Patent Publication Number: US-9417929-B2

Title: Runtime profile determinations of virtual machines for graphics processing unit (GPU) allocation

Description:
TECHNICAL FIELD 
     The present disclosure relates to methods, techniques, and systems for virtualizing graphics processing units and, in particular, to methods, techniques, and systems for efficiently allocating graphics processing units dynamically based upon virtual machine workload. 
     BACKGROUND 
     A graphics processing unit (GPU) can be a useful resource to accomplish activities such as accelerating three dimensional (3D) graphics and video processing due to the parallel computing nature of these tasks. More specifically, the highly parallel structure of a GPU makes it more effective than a general-purpose CPU for algorithms that process large blocks of data in parallel such as 3D graphics that involve, for example, transforms, lighting, textures, shading, etc. In computing systems that contain both a computer processor unit (CPU) and a GPU, computations such as 3D graphics or motion compensation performed for video decoding, that are simple, repetitive, high throughput, and not as latency sensitive can be offloaded to a GPU in order to provide faster computations and leave more room for other tasks to be processed by the CPU. 
     A typical computing system is expected to have a much larger number of CPU cores than GPUs. GPUs come in a variety of forms. A GPU might be integrated with the CPU on the same chip and share system memory or may be made available on a separate card connected to the CPU and memory through, for example, a PCI (Peripheral Controller Interface) bus. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example virtualization server computing system for executing example embodiments of a dynamic GPU allocation system. 
         FIG. 2  is an example block diagram of components of an example dynamic GPU allocation system. 
         FIG. 3  is an example flow diagram of an overview of operations of an example dynamic GPU allocation system. 
         FIG. 4  is a block diagram of an example GPU allocation list (GAL) used with an example dynamic GPU allocation system. 
         FIG. 5  depicts an example allocation of GPUs among an example set of virtual machines. 
         FIGS. 6A and 6B  depict a flow diagram of example logic for managing a GPU allocation list and allocating GPU resources based upon the list. 
         FIG. 7  is a flow diagram of example logic for determining a runtime profile of a designated virtual machine. 
         FIG. 8  is a flow diagram of example logic for handling events that cause a GPU benefit factor (GBF) to be set or changed for a designated virtual machine. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments described herein provide enhanced computer- and network-based methods, techniques, and systems for dynamically allocating resources of physical graphics processing units among virtual machines. Example embodiments provide a dynamic GPU allocation system (DGAS), which enables efficient allocation of physical GPU resources to one or more virtual machines. 
     In overview, the DGAS associates each virtual machine with a GPU benefit factor (a GBF), which is a measurement of the potential benefit a virtual machine may attain from use of one or more GPUs. The DGAS then uses the GBFs assigned to the virtual machines to allocate physical GPU resources as discussed in detail below to a set of those VMs that would seem to gain the greatest benefit. For example, a higher GBF may be assigned to a virtual machine that processes true 3D graphics than a virtual machine that processes mostly 2D graphics or video. Each virtual machine may also be associated with a statically determined (predetermined, designated, pre-assigned, etc.) priority, set for example by an administrator-user or the like. In at least one embodiment, this static priority is used to override the GPU resource allocation that would otherwise occur by the DGAS just taking into account the GBF. The administrator-user can thereby insure that physical GPU resources are allocated to particular users or virtual machines, such as a computer-aided design engineer, mechanical project manager, or architect, who may need such resources to run 3D design applications, for example, as part of his or her job. 
     In example embodiments, the DGAS comprises virtualization logic running on one or more server computing systems that computes GPU benefit factors for the virtual machines on a dynamic basis, and combines the computed GBFs with the corresponding static priorities of the virtual machines to determine an initial ranked ordering of virtual machines. In some embodiments, a runtime profile of each virtual machine is used to further adjust its GBF and the ranked ordering of the virtual machine adjusted accordingly. The runtime profile provides some measurement of GPU usage effectiveness based upon the workload running on a virtual machine, such as based upon video memory usage, load of the command queue, bus bandwidth usage and/or other measurement. Accordingly, a runtime profile can provide a type of “tie-breaker” when the GBF and priority of two or more virtual machines result in the same ranking. In some embodiments, a data structure such as a GPU allocation list (GAL) is used to manage and maintain the rank ordering of the virtual machines for allocation purposes. 
     Once the VMs are ranked according to their GBFs and potentially priorities and runtime profiles, the available hardware GPU resources of the one or more server computing systems are then allocated by, for example, by GPU allocation logic, to some subset of these virtual machines in rank order, the subset determined as those virtual machines that will fit on the GPUs, for example based upon matching physical GPU capacity (e.g., video memory availability) with the requirements of the subset of virtual machines (e.g., video memory requirements). Hardware GPU resources are then assigned to the determined subset of virtual machines commensurate with the (physical) GPU resource allocations. Because the GBFs are determined (e.g., computed, calculated, received, etc.) and adjusted dynamically (e.g., while a current subset of VMs is running on the server computing systems), the allocation of GPU resources can vary over time, resulting in better utilization of GPU resources, and hence a more optimally performing and well balanced system. Although allocation is described herein based upon a rank ordering of virtual machines, in some embodiments, allocation is based upon a comparison of requirements with some or no regard to ranking (for example, based upon a first-come, first-serve approach). 
       FIG. 1  is a block diagram of an example virtualization server computing system for executing example embodiments of a dynamic GPU allocation system. Virtualization server computing system  100  may be configured on a personal computer, a laptop, or server (host) hardware platform  101 , such as an x86 architecture platform. Note that a general purpose or a special purpose computing system suitably instructed may be used to implement the virtualization server computing system  100 . The virtualization server computing system  100  may comprise one or more server computing systems and may span distributed locations. In addition, each block shown may represent one or more such blocks as appropriate to a specific embodiment or may be combined with other blocks. 
     In the embodiment illustrated, host hardware platform  101  may comprise a computer memory  102 , one or more central processing units (“CPU”)  103 , a frame buffer (“FB”)  104 , and one or more network connections, accessible for example via network interface card (“NIC”)  105 . In addition, the host hardware platform  101  may optionally comprise other components such as one or more displays  109 , graphics processing units (“GPU”)  108 , input/output (“I/O”) devices  111  (e.g., keyboard, mouse, CRT or LCD display, etc.), or other computer readable media  110 . 
