. 
     iv) Minimal Completion of GIOs 
     There are multiple ways to implement GIOs in hardware. In one embodiment, a Loop Stream Detector (LSD)  3008  associated with the IDQ  1305  includes circuitry to implement semi-lockstep gang execution. If it is determined that one or more gangs are executing the same IPs, the gangs will share entries in the IDQ  1305  which will stream uops for each gang to the backend. In one implementation, the gang selection circuitry of the front end  1307  (e.g., the gang scheduler  1301 ) will round robin between gangs and attempt to allocate uops from each gang such that no gang tries to allocate past the current shared gang stream before all gangs have allocated all the uops in the stream. 
     In one embodiment, hardware support to manage invariance within a lane and across gangs includes register file design for writing the results of a load into the register files of multiple Gangs. In one embodiment, this is accomplished by placing the same register ID of multiple gangs adjacent to each other and performing a wide broadcast-type write into all the Gangs&#39; register files simultaneously. 
     When a gang invariant operation is detected by the dynamic GIO detection circuitry  3005 , it marks the uop with the invariant portion (pdst, load-op, or load-op+pdst). In one embodiment, the front end  1307  reads these uop bits and forces other gangs to be picked when the next uop to be allocated is an invariant op. When all gangs have allocated the uops immediately before the invariant uop, then the front end  1307  allocates the invariant uop. A shared execution of an invariant uop is permitted when all gangs participating have allocated the uops immediately prior to the invariant uop. In this way, hazards are prevented. 
     In one embodiment, hardware register resources are dedicated to values produced by gang invariant operations. Execution of a GIO results in a value which is written to this dedicated state, and the front-end  1307  is notified by a broadcast that this particular GIO value is stored within the machine. Each scheduling or allocation decision checks to see if it is a GIO whose value has been successfully produced by some other thread within this lane, and the operation may be cancelled before dispatch if this test succeeds. In one embodiment, redundant operations are eliminated by the front end  1307 . A table of IPs may be used to track unique GIO producers in the backend of the machine and release the physical register when all threads within a lane no longer have the value produced by the GIO visible. 
     A method in accordance with one embodiment of the invention is illustrated in  FIG. 31 . The method may be implemented on the various processor and system architectures described above, but is not limited to any particular architecture. 
     At  3101  macroinstructions of one or more threads are decoded into microthreads comprising microoperations. At  3102  always invariant operations (AIOs) and conditionally invariant operations (CIOs) are identified. For example, an indication of the type of operation may be encoded in or otherwise associated with each uop. At  3103 , each APO is scheduled to limit its execution to one lane or some subset of lanes. 
     At  3104 , for each CIO, a determination is made as to whether the CIO is gang invariant. For example, an evaluation of current variables may be performed to determine whether the CIO under the current set of conditions will be gang invariant. If not, then at  3105  the CIO is scheduled for execution across lanes as a non-invariant operation. If so, then at  3106  the CIO is scheduled for execution across one or more lanes as a gang invariant operation. 
     Apparatus and Method for a High Throughput Parallel Co-Processor and Interconnect With Low Offload Latency 
     As mentioned above with respect to  FIG. 14C , a data parallel cluster  1300  may be coupled to the cores  1401   a - b  of a central processing unit (CPU) in a co-processor/accelerator arrangement, over a high-speed cache coherent interface  1496  (the terms “co-processor” and “accelerator” will be used interchangeably herein). Various coherent co-processor/accelerator interfaces are in use today including, for example, NVLink, Open Coherent Accelerator Processor Interface (OpenCAPI), Cache Coherent Interconnect for Accelerators (CCIA), and UltraPath Interconnect. Each interface includes mechanisms to dispatch work to a co-processor device and techniques to protect the coherency of the data shared between the CPU and the co-processor device. 
     One key limiter in the offload of data parallel problems from the CPU to accelerator devices is the transfer latency. Embodiments of the invention provide a scalable solution by implementing heterogeneous hardware at two different optimization points and transparently moving the offloaded execution between the two different hardware units. While the embodiments described below focus on the interaction between a data parallel cluster and host processor, the underlying principles of the invention are not limited to any specific type of accelerator device. 
     One embodiment of the invention includes circuitry and logic for expressing the data parallel work between hardware units such as a host processor and an accelerator device. One embodiment includes instructions for offloading parallel work from a processor which do not specify the execution resources employed. In addition, specialized instructions may be used within the parallel execution resources that distribute execution across a plurality of processing elements and/or lanes. A software mechanism may also be implemented for expressing parallel work (e.g., such as may be embodied in a compiler which is flexible in the parallel execution resources used). 
       FIG. 32  illustrates a specific implementation in which a DPC controller  3200  integrated within the host processor or core  3201  (hereinafter “processor  3201 ”) manages the power and occupancy signals for adjusting the power states of different execution resources within the DPC  1300  (e.g., determining which execution resources to keep active). In the illustrated embodiment, a host/DPC communication channel  1350  connects the processor  3201  to the DPC  1300 . In addition,  FIG. 32  illustrates an embodiment in which both the processor  3201  and DPC  1300  include independent memory controllers,  3205  and  3210 , respectively, for coupling each device to system memory  1460 . 
     In one embodiment, the DPC controller  3200  adjusts the number of concurrent execution resources for a parallel task offloaded from the processor  3201  to the execution lanes  3030  of the DPC  1300  based on different variables and components. For example, the DPC controller  3200  may determine the most efficient schedule for parallel tasks on the DPC  1300 , based on signals that indicate the power consumed by dispatched parallel work and the breadth of parallel work yet to be dispatched, for each lane  3030 . It evaluates these signals to determine whether to suspend further execution of parallel work in one or more execution units within one or more lanes  3030  and/or migrate work to one or more different execution units or lanes  3030 . For example, in the specific architectures described herein, the DPC controller  3200  may reallocate work from one or more ALUs  1350  and/or TALUs  1340  to different ALUs/TALUs, potentially in a different lane  1310 , based on the current/anticipated processing requirements and the overall system power budget. 
