基于虚拟机的GPU计算资源管理系统
发布时间：2019-03-16 16:30
【摘要】：统一计算架构让图形处理器（GPU）升级为通用图形处理器，，分担原来需由中央处理器处理的通用计算任务，并获取相对中央处理器数倍甚至数十倍的加速比。与此同时，随着虚拟化技术日趋成熟，虚拟化技术和GPU计算的结合越来越受到高性能计算的青睐。但是目前没有基于虚拟机的GPU计算管理系统，或者在虚拟化环境下不能有效使用GPU的强劲计算能力，或者基于物理机的集群中资源不便有效管理、容易受到恶意用户攻击且崩溃后不能迅速恢复。 针对基于虚拟化技术的GPU计算，基于虚拟机的GPU计算资源管理系统（简称VMGPURMS）主要包含支持虚拟机中GPU计算，并采用CPU-GPU混合调度策略提高系统利用率。系统采用共享内存方式实现虚拟机和特权域之间的数据通讯，减少数据通讯带来的GPU计算性能的损耗；提出了CPU-GPU混合调度的策略：针对CPU资源远远多于GPU资源的现状，采用最少资源策略合理调度CPU和GPU作业，减少资源无益占据GPU作业的等待时间；针对GPU作业运行需要CPU协助的特点，提出了处理器资源预留策略，为GPU作业准备好配套的处理器和GPU资源；针对CPU作业占据GPU节点处理器资源，提出了GPU作业抢占的策略，把CPU作业迁移到其他节点上释放GPU资源给GPU作业，既不影响CPU作业的运行，也避免了GPU作业的等待。 基于虚拟机的GPU计算资源管理系统实现了在虚拟机层次上对处理器资源和GPU资源的协同管理和调度，有效结合利用虚拟机的良好特性和GPU的强劲计算能力。通过对系统的测试显示，虚拟化环境中的GPU计算能力保持在物理机的85%；采用CPU-GPU混合调度策略对CPU和GPU协同调度策略后，整个系统的利用率提升了17%。
[Abstract]:The unified computing architecture allows the graphics processor (GPU) to upgrade to a general-purpose graphics processor, sharing the common computing tasks that were originally handled by the central processing unit (CPU), and obtaining several or even tens of times the acceleration ratio of the CPU. At the same time, with the maturity of virtualization technology, the combination of virtualization technology and GPU computing is more and more popular in high-performance computing. However, at present, there is no virtual machine-based GPU computing management system, either can not effectively use the powerful computing power of GPU in a virtualized environment, or the physical machine-based cluster is inconvenient and effective management of resources, Vulnerable to a malicious user and unable to recover quickly after crashing. For GPU computing based on virtualization technology, GPU computing resource management system (VMGPURMS) based on virtual machine mainly includes supporting GPU computing in virtual machine, and adopts CPU-GPU mixed scheduling strategy to improve system utilization. The system uses shared memory to realize the data communication between virtual machine and privileged domain, and reduces the loss of GPU computing performance caused by data communication. This paper proposes a hybrid scheduling strategy for CPU-GPU: in view of the fact that CPU resources are far more than GPU resources, the least resource strategy is adopted to schedule CPU and GPU jobs reasonably, so as to reduce the waiting time that resources do not benefit to occupy GPU jobs; According to the characteristic that CPU is needed in the operation of GPU job, a resource reservation strategy of processor is put forward to prepare the supporting processor and GPU resource for GPU job. In order to solve the problem that CPU jobs occupy the resources of GPU node processor, the strategy of preemption