     Virtualization logic  120  is loaded into memory  102  of host hardware platform  101  and may execute on one or more CPUs  103 . Virtualization logic  120  may alternatively be implemented in software, hardware, or firmware, or some combination thereof. Virtualization logic  120 , includes one or more virtual machine monitors (VMM)  142   a - 142   c  and VMX processes  151   a - 151   c , which can support multiple virtual machines (VM)  141   a - 141   c  that can concurrently be instantiated and executed. As used herein a “virtual machine” or VM is an abstraction representing the execution space that a guest operating system and applications (the “guest”) may execute within, such as VM  141   a - 141   c . Each virtual machine  141   a - 141   c  may include a guest operating system (guest OS), e.g., guest OSes  143   a - 143   c , and one or more corresponding applications, e.g., guest applications  144   a - 144   c , running on each respective guest OSes  143   a - 143   c . In one example embodiment, each VM, when executing, is made accessible to a different user who is remotely connected from a different client connection. The number of VMs supported by any particular host may vary, for example, based on aspects such as the hardware capabilities, virtualization logic configurations, and desired performance. Other code  161  may also execute on virtualization logic  120 . 
     Each VM  141   a - 141   c  may require virtualization of one or more aspects implemented by the virtualization logic  120  and/or the host hardware platform  101 . That is, the virtualization logic  120  may provide emulated hardware and drivers to each VM. For example, through the VMX processes  151   a - 151   c  and the VMMs  142   a - 142   c , the virtualization logic  120  may provide one or more of a virtual CPU (“VCPU”), a virtual memory (“VMEM”), virtual device drivers (“VDD”), a virtual file system and virtual disks, virtual network capabilities, and virtual graphics capabilities, such as virtual graphics adaptors drivers and command emulation, and the like. Each virtualization environment may function as an equivalent of a standard x86 hardware architecture platform such that any operating system (e.g., Microsoft Windows®, Linux®, Solaris®86, NetWare, FreeBSD, etc.) may be installed as a guest OS (e.g., guest OS  143   a - 143   c ) to execute applications in an instantiated virtual machine. Note that in other embodiments, virtualization of other hardware architectures may be supported. 
     In one embodiment, the virtualization logic  120  provides virtualized storage support through a distributed VM file system  132 , storage stack  131 , and device drivers  130  that communicate with the physical data drives  106  and  107 . In addition, the virtualization logic  120  provides virtualized network support through a virtual switch  133  and network stack  134  to communicate with NIC  105  of the host hardware platform  101 . This support may be used to provide TCP/IP connections at the virtualization logic level to connect with other systems, such as to remote user interfaces or send video to client devices. Also, the virtualization logic  120  provides virtualized graphics support through the Super Video Graphics Array (SVGA) or VGA graphics adaptor implementations which use the server graphics API  121  (such as OpenG1, Xserver implementations, etc.) to communicate with graphics drivers  122  that manage and fill frame buffer  104  of the host hardware  101  using graphics commands. Other embodiments may provide virtualized graphics support in other manners using other communications mechanisms. In some embodiments such as those described herein, the graphics capabilities of the host hardware platform  101  may be accelerated through the use of one or more GPUs  108 . Also, although many of the examples described herein are oriented to accelerating graphics and video using GPUs, in some embodiments, other types of (non-graphics) computing, for example, code written using the OpenCL framework, may also be accelerated through the use of one or more GPUs  108 . 
     In some embodiments, the virtualization execution environments are provided through both a process executing at USER (less privileged mode), referred to as the VMX process (e.g., VMX processes  151   a - 151   c ) and the VMM executing in a more privileged state (e.g., VMMs  142   a - 142   c ). Each VM  141   a - 141   c  effectively executes in the process space of its respective VMX process  151   a - 151   c  (that is its memory is mapped to each respective VMX process). A VMX process, for example processes  151   a - 151   c , may comprise an MKS (mouse, keyboard, screen) thread (e.g., thread  152   a ) for processing input and output from the respective VM, e.g., VMs  141   a - 141   c . A VMX process also includes USER mode graphics level support, such as a virtual SVGA driver  153   a . The SVGA driver  153   a  is used to send graphics commands (through the graphics API  121 ) to the graphics drivers of the virtualization logic  120 . As described herein, these commands may ultimately run on one or more GPUs  108  when the corresponding VM has been allocated one or more GPUs  108 . Each VMX process and VMM pair cooperate to provide the effective (and isolated) virtualization execution environment for each VM to run. In general operation, the virtualization logic  120  receives requests from the virtualized device drivers implemented by the VMMs and VMX processes, translates (or otherwise transfers, forwards, sends, or communicates) these requests to corresponding requests to real device drivers  130  or  122  that communicate with real devices resident in the host hardware platform  101  (such as frame buffer  104 , NIC  105 , etc.). 
     The various terms, layers, categorizations, components used to describe the virtualization server computing system  100  of  FIG. 1  may be referred to differently without departing from their functionality or the spirit of this disclosure. Also, one or more of the components may not be present in any specific implementation. For example, the virtual components shown as part of virtualization logic  120  that are not included in each VMM  142   a - 142   c  (for example, one or more of components  130 - 134 ,  121 - 122 , or the like) may be considered in other embodiments to be part of the VMMs  142   a - 142   c . In addition, in some embodiments, no VMX process is used and the MKS thread capabilities and virtual graphics adaptor support are integrated instead into the VMMs  142   a - 142   c  or into other parts of the virtualization logic  120 . Also, in some embodiments the VMMs  142   a - 142   c  may be considered to be separate from or part of the VM  141   a - 141   c . Embodiments of a DGAS may be practiced in other virtualized computing environments such as hosted virtual machine systems, where the virtualization logic  120  is implemented on top of an operating system executing on host hardware platform  101  instead of directly on the host hardware. 
     Furthermore, in some embodiments, some or all of the components of the virtualization server computing system  100  may be implemented or provided in other manners, such as at least partially in firmware and/or hardware, including, but not limited to one or more application-specific integrated circuits (ASICs), standard integrated circuits, controllers executing appropriate instructions, and including microcontrollers and/or embedded controllers, field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), and the like. Some or all of the components and/or data structures may also be stored as contents (e.g., as executable or other machine-readable software instructions or structured data) on a tangible or non-transitory computer-readable medium (e.g., a hard disk; memory; network; other computer-readable medium; or other portable media article to be read by an appropriate drive or via an appropriate connection, such as a DVD or flash memory device) such as computer readable medium  110  to enable the computer-readable medium to execute or otherwise use or provide the contents to perform at least some of the described techniques. 