     The accelerator device may include one or more parallel hardware units optimized for different design points. For example, the design points may include frequency, energy efficiency, total amount of execution state, available memory bus bandwidth, and available microarchitectural resources such as ALUs  1350  and TALUs  1340 . 
     In one embodiment, the host processor  3201  executes an application which includes parallel program code  3271 . When the application  3270  is started up, the instruction processing pipeline of the processor  3201  executes the primary application thread. In particular, instructions of the thread are passed from the memory controller  3205  to the I-cache  1410  and/or fetch unit  1418 , decoded by the decoder  1409  and executed by the execution circuitry  1408 . The decoder  1409  and/or execution circuitry  1408  detects when a sequence of instructions in the primary thread are designed to be executed on the DPC  1300 , the decoder  1409  and/or execution circuitry  1408  forwards these instructions to the DPC controller  3200  which initiates execution on the DPC lanes  3030 . 
     The DPC controller  3200  may initially configure the DPC cluster  1300  by passing initial values such as the thread context identifier, the number of active threads, and the number of loop iterations to the lanes  3030  of the DPC  1300 , either directly or via the DPC FE  1307 . In one embodiment, the DPC controller  3200  then passes an address pointer to the parallel program code  3271  over the host/DPC channel  1350 . The DPC FE  1307  begins fetching instructions from this address pointer and scheduling the instructions for execution in parallel across the lanes  3030 . In this embodiment, the instructions of the parallel program code  3271  are fetched and decoded by fetch/decode circuitry  3202  within the DPC FE  1307 . However, in other embodiments, the parallel program code  3271  is decoded by the host processor  3201  and stored to memory  1460  or transmitted over the host/DPC channel  1350 . Results  3272  of the parallel execution on the lanes are stored back to a designated region in memory  3272  which is accessible by the processor  3201  (e.g., so that it the primary thread and/or other threads can access the data). 
     In one embodiment, the host processor  3201  performs other operations to support the DPC  1300  such as allocating in-memory stacks for the microthreads/uops and pushing the pointer to the base of the stack(s) and the stack size to the DPC  1300 . These stacks may then be used by the lanes  3030  when executing the microthreads. In addition, the host processor  3201  may allocate in-memory thread-local storage for certain programming models. 
     In one embodiment, if the host processor  3201  detects that the execution regime is not suited to the currently executing execution resources of the lanes, it may implement a transfer of the current parallel program code  3271  to a different unit (e.g., a different ALU/TALU and/or a different lane). 
     A method in accordance with one embodiment of the invention is illustrated in  FIG. 33 . The method may be implemented on the system architecture described above, but is not limited to any particular processor or system architecture. 
     At  3301 , initial values are pushed to the parallel execution accelerator. As mentioned, this may include the thread context identifier (e.g., to identify the application  3270  initiating the operations), the number of active threads, and the number of loop iterations. At  3302 , an instruction pointer is pushed to the parallel execution resources identifying a location in memory from which to execute the microthreads. In one embodiment, this region of program code is initially set up in memory by the host processor to generate the pointer; the host processor then provides the pointer to the parallel execution resources. 
     At  3303 , in-memory stacks are allocated for the various microthreads and the base pointers of the various stacks and the size of each stack are pushed to the various microthreads, thereby providing the execution resources visibility of the execution stacks for executing the microthreads. At  3304 , in memory thread-local storage is allocated (dependent on the particular programming model being used). 
     At  3305  the microthreads are executed on the parallel execution resources and results are stored. Depending on the implementation, the parallel execution resources may slice the parallel work according to the architectural scheme defined by the instructions for expressing parallel execution of a loop. In addition, at  3305  the host processor or circuitry of the parallel execution resources monitor variables related to the performance and/or power usage of the parallel execution resources. For example, the average power consumed per unit of time, instruction execution efficiency, workload on the parallel execution resources, and/or temperature readings may be collected. 
     At  3106  the performance/power variables are evaluated to determine whether the microthreads should be reallocated across the processing resources a more efficient manner. For example, if the power budget of the system is being exceeded, then processing resources may be reallocated to reduce power consumption. Conversely, if a particular performance metric is not being met, then the processing resources may be allocated to raise performance. Different power/performance policies may be implemented for different systems. If a reallocation decision is made, then one or more microthreads are reallocated to different execution resources at  3107 . 
     In one embodiment, if it is determined that the execution regime of the currently active parallel procedure would be better executed on different resources, the controller may signal to the active parallel execution resources that the number of active threads is different and/or may signal that the next thread contexts are null (e.g., to cause the active parallel execution resources to terminate execution). In either case, the code executing on the active execution resources may finish a number of loop iterations at defined architectural points specified by the compiler (e.g., inserted into the control flow graph). Thus, thread contexts do not need to be saved by a large collection of parallel execution resources and transmitted, potentially at large cost, to a different location on- or off-chip. Only a small amount of state is transmitted, keeping the transition latency low. 
     One embodiment of the invention includes a set of instructions to access and manage parallel processing resources. Table A below specifies a particular set of instructions and includes an indication as to whether the instructions are to be executed on the host processor or on the parallel processing device. 
     