       FIG. 2  is an example block diagram of components of an example dynamic GPU allocation system. In one embodiment, the dynamic GPU allocation system (DGAS) comprises one or more functional components/modules that work together to dynamically allocate physical GPU resources among a set of virtual machines as overviewed above. These components may be implemented in software, hardware, firmware, or a combination. In  FIG. 2 , the DGAS comprises server side support that resides on one or more host or server computing systems  201 . In other embodiments, there may be some amount of support that resides in different locations to provide up-to-date reporting of runtime profile information such as workload or planned workload factors. 
     In an example embodiment, the server side support includes GPU allocation logic  203  for maintaining a ranked list of virtual machines to which physical GPU resources may be allocated (the GAL), for determining which virtual machines should be allocated some or all of the available GPU resources, and for performing the allocation and de-allocation of hardware GPU support to virtual GPUs (or equivalent communication mechanism) of the virtual machines. In some embodiments, other communication mechanisms (such as sockets, pipes, and the like) are used instead of virtual GPUs, although for ease of description, the term virtual GPU is used for describing an allocation of physical GPU resources. As shown, the GPU allocation logic  203  may execute as part of the virtualization logic  202  or may be provided in whole or in part by other aspects of the computing system running on one or more host/servers  201  such as by distributed logic running on each host/server  201  that communicates to allocate physical GPUs across servers (such as to support VMs that are moved or migrated). In addition, such distributed logic may be configured to allocate GPUs from multiple host/servers to a single VM. 
     In addition, in some embodiments, the server side support includes a workload advisor/GBF reporting component for each virtual machine, e.g., advisor/GBF reporting components  206   a - 206   b , for reporting data used to manage profile information, and rendering support, e.g., rendering support logic  205   a - 205   b , for assisting VMs (through API, libraries, commands, etc.) to take advantage of the parallelism available with use of GPUs. In some embodiments, these components execute as part of the VM Support  204   a - 204   b , for example, as part of a process (e.g., a VMX process in VMware&#39;s virtualization environment) that executes on virtualization logic  202 , which is hosted by one or more host/server computing systems  201 . For example, the components  204   a - 206   a  and  204   b - 206   b  may execute as part of an MKS (mouse, keyboard, screen handling) thread  152   a , which executes as part of VMX processes  151   a - 151   c  as described with reference to  FIG. 1 . In other embodiments, these components may be implemented in other parts of the virtualization environment such as part of each VMM (virtual machine monitor, e.g., VMMs  142   a - 142   c ) or as other parts of virtualization logic  202 . The rendering support  205   a  and  205   b  is responsible for receiving the virtual graphics device commands from guest  210   a  and  210   b  (e.g., guest applications  212   a  and  212   b  executed from the desktop using the guest operating system  211   a  and  211   b , respectively) and carrying them out through the graphics stack (shown in  FIG. 1  as graphics API  121  and graphics drivers  122 ) to the graphics hardware associated with the host  201 , such as frame buffer  104 . The workload advisor/GBF reporting logic  206   a  and  206   b  is responsible for gathering and communicating information from each respective VM to use in computing the GBF for that VM. In some embodiments, the logic  206   a  and  206   b  also report factors used to determine a VM&#39;s runtime profile (describing or measuring the workload being processed on the VM), such as a measure of the video RAM (VRAM) used, the load of the command queue (e.g., an SVGA command queue or graphics command queue), the ratio of data transfers to all commands (e.g., the amount of DMA accesses to available bandwidth), and/or other measurements. 
     In example embodiments, the components of the server side support for the DGAS are implemented using standard programming techniques. In general, a range of programming languages known in the art may be employed for implementing such example embodiments, including using object-oriented, functional, scripting, and declarative languages. In addition, in other embodiments the functionality of the different components may be distributed amongst themselves in different ways or distributed in other ways, yet still achieve the functions of a DGAS. 
     The components of the DGAS shown in  FIG. 2  cooperate to dynamically allocate physical GPU resources among a set of virtual machines according to their GPU benefit factors and potentially other considerations.  FIG. 3  is an example flow diagram of an overview of operations of an example dynamic GPU allocation system. The operations described with reference to  FIG. 3  are described in further detail with reference to other figures that follow. 
     In block  301 , the DGAS initializes the GPU allocation list (GAL) to insert and rank all running VMs according to their GPU benefit factors and, potentially, static priorities set by someone such as an administrator-user. The GAL can be any suitable data structure, for example a linked list, array, database, file, or the like, and may be stored in temporary or persistent storage. In one embodiment the GAL is a list of VMs, an abstraction of which is described with reference to  FIG. 4 . 
     The GPU benefit factor (GBF) of a virtual machine is calculated based upon the workload (or intended workload) of the virtual machine, such as the types of graphics and/or video commands that are being or intended to be processed. For example, different GPU benefit factors may be associated with VMs that perform or have historically performed 3D graphics, 2.5D graphics, video, 2D graphics, or that will not likely benefit from use of a GPU. Other GBFs may be defined for other situations and accommodated accordingly. For example, other non-graphics and non-video commands that may benefit from GPU acceleration, such as for highly parallel and/or repetitive algorithms (e.g., what is termed GPGPU processing or general purpose GPU processing), may also contribute to a GBF of a virtual machine. Initially (before much is known about the VM), the VM may be assigned a GBF based upon the kind of guest operating system and any history available about the applications that will be run or are intended to run on the VM. Over time, the GBF of a particular machine will “self correct” as the workload is better assessed, e.g., by determining a runtime profile of the VM. 
     In one example embodiment, a GBF may designate six different values that can be assigned to a virtual machine based upon different categorizations (e.g., computational profiles, classifications, and the like). According to one example categorization, VM that is performing three dimensional (3D) graphics is assigned a GBF of “6,” a VM that is performing non-graphics, non-video computing that may benefit from GPU use (e.g., general purpose GPU computing, also called GPGPU computing) is assigned a GBF of “5,” a VM that is performing two and a half dimensional (2.5D) graphics is assigned a GBF of “4,” a VM that is encoding/decoding video is assigned a GBF of “3,” a VM that is performing two dimensional (2D) graphics is assigned a GBF of “2,” and a VM that is not performing the type of processing that would generally gain from GPU usage is assigned a default GBF of “1.” Other embodiments may designate a fewer or greater number of GBF values or categorize workloads differently. 
     VMs performing 3D with a GBF of “6” are typically VMs that perform true 3D rendering such as MCAD (Mechanical Computer Aided Design) applications, Google Earth rendered in 3D mode, 3D games (such as Chess Titans), and the like. These VMs typically involve large numbers of 3D triangles that require lighting, texturing, and Z-buffering. Use of a GPU in such instances should make a material difference in improving the performance of these types of applications and their presentation quality. 
     VMs performing (non-graphics, non-video) computing that may benefit from GPU use (such as GPGPU applications) with a GBF of “5” are typically VMs that are running high performance computing applications such as those that require highly repetitive or parallel algorithms that contain building blocks capable of running (separately) on multiple processors. Modeling applications such as statistical regressions on very large data are examples of such applications. Additional examples include text search, encryption, mathematical analysis (such as finite element analysis or fluid dynamics, and the like). Some of these applications do not have real-time sensitivity and can be performed in “batch” mode. GPUs can be used to run such workloads in the off-work hours, after the graphics or video intensive VDI sessions terminate. 
     VMs performing 2.5D graphics with a GBF of “4” may include, for example, VMs running Windows with the “Aero” user interface turned on, including Flip 3D. Such user interfaces or similar applications may include a few triangles with texturing, however no lighting or Z-buffering. In addition, such applications may employ some image processing operations such as Gaussian Blur. 
     VMs performing video with a GBF of “3” may include, for example, VMs engaged in video playback, which are able to leverage the video decode or transcode capabilities of a GPU. 
     VMs performing 2D with a GBF of “2” may include, for example, VMs with desktop user interfaces that do not fall into any of the above categories. They may still benefit from GPU resources, for example, to speed up the encoding for remoting the user interface. 
     VMs that do not fall into any of the above categories with a default GBF of “1” may include VMs with workloads that would not likely gain much advantage from GPU acceleration. For example, VMs for database management, web serving, email serving, and the like, may fall into this category. Typically, applications that do not lend themselves to parallelization, overly complex, or are latency sensitive may fall into this category. 
     In some embodiments static priorities may also be set by a user, such as an administrator-user to guarantee certain behaviors in spite of the runtime flexibility affording by the GBFs. In one example embodiment, static priorities may designate one of four values: OVERRIDE, HIGH, DEFAULT, or BOTTOM. The OVERRIDE priority designation is used to indicate that the GBF ranking for a VM is to be overridden—physical GPU resources are to be allocated to the designated VM whenever available. This mechanism can be used, for example, to give preference to users of VMs running 3D applications that are critical to their jobs. It also supports an allocation mechanism that allows users to pay for guaranteed GPU support, such as in a public cloud or shared resource environment. In some embodiments, when the GPU allocation logic detects that a VM with an OVERRIDE priority is actually not making use of the GPU resources (such as a VM with a consistently low GBF as determined from its runtime profile) it may issue alerts or warnings to an administrator-user and/or inform the user of the VM that he or she is paying for unused resources. The HIGH priority designation is used to indicate a “tie-breaker” when two or more VMs with the same GBF are contending for the same GPU allocations. Thus, for example, a VM with a GBF of 6 and a priority of HIGH is “seated” (allocated GPU resources) before a VM with a GBF of 6 and a priority of DEFAULT. The BOTTOM priority designation is used to indicate that the VM should appear at the bottom of the GBF allocation list to be assigned “left-over” GPU resources. For example, such left-over GPU resources may be used for batch GPGPU workloads. The DEFAULT priority designation is assigned to any remaining VMs not assigned an OVERRIDE, a HIGH, or a BOTTOM priority. In this case VMs with identical GBFs may be randomly ordered. 
     In block  303 , the DGAS determines which subset of VMs on the GPU allocation list can be allocated physical GPU resources based, for example, on which VMs of the ranked list will fit (require an amount of available resources) on the GPUs. An example allocation of a subset of VMs is described with reference to  FIG. 5 . 
     In blocks  305 - 309 , the DGAS processes events and dynamically updates the GBF of running VMs and hence the GPU allocations. More specifically, in block  305 , the DGAS process events, such as starting up a new VM, performing a migration of a running VM, etc., that affects the GBF of at least one VM. The DGAS then updates the GPU allocation list. Some events may result in downgrading a GBF of a particular VM. Other events may set an initial GBF for a VM to be initially run, moved, or run again, based, for example, on past history. An example processing of such events is described with reference to  FIG. 8 . 
     In block  307 , the DGAS determines the runtime profiles of the VMs on the GPU allocation list, re-ranks them, and marks any VMs that can be potentially unseated (when a better contender becomes available) because their runtime profiles indicate that they are not really benefiting from GPU acceleration and not benefiting in an amount that is beyond a tolerable threshold (below or above depending upon how the measurement is performed). In general, only unseating a VM when a threshold is reached insures that the potential benefit of seating a new contender VM avoids the cost of the context switch between VMs. In effective embodiments, the switching between a VM&#39;s use of a CPU and use of a GPU needs to occur sufficiently quickly otherwise the potential gain in switching from one to another may be partially or completely compromised. An example determination of runtime profiles is described with reference to  FIG. 7 . 
     In block  309 , the DGAS changes the GPU resource allocation if warranted. That is, if viable contender VMs are available, then one or more of the VMs previously marked as potentially able to be unseated are unseated (have their GPU resource allocations de-allocated) and these resources are then given to one or more of the viable contender VMs. 
     The DGAS continues to execute the logic or processing events and handling dynamic GPU allocation by returning to block  305 . 
       FIG. 4  is a block diagram of an example GPU allocation list (GAL) used with an example dynamic GPU allocation system. The diagram of  FIG. 4  illustrates an abstraction of a GAL  400  and, as mentioned, may be implemented by any suitable data structure including for example a list, array, table, database, file, etc. The GAL  400  contains a list of VMs in a ranked order, where each row  410 - 425  indicates a rank  401 , an identifier of a VM  402 , an indicator of a priority  403 , an indicator of a GPU benefit factor  404 , and an indicator of requirements of that VM such as the amount of video random access memory (VRAM)  405 . As shown, the priority indicator  403  indicates a value of “2” corresponding to a priority of OVERRIDE; a value of “1” corresponding to a priority of HIGH; a value of “−1” corresponding to a priority of BOTTOM; and a value of “0” corresponding to a priority of DEFAULT. In other embodiments, the indicator and/or values and/or number of discrete priorities may be different or modified. The VM identifier  402  is shown as a number, however this identifier could be of any form, including for example, text, numeric, mixed, or otherwise. The indicator  404  of the GBF is shown as a numeric value from “1” to “6” as described above; however, this indicator and/or values and/or number of discrete GBFs may be different or modified. The indicator  405  of VM requirements may indicate more than VRAM needs, or may not be present an all in the GAL  400  and may be computed or retrieved from an external resource. Again, the figure demonstrates one example of categorizations of GBF and a priority scheme. 