       
         
           
               
               
               
             
               
                 TABLE A 
               
               
                   
               
               
                   
                 Valid 
                   
               
               
                 Instructions for Managing Heterogeneous Parallel 
                 on 
                 Valid on 
               
               
                 Task Offload 
                 host 
                 device 
               
               
                   
               
             
            
               
                 Parallel procedure call (PCALL)-Execute parallel 
                 True 
                 True 
               
               
                 procedure at memory location with a number of 
                   
                   
               
               
                 iterations specified. Results are stored in memory, 
                   
                   
               
               
                 potentially at a location specified by a control structure. 
                   
                   
               
               
                 Parallel procedure return (PRET)-Cease execution of 
                 False 
                 True 
               
               
                 a parallel procedure. A signal can be sent to the control 
                   
                   
               
               
                 unit to indicate this resource is available. 
                   
                   
               
               
                 Parallel procedure thread context (TCONTEXT)- 
                 False 
                 True 
               
               
                 Returns an identifier that provides a unique slice into 
                   
                   
               
               
                 the loop iterations of the current parallel procedure. 
                   
                   
               
               
                 Number of active threads (TOCCUPANCY)-Returns 
                 False 
                 True 
               
               
                 an unsigned integer which indicates the number of 
                   
                   
               
               
                 concurrently executing resources. Used to calculate the 
                   
                   
               
               
                 next slice into the parallel procedure call&#39;s loop 
                   
                   
               
               
                 iterations. 
                   
                   
               
               
                 Next thread context (INCCONTEXT)-Returns an 
                 False 
                 True 
               
               
                 identifier which indicates the next unique slice into the 
                   
                   
               
               
                 loop iterations of the current parallel procedure. Can 
                   
                   
               
               
                 return a null identifier. 
               
               
                   
               
               
                 Any of these instructions may reference the depth of nested parallel procedure calls to get a unique slice at each level of parallel procedure call. 
               
            
           
         
       
     
     In this embodiment, the host processor executes the PCALL instruction to initiate a parallel procedure call on the parallel execution resources. The parallel procedure call identifies a memory location/pointer from which the parallel execution resources are to execute the parallel program code as well as a number of iterations to be performed. Results are stored in memory, potentially at a location specified by a control structure. In the embodiment shown in  FIG. 32 , for example, the memory pointer to the parallel program code  3271  is transmitted over the host/DPC channel  1350  and the results  3272  are stored in a memory location specified by the host processor  3201  (e.g., a memory region allocated to the DPC  1300  by the host processor  3201  or the memory subsystem). 
     The remaining instructions listed in Table A are executed by the parallel execution resources. In particular, when execution is complete and results generated, the parallel execution resources may execute a parallel procedure return (PRET) instruction, which signals the controller that processing is complete (and therefore that this execution resource is available). 
     The parallel procedure thread context instruction (TCONTEXT) returns an identifier that provides a unique slice into the loop iterations of the current parallel procedure. For example, TCONTEXT may indicate the amount of work performed by the parallel execution resources. 
     The number of active threads instruction (TOCCUPANCY) returns a value indicating the number of concurrently executing resources and may be used (e.g., by the host processor  3201 ) to calculate the next slice into the parallel procedure call&#39;s loop iterations. 
     The next thread context instruction (INCCONTEXT) returns an identifier which indicates the next unique slice into the loop iterations of the current parallel procedure. In one embodiment, it can return a null identifier. 
     In one embodiment, the parallel program code  3721  includes a general Turing-complete compute instruction set augmented with the instructions highlighted above. The iterations of a loop without dependencies between loop iterations can be expressed in a concurrency-independent machine representation that can be directly executed by a compatible parallel accelerator device without any intervening steps. The state of any particular hardware context is implied by the state of the parallel execution resources set up by the controller (e.g., DPC controller  3200 ), rather than expressly defined statically in the offload instruction specification. 
     In one embodiment, while executing the program code identified by the parallel procedure call, the parallel execution resources receive values resulting from the instructions in Table A and use them to map different loop iterations to execution resources according to the scheme detailed in  FIG. 34 . With respect to the DPC embodiment, for example, the DPC controller  3200 , the parallel program code  3271 , and the instructions listed above are combined to determine which loop iteration is currently being executed by each hardware context inside the DPC lanes  3030 . 
     In  FIG. 34 , a parent thread  3401  (e.g., executed on the host processor) executes a parallel procedure call (PCALL) identifying a particular set of loop iterations  3400  to be executed. In response to the PCALL instruction, the loop iterations  3400  are scheduled and executed across two different parallel execution resources  3407  (e.g., such as the lanes discussed above). When execution is complete, each parallel execution thread (sometimes referred to above as a microthread) executes a parallel procedure return instruction to notify the parent thread  3401  that execution is complete. 
     Another example of how instructions can dynamically map to available execution resources is provided in  FIG. 35 . This example is based on the DPC architecture described above. Consider a copy loop trying to move a buffer of N elements from some memory address x to some memory address y. 
                                                    for(int i=0; i&lt;n; i++) {                y[i] = x[i];               }                        
In this example, the available parallel execution resources consist of two lanes of one hardware context each, for a total of two active threads in the DPC  1300 .
 