     As described further with respect to  FIG. 5 , VMs are allocated GPU resources based, for example, on their position in the ranked ordering  401  in the GAL  400 . When ranked as shown, when the GPU resources comprise two GPUs of 4 gigabytes each, then the VMs occupying “seats” 1-8 in the ranked order  401  can be allocated virtual resources (e.g., virtual GPU resources) that correspond to their portion of the physical GPU resources (or assigned to existing virtual resources when previously allocated). In  FIG. 4 , the subset of VMs  430  represents those VMs which have received an allocation of GPU resources. The subset of VMs  430  contains 4 VMs shown in rows  410 - 413  with an OVERRIDE priority; thus, they are allocated GPU resources first without regard to their corresponding GBFs. The subset of VMs  430  also contains 2 VMs shown in rows  414 - 415  with a HIGH priority and the same GBF of “6” (category assigned to VMs with a 3D type workload). The next VM shown in row  416  has the same GBF but a DEFAULT priority and thus appears after the VMs in rows  414 - 415 . The subset of VMs  430  also contains 1 VM in row  417  with a DEFAULT priority and a GBF of “5.” Thus, it can be seen that, in this embodiment, generally, VMs with an OVERRIDE priority will be considered for GPU resource allocations first, followed by those VMs with the highest GBFs, using a “HIGH” priority to break ties between them. Over time, the positions of the VMs on the GAL  400  may vary dynamically as the GBFs of these VMs are decreased or increased through evaluating the runtime profile of these VMs as described with reference to  FIGS. 6A-6B  and  FIG. 7 . 
     In some embodiments, a GBF may be used without a priority. Further, in other embodiments, the priority may be determined at other times other than when initializing, bringing up, or booting the system and may be retrieved by different methods, such as, for example, a property value stored in an initialization file, on a network resource, or the like. 
     Also, GAL  400  as illustrated includes all running VMs—those using GPU resources and those running on one or more CPUs. In some embodiments, separate lists are maintained for those VMs running on CPUs and those running on GPUs. The logic and handlers for maintaining such lists are correspondingly modified. 
       FIG. 5  depicts an example allocation of GPUs among an example set of virtual machines. This allocation corresponds to the subset of VMs  430  indicated in  FIG. 4  and illustrates how the different VMs are allocated to one of the two corresponding GPUs based upon how their resource requirements match the available resources of the two GPUs  510  and  520 , available, for example, as part of server/host hardware  101  of  FIG. 1 . The VMs shown in  FIG. 5  correspond to the rows  410 - 425  in the GAL  400 , thus, some of the VMs, such as the VM represented by VM representation  510 , are not allocated GPU resources. VM representations  501 - 508  correspond to the eight VMs in the subset of VMs  430  listed in the GAL  400  that are allocated GPU resources. As can be observed, the VMs are allocated GPU resources based upon availability—not necessarily in the order that they appear on the GAL  400 . Accordingly, VM representations  501 ,  502 ,  503 , and  507  are allocated GPU resources from the first physical GPU  530  and VM representations  504 ,  505 ,  506 , and  508  are allocated GPU resources from the second physical GPU  520 . In some embodiments, some portion of one or more of the GPUs will be available and not used if there is no VM that fits in the remaining portion. Also, in some embodiments, a VM further down in the ranked list may be allocated the remaining portion before others with higher GBFs if it fits and the other VMs higher on the ranked list do not. In other embodiments, VMs are not allocated GPU resources out of the ranked order. 
     In the allocation illustration  500 , the VM identified by VM representation  509  is the next likely VM to be seated—receive an allocation of VM resources—if and when one of the other VMs identified by VM representations  501 - 508  is suspended, moved, closed, and/or unseated, for example, by having its VM changed, for example as a result of an assessment of its runtime profile or as a result of a changed priority. For example, the VM representations  501 ,  503 , and  505  are indicated by dashed lines to indicate for one or more reasons that they are available to be unseated. (Their new GBFs are not shown.) One or more of these VMs may be unseated to allocate resources to a contender VM, such as the VM identified by VM representation  509 . In this example, only one of these VMs available to be unseated would be unseated to address the needs of the contender VM identified by VM representation  509  because the contender VM only needs 256 MB, which are more than available from one of the VMs available to be unseated. Of note, if a VM can be unseated that would not yield sufficient GPU resources (for example, only would yield 128 MB of VRAM as opposed to the needed 256 MB), then the VM that could be unseated may not be unseated. 
     Although the examples described herein often refer to a GPU allocation list and GPU benefit factor, the techniques described herein can also be used to allocate other types of resources. In addition, the concepts and techniques described are applicable to other prioritization techniques. Also, although certain terms are used primarily herein, other terms could be used interchangeably to yield equivalent embodiments and examples. In addition, terms may have alternate spellings which may or may not be explicitly mentioned, and all such variations of terms are intended to be included. 
     Example embodiments described herein provide applications, tools, data structures and other support to implement a GPU allocation system to be used to dynamically allocate GPU resources to one or more virtual machines. In the following description, numerous specific details are set forth, such as data formats and code logic sequences, etc., in order to provide a thorough understanding of the described techniques. The embodiments described also can be practiced without some of the specific details described herein, or with other specific details, such as changes with respect to the ordering of the logic, different logic, etc. Thus, the scope of the techniques and/or functions described are not limited by the particular order, selection, or decomposition of aspects described with reference to any particular routine, module, component, and the like. For example, the separate handling of the determination of the GBFs and allocation/reallocation as depicted in  FIGS. 6-8  may not be implemented as separate threads in a production implementation, but are so depicted for ease of description. 
       FIGS. 6A and 6B  depict a flow diagram of example logic for managing a GPU allocation list and allocating GPU resources based upon the list. Logic  600  may be executed, for example, by the GPU allocation logic  203  of virtualization logic  203  running on the host/server  201  in  FIG. 2 . As described with reference to blocks  307  and  309  of  FIG. 3 , the GPU allocation list handler is responsible for managing the GAL (such as GAL  400  of  FIG. 4 ) including updating the ranked list of VMs based upon runtime profiles and performing any reallocations of GPU resources based upon the updated GAL. Blocks  601 - 607  described example logic to implement updating the ranked list of VMs. Blocks  609 - 623  describe example logic to implement adjusting the GPU resource allocations. 