     The parallel procedure executed by each thread is identical. The thread context instruction provides an offset into the input array that is independent from the other iterations of the loop. Then, the next thread context instruction provides an increment of the induction variable i of the loop. The compiler inserts a comparison to make certain that the returned identifier is not null and execution has not finished. The active thread then executes a different loop context. Each thread is responsible for pulling any state required, such as addresses of input and output. This is designed to reduce the amount of data transferred in dispatching new parallel work to parallel execution resources. 
     The next thread context is dependent on the current thread context, the total active threads, and the number of loop iterations. As the number of active threads changes due to the hardware control unit making decisions about which parallel execution resources are participating in this parallel procedure call. This information, as expressed via the new instructions, is sufficient to locate a hardware context&#39;s iteration within the larger collection of active parallel execution resources for this loop. 
     Embodiments of the invention may encode concurrency requirements, for example by a control register that indicates how many threads are available to execute concurrently, to support synchronization between loop iterations to express dependencies such as compare-and-swap or barrier. Optionally, an implementation may perform a context switch to support synchronization between loop iterations that are expected to execute concurrently but are mapped to fewer hardware contexts. Or, instead, an implementation could make the parallel procedure call with an instruction which will fail due to insufficient available execution resources and require the host thread to use alternate code paths with fewer threads required for concurrent operation. 
     In the foregoing specification, the embodiments of invention have been described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. 
     Components, features, and details described for any of the apparatus may also optionally apply to any of the methods, which in embodiments may be performed by and/or with such apparatus. Any of the processors described herein may be included in any of the systems disclosed herein. In some embodiments, the computer system may include an interconnect, a processor coupled with the interconnect, and a dynamic random access memory (DRAM) coupled with the interconnect. Alternatively, instead of DRAM, other types of volatile memory that don&#39;t need to be refreshed may be used, or flash memory may be used. 
     In the description and claims, the terms “coupled” and/or “connected,” along with their derivatives, may have be used. These terms are not intended as synonyms for each other. Rather, in embodiments, “connected” may be used to indicate that two or more elements are in direct physical and/or electrical contact with each other. “Coupled” may mean that two or more elements are in direct physical and/or electrical contact with each other. However, “coupled” may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. For example, an execution unit may be coupled with a register and/or a decode unit through one or more intervening components. In the figures, arrows are used to show connections and couplings. 
     The term “and/or” may have been used. As used herein, the term “and/or” means one or the other or both (e.g., A and/or B means A or B or both A and B). 
     In the description above, specific details have been set forth in order to provide a thorough understanding of the embodiments. However, other embodiments may be practiced without some of these specific details. The scope of the invention is not to be determined by the specific examples provided above, but only by the claims below. In other instances, well-known circuits, structures, devices, and operations have been shown in block diagram form and/or without detail in order to avoid obscuring the understanding of the description. Where considered appropriate, reference numerals, or terminal portions of reference numerals, have been repeated among the figures to indicate corresponding or analogous elements, which may optionally have similar or the same characteristics, unless specified or clearly apparent otherwise. 
     Certain operations may be performed by hardware components, or may be embodied in machine-executable or circuit-executable instructions, that may be used to cause and/or result in a machine, circuit, or hardware component (e.g., a processor, portion of a processor, circuit, etc.) programmed with the instructions performing the operations. The operations may also optionally be performed by a combination of hardware and software. A processor, machine, circuit, or hardware may include specific or particular circuitry or other logic (e.g., hardware potentially combined with firmware and/or software) is operative to execute and/or process the instruction and store a result in response to the instruction. 
     Some embodiments include an article of manufacture (e.g., a computer program product) that includes a machine-readable medium. The medium may include a mechanism that provides, for example stores, information in a form that is readable by the machine. The machine-readable medium may provide, or have stored thereon, an instruction or sequence of instructions, that if and/or when executed by a machine are operative to cause the machine to perform and/or result in the machine performing one or operations, methods, or techniques disclosed herein. 