     Specifically, in block  601 , the logic begins a loop to process each running VM in the GPU allocation list beginning with the first. In block  602 , the logic executes additional logic described with reference to  FIG. 7  to determine the current runtime profile for the current VM being processed. The runtime profiles of the VMs in the GAL are used to determine the most promising contender VMs (to seat next—i.e., to allocate GPU resources) and to identify which VMs have any actual low utilization of the GPU resources previously allocated to them. Since the GAL includes VMs that are potentials for GPU resource allocation, a current runtime profile is computed for each VM that is currently running on a CPU as well as each VM that is currently running on a GPU. In some embodiments, a runtime profile may be viewed as a “fractional” component of a GBF (computed once a VM has been executing); whereas the category of workload (3D, 2D, etc.) may be viewed as an “integer” component of a GBF. In this manner the runtime profile can be used to further distinguish two or more VMs with the same category (base) GBF. 
     In block  603 , the logic determines whether it is necessary to downgrade or upgrade the GBF of the current VM being processed, for example, based upon whether the runtime profile determination has indicated that the current VM is using GPU relevant resources or processing a categorization of workload that is indicative that the current VM would benefit more or less from GPU resource allocations. If so, the logic continues in block  604 , otherwise the logic continues in block  605 . 
     In block  604 , the logic adjusts the GBF of the current VM being processed. In embodiments that use the runtime profile as a fractional component, it can be added to the base component of the GBF to compute a total GBF. In other embodiments, where the GBF is changed to another category, a timeout mechanism may be used to downgrade or upgrade the GBF. For example, a downgrade of a GBF can be performed to downgrade a VM&#39;s GBF from a 3D type VM to a 2.5D type VM using a “no 3D commands” timeout (e.g., an alert or signal). Similarly, an upgrade of a GBF can be performed to upgrade a VM&#39;s GBF from a 2D type VM to a 2.5D type VM using a “use 2.5D commands” timeout. In other embodiments, an upgrade or downgrade of a GBF may be performed at other and/or different times. 
     In block  605 , the logic determines whether the (perhaps new) GBF of the current VM being processed is below a particular threshold (within or above, depending upon how the threshold is defined), and, if yes, the logic continues in block  606 , otherwise the logic continues in block  607 . In block  606 , the current VM being processed is marked as a candidate for unseating (de-allocation of GPU resources) as illustrated by the dashed line VM representations in  FIG. 5 . 
     A threshold value is used to prevent unnecessary context switches of VMs on a GPU where the benefit of GPU resource usage does not exceed the time required to perform the context switch. In some embodiments, the threshold value is a system configuration parameter that is predetermined. In other embodiments, the threshold value is varied depending upon load and performance of the DGAS. 
     In block  607 , the logic determines whether there are additional VMs to process, for example, more VMs on the GAL, and if so returns to the beginning of the loop in block  601 , otherwise continues processing in block  609 . 
     Blocks  609 - 623  perform adjustments of the GPU resource allocation in view of the updates determined in blocks  601 - 607 . In particular, block  609 , the logic determines (e.g., computes) the total physical GPU resources potentially available for allocation based upon the candidate VMs marked for unseating in block  606  and any other available GPU resources. Additional GPU resources may be available, for example, from leftover GPU resource availability that was previously too small to be allocated to any of the VMs on the GAL (with their requirements as listed at the time of prior allocation). 
     In block  611 , the logic determines (e.g., computes, receives, etc.) the total amount of physical GPU resources required for possible contender VMs such as the contender VM identified by VM representation  509  of  FIG. 5 . 
     In block  613 , the logic determines whether there exist sufficient GPU resources to allocate for all contender VMs, and, if so, continues in block  615 , otherwise continues in block  619 . In block  615 , the logic de-allocates as many GPU resources from VMs that are candidates for unseating as needed for all contender VMs, and updates the GAL with corresponding statuses. In block  617 , the logic allocates the resultant GPU resources to all contender VMs by allocating hardware GPU resources to corresponding virtual resources (e.g., virtual GPUs) of (or allocating virtual resources if not already allocated to) those contender VMs, and updates the GAL with corresponding statuses. The logic  600  then ends. 
     In block  619  (when there are insufficient resources to allocate GPU resources to all contenders), the logic determines which contender VMs will fit on the potentially available GPU resources of marked VM candidates for unseating (as marked in block  606 ). In block  621 , the logic de-allocates GPU resources from the VM candidates for unseating identified in the previous block  619 , and updates the GAL with corresponding statuses. In block  623 , the logic allocates the GPU resources just de-allocated in block  621  as needed for the contender VMs, and updates the GAL with corresponding statuses. The logic  600  then ends. 
     In some embodiments, the GBFs of VMs may be used to load balance across a server cluster. In this case VMs with a high priority and high GBF on a heavily loaded server GPU may be moved to servers with greater GPU resource availability. Also, a single VM may be accelerated on more than one GPU. In this case, when there is appropriate capacity on multiple GPUs (even across servers) then higher performance and better system utilization may be achieved by processing the workload (e.g., rendering the frames) of the VM in a distributed manner across the multiple GPUs. For example, the GPUs could parallel process alternating scan lines or different chunks of the VM&#39;s frame and then combine the results through a primary GPU or the CPU. The rendering support  205   x  in conjunction with the GPU allocation logic  203  of  FIG. 2  may be used to accomplish this task. Hooks for the virtualization logic  202  may be provided to determine a GPU&#39;s load and available capacity to aid in organizing this distributed approach. Appropriate load balancing and distribution logic may be incorporated into the logic of  FIGS. 6A and 6B . 
       FIG. 7  is a flow diagram of example logic for determining a runtime profile of a designated virtual machine. Logic  700  may be executed, for example, by the GPU allocation logic  203  of virtualization logic  203  running on the host/server  201  in  FIG. 2  in combination with workload advisor/GBF factor reporting logic  206   x  and rendering support  205   x  for the designated VM that reports information used to compute the runtime profile. The logic  700  reflects one technique (algorithm, mechanism, etc.) for determining a runtime profile. Other techniques may be used and similarly incorporated into a DGAS. This logic is computed, for example, for each current VM being processed by the logic that maintains the GAL. 
     As illustrated, the runtime profile is determined based upon some metric of VRAM use, the type or amount of rendering (e.g., SVGA) commands used, and/or the amount of data transfers (e.g., graphics data transfers via DMA-direct memory access or other mechanism). In other embodiments, other measurements may be substituted, added, or modified. These factors are defined in such a way that they can be measured independently of whether the workload is being served by a GPU or by a CPU. 
     Accordingly, in block  701 , the logic first determines a representation of load/amount of VRAM in use by the designated VM. Large VRAM utilization suggests that the VM has large models in local storage that are rendered repeatedly, which potentially benefits from a GPU resource allocation. A GPU&#39;s VRAM bandwidth is usually faster than CPU RAM&#39;s bandwidth, so moving workloads that consume more VRAM to a GPU (allocating hardware GPU resources to the VM) should benefit overall performance. One measure of this load is the average fraction of allocated virtual video RAM (VRAM). For example, equation (1) can be used for this determination:
 