     In some embodiments, the machine-readable medium may include a non-transitory machine-readable storage medium. For example, the non-transitory machine-readable storage medium may include a floppy diskette, an optical storage medium, an optical disk, an optical data storage device, a CD-ROM, a magnetic disk, a magneto-optical disk, a read only memory (ROM), a programmable ROM (PROM), an erasable-and-programmable ROM (EPROM), an electrically-erasable-and-programmable ROM (EEPROM), a random access memory (RAM), a static-RAM (SRAM), a dynamic-RAM (DRAM), a Flash memory, a phase-change memory, a phase-change data storage material, a non-volatile memory, a non-volatile data storage device, a non-transitory memory, a non-transitory data storage device, or the like. The non-transitory machine-readable storage medium does not consist of a transitory propagated signal. In some embodiments, the storage medium may include a tangible medium that includes solid matter. 
     Examples of suitable machines include, but are not limited to, a general-purpose processor, a special-purpose processor, a digital logic circuit, an integrated circuit, or the like. Still other examples of suitable machines include a computer system or other electronic device that includes a processor, a digital logic circuit, or an integrated circuit. Examples of such computer systems or electronic devices include, but are not limited to, desktop computers, laptop computers, notebook computers, tablet computers, netbooks, smartphones, cellular phones, servers, network devices (e.g., routers and switches.), Mobile Internet devices (MIDs), media players, smart televisions, nettops, set-top boxes, and video game controllers. 
     Reference throughout this specification to “one embodiment,” “an embodiment,” “one or more embodiments,” “some embodiments,” for example, indicates that a particular feature may be included in the practice of the invention but is not necessarily required to be. Similarly, in the description 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 various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of the invention. 
     EXAMPLES 
     The following are example implementations of different embodiments of the invention. 
     Example 1 
     A processor comprising: instruction fetch circuitry to fetch instructions of one or more primary threads; a decoder to decode the instructions to generate uops; a data parallel cluster (DPC) to execute microthreads comprising a subset of the uops, the DPC further comprising: a plurality of execution lanes to perform parallel execution of the microthreads; an instruction decode queue (IDQ) to store the uops prior to execution; and a scheduler to evaluate the microthreads based on associated variables including instruction pointer (IP) values, the scheduler to gang microthreads into fragments for parallel execution on the execution lanes based on the evaluation. 
     Example 2 
     The processor of example 1 wherein the scheduler is to gang the microthreads into fragments based on IP values to induce microthread convergence. 
     Example 3 
     The processor of example 1 wherein a fragment comprises a collection of associated microthreads. 
     Example 4 
     The processor of example 2 further comprising: reconvergence circuitry to be used by the scheduler to determine an order in which to execute the fragments, the reconvergence circuitry comprising a data structure to store variables associated with each fragment. 
     Example 5 
     The processor of example 4 wherein the reconvergence circuitry is configured to generate a signal to identify a next fragment to be executed based on a comparison of the variables of all fragments. 
     Example 6 
     The processor of example 5 wherein the comparison comprises a comparison of the IP values of the fragments and wherein the fragment having a minimum IP value is to be selected for execution by execution lanes. 
     Example 7 
     The processor of example 1 wherein the DPC further comprises: mask storage to store an execution mask having at least one value associated with each parallel execution lane. 
     Example 8 
     The processor of example 7 wherein the DPC is to enable or disable execution lanes for executing each fragment or microthread based on the values associated with the lanes. 
     Example 9 
     The processor of example 8 wherein the execution mask is to be updated dynamically for each fragment or microthread, thereby enabling a number of lanes required to execute the fragment or microthread. 
     Example 10 
     The processor of example 1 wherein the DPC further comprises: a data cache to store data to be used to execute the fragments; a translation lookaside buffer (TLB) to store virtual-to-physical address translations for accessing system memory. 
     Example 11 
     The processor of example 1 wherein each lane of the DPC further comprises: a register file to store data associated with an executing fragment; a tensor arithmetic logic unit (TALU) to process tensor data associated with an executing fragment; and an address generation unit to generate addresses required to execute each fragment. 
     Example 12 
     A method comprising: fetching instructions of one or more primary threads; decoding the instructions to generate uops; identifying microthreads comprising a subset of the uops; evaluating the microthreads based on associated variables including instruction pointer (IP) values; and ganging the microthreads into fragments for parallel execution on a plurality of parallel execution lanes based on the evaluation. 
     Example 13 
     The method of example 12 wherein the microthreads are ganged into fragments based on the IP values to induce microthread convergence. 
     Example 14 
     The method of example 12 wherein a fragment comprises a collection of associated microthreads. 
     Example 15 
     The method of example 13 further comprising: determining an order in which to execute the fragments using a data structure storing variables associated with each fragment. 
     Example 16 
     The method of example 15 further comprising: generating a signal to identify a next fragment to be executed based on a comparison of the variables of all fragments. 
     Example 17 
     The method of example 16 wherein the comparison comprises a comparison of the IP values of the fragments and wherein the fragment having a minimum IP value is to be selected for execution on the parallel execution lanes. 
     Example 18 
     The method of example 12 further comprising: 
     storing an execution mask having at least one value associated with each of the parallel execution lanes. 
     Example 19 
     The method of example 18 further comprising: enabling or disabling execution lanes for executing each fragment or microthread based on the values associated with the lanes. 
     Example 20 
     The method of example 19 further comprising: dynamically updating the execution mask for each fragment or microthread, thereby enabling a specified number of lanes required to execute the fragment or microthread. 
     Example 21 
     A machine readable medium having program code stored thereon which, when executed by a machine, causes the machine to perform the operations of: fetching instructions of one or more primary threads; decoding the instructions to generate uops; identifying microthreads comprising a subset of the uops; evaluating the microthreads based on associated variables including instruction pointer (IP) values; and ganging the microthreads into fragments for parallel execution on a plurality of parallel execution lanes based on the evaluation. 
     Example 22 
     The machine-readable medium of example 21 wherein the microthreads are ganged into fragments based on the IP values to induce microthread convergence. 
     Example 23 
     The machine-readable medium of example 21 wherein a fragment comprises a collection of associated microthreads. 
     Example 24 
     The machine-readable medium of example 22 further comprising program code to cause the machine to perform the operation of: determining an order in which to execute the fragments using a data structure storing variables associated with each fragment. 
     Example 25 
     The machine-readable medium of example 24 further comprising program code to cause the machine to perform the operation of: generating a signal to identify a next fragment to be executed based on a comparison of the variables of all fragments. 
     Example 26 
     The machine-readable medium of example 25 wherein the comparison comprises a comparison of the IP values of the fragments and wherein the fragment having a minimum IP value is to be selected for execution on the parallel execution lanes. 
     Example 27 
     The machine-readable medium of example 21 further comprising program code to cause the machine to perform the operation of: storing an execution mask having at least one value associated with each of the parallel execution lanes. 
     Example 28 
     The machine-readable medium of example 27 further comprising program code to cause the machine to perform the operation of: enabling or disabling execution lanes for executing each fragment or microthread based on the values associated with the lanes. 
     Example 29 
     The machine-readable medium of example 28 further comprising program code to cause the machine to perform the operation of: dynamically updating the execution mask for each fragment or microthread, thereby enabling a specified number of lanes required to execute the fragment or microthread. 
     Embodiments of the invention may include various steps, which have been described above. The steps may be embodied in machine-executable instructions which may be used to cause a general-purpose or special-purpose processor to perform the steps. Alternatively, these steps may be performed by specific hardware components that contain hardwired logic for performing the steps, or by any combination of programmed computer components and custom hardware components. 
     As described herein, instructions may refer to specific configurations of hardware such as application specific integrated circuits (ASICs) configured to perform certain operations or having a predetermined functionality or software instructions stored in memory embodied in a non-transitory computer readable medium. Thus, the techniques shown in the Figures can be implemented using code and data stored and executed on one or more electronic devices (e.g., an end station, a network element, etc.). Such electronic devices store and communicate (internally and/or with other electronic devices over a network) code and data using computer machine-readable media, such as non-transitory computer machine-readable storage media (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer machine-readable communication media (e.g., electrical, optical, acoustical or other form of propagated signals—such as carrier waves, infrared signals, digital signals, etc.). In addition, such electronic devices typically include a set of one or more processors coupled to one or more other components, such as one or more storage devices (non-transitory machine-readable storage media), user input/output devices (e.g., a keyboard, a touchscreen, and/or a display), and network connections. The coupling of the set of processors and other components is typically through one or more busses and bridges (also termed as bus controllers). The storage device and signals carrying the network traffic respectively represent one or more machine-readable storage media and machine-readable communication media. Thus, the storage device of a given electronic device typically stores code and/or data for execution on the set of one or more processors of that electronic device. Of course, one or more parts of an embodiment of the invention may be implemented using different combinations of software, firmware, and/or hardware. 
     Throughout this detailed description, for the purposes of explanation, numerous specific details were set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced without some of these specific details. In certain instances, well known structures and functions were not described in elaborate detail in order to avoid obscuring the subject matter of the present invention. Accordingly, the scope and spirit of the invention should be judged in terms of the claims which follow.