 v ram_ratio= v ram_workingset/ v ram_size  (1)
 
where vram_workingset is the working set size of virtual VRAM used by the guest (e.g., the size of textures actually being frequently referred to in the command stream) and vram_size is the total size of the virtual VRAM allocated to the guest.
 
     In block  703 , the logic determines the load of the command queue (e.g., an SVA command queue, rendering command queue, etc.) from the guest OS to the host. For example, in  FIG. 2 , the VM Support  204   a  can determine the load of commands from guest OS  211  to the rendering support  205   a . The load of the command queue is suggestive of the execution load of the VM. A mostly empty queue suggests that the VM process is waiting for commands. If this is a VM supported by a GPU, then the GPU is not likely being put to good use. If a workload is sporadic (idle a great deal of the time), then it may be advantageous to move the GPU resources from this VM (unseat the VM) to avoid unnecessary consumption of resources like VRAM during this idle period. Equation (2) can be used for this determination:
 
activity_ratio=1−( t _idle/ t _interval)  (2)
 
where t_idle is the amount of time the command queue is idle on a given interval of time t_interval. In some embodiments, instead of measuring the command queue, the amount of time a processing unit for handling graphics is idle is measured. The processing unit may be a virtual GPU or some other communications mechanism.
 
     In block  705 , the logic determines the portion of data transfers (e.g., DMA accesses, graphics data transfers, and the like) to the total command bandwidth. A high bandwidth use is suggestive that the data being processed is constantly changing and hence is frequently, if not constantly, being communicated from the guest to the VM. For example, textures and vertex data, and the like are constantly getting generated by the application and transmitted to the VM. PCI bus bandwidth (used to communicate between main memory and a GPU when the GPU resides on a separate card) is a limited resource, so workloads that do a lot of graphics data uploads and/or downloads to/from main memory may cause the PCI bandwidth to become a bottleneck if moved to a GPU. Accordingly, it may be preferable to leave such VMs executing on a CPU. Equation (3) can be used for this determination:
 
data_transfer_ratio=( cmd _bandwidth−data_transfers)/( cmd _bandwidth)  (3)
 
where data_transfers include, for example, textures and vertex/index/constant buffers and cmd_bandwidth is the bandwidth of all commands.
 
     These factors are defined in such a way that they yield values near 1 for situations favorable to hardware GPU allocations and values near 0 for situations favorable to software (CPU) support. For example:
         vram_ratio of 1 means the workload is making full use of the VRAM and given that the GPU&#39;s VRAM is typically faster than system memory, it is more efficient to move or keep such a workload running on the GPU;   vram_ratio near 0 means the workload is not likely to benefit from a faster VRAM;   activity_ratio of 1 means the GPU has high utilization and therefore there is benefit in moving or keeping this workload on a GPU, given that a GPU&#39;s processing throughput is typically much higher than a CPU&#39;s;   activity_ratio near 0 means that the GPU is not heavily utilized (e.g, is not a bottleneck) and it is often idle, therefore, the workload cannot be significantly sped up by moving it to or keeping it on a GPU;   a data_transfer_ratio of 1 means that the communication to the GPU is dominated by commands, which can be sent very effectively across the PCI bus to the GPU; and   data_transfer_ratio near 0 means that the communication to the GPU is dominated by data transfers such as containing graphics data, which cannot be virtualized well on a GPU as the PCI bus bandwidth is a scarce resource.       

     Together, these factors yield an estimate of how much benefit there is to move the workload of the designated VM to a GPU. In some embodiments, this measurement may be incorporated into the GBF of the designated VM as a fractional term. In some embodiments, the runtime profile may be used to cause the GBF to be decreased or increased via, for example, a timeout mechanism. 
     In block  707 , the logic determines a weighted formula of the three above measurements to adjust the GBF of the designated VM. 
     One example weighted formula is described in equation (4):
 
 GBF _frac= v ram_ratio*activity_ratio*data_transfer_ratio  (4)
 
which gives a value between 0 and 1, which can be incorporated into the GBF of the designated VM as a fractional term: GBF=GBF_base+GBF_frac. Used in this manner, the GBF would serve to distinguish between workloads with the same base GBF (by category). Note that the GBF_frac will tend to zero if any of its factors tend to zero.
 
     Another weighted formula, based upon geometric mean, is described in equation (5):
 
 GBF _frac=( v ram_ratio^ W 1*activity_ratio^ W 2*data_transfer_ratio^ W 3)^(1/( W 1 +W 2 +W 3))  (5)
 
This equation yields a fractional result in [0, 1], which tends to zero if any of its components tends to zero. The weights assigned depend on the system category (e.g., GBF_base=[1, 2, 3, 4, 5, 6]) and possibly particular configuration thereof. The weights may start with defaults (for example, based on benchmarks as part of system certification or configuration) and could be calibrated or improved using benchmarks on a specific system configuration.
 
       FIG. 8  is a flow diagram of example logic for handling events that cause a GPU benefit factor (GBF) to be set or changed for a designated virtual machine. Logic  800  may be executed, for example, by the GPU allocation logic  203  of virtualization logic  203  running on the host/server  201  in  FIG. 2 . Although shown as separate logic from  FIGS. 6A and 6B , which manages the GAL, it can be appreciated that the logic of  FIG. 8  may be performed by one or more separate execution paths (e.g., threads) or the same execution path of the GAL handler. 
     The logic of  FIG. 8  is currently shown to handle a certain set of events. Other or different events may be incorporated. Specifically, in block  801 , the logic determines whether it has received a timeout event to adjust a GBF of an indicated VM. If so, then the logic continues in block  802 , otherwise continues to process a different event in block  804 . In block  802 , the logic causes the GBF of the VM corresponding to the event to be revised on the GPU allocation list and continues in block  803 . In block  803 , the logic determines whether the GBF of the VM corresponding to the event has been downgraded and is below a certain threshold (above or outside depending upon how the threshold is defined) and if so continues to block  805  to de-allocate the VM, otherwise continues to block  809 . As explained above, even if a GBF is downgraded to one which might otherwise be de-allocated, this is not done unless it falls below (or outside, etc.) a certain threshold so that context switches on a GPU are not performed without the ability to reap sufficient benefit. 
     In block  804 , the logic determines whether it has received notification that a VM with already allocated GPU resources is to be shutdown or suspended. If so, then the logic continues in block  805 , otherwise continues to process a different event in block  806 . In block  805 , the logic de-allocates the VM (but keeps track of the priority and GBF of the VM for potential resumes if the VM is being suspended) and updates the GAL accordingly. In some embodiments this results in a call to another thread responsible for the GAL. The logic continues to block  809 . 
     In block  806 , the logic determines whether it has received notification of a new VM launch, a new application launch on a VM, a VM resume, or a VM migrate (e.g., a vMotion event). If so, then the logic continues in block  808 , otherwise continues to block  810 . In block  808  the logic determines an “initial” GBF for the VM corresponding to the event and then continues to block  809 . If the VM is a newly created VM, then the GBF and priority are determined as expressed above with reference, for example, to  FIGS. 3 and 4 . If, on the other hand, the VM has been migrated or resumed, then the GBF and priority of the VM last in effect when the VM was last running (before it was suspended or live moved) is used to provide an initial seat for the VM. This provides a “predictive” component to the GBF, because the prior history of the GBF is used to predict the needs of the VM when it is resumed or moved. Loading the VM on the GPU is performed as part of the resume/vMotion (live move) process and before its first frame is rendered, to avoid a disruptive context switch shortly after the VM begins serving its user interface. The GBF is universal across servers that run a compatible hypervisor and thus can be used when a VM is moved. 
     This predictive approach also may be used to smooth out the launching of new applications. For example, a 3D application typically loads its models (geometry, textures, and shaders) before rendering its first frame. An analysis of the graphics rendering commands (e.g., SVGA command queue) is sufficient to determine the GBF category (e.g., 3D, 2.5D, etc.) of the VM. Thus, a quick determination can be made to see if the newly loading application justifies a change in the GBF of the VM, hence its rank in the GAL, and possibly a GPU resource allocation. If so, the application (its VM) may be switched to use the GPU resources before this context loading phase and before the first frame is rendered. This avoids the disruption that would likely result if such a context switch were to occur shortly after the application&#39;s initiation of rendering. 
     In block  809  the logic causes the GPU allocation list handler to be invoked to recompute the rankings, markings of available VMs to unseat, and changes to allocations, and then continues to block  810 . 
     In block  810  the logic continues with other activities and/or ends. 
     All of the above U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet, including but not limited to co-pending applications entitled, “Dynamic Allocation of Physical Graphics Processing Units to Virtual Machines,” Ser. No. 13/722,998; and “Managing a Data Structure for Allocating Graphics Processing Unit Resources to Virtual Machines,” Ser. No. 13/723,021 both filed concurrently, are incorporated herein by reference, in their entireties. 
     From the foregoing it will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the present disclosure. For example, the methods, techniques, and systems for performing video encoding for UI remoting discussed herein are applicable to other architectures other than an x86 architecture. Also, the methods and systems discussed herein are applicable to differing protocols, communication media (optical, wireless, cable, etc.) and devices (such as wireless handsets, electronic organizers, personal digital assistants, portable email machines, tablets, notebooks, game machines, pagers, navigation devices such as GPS receivers, etc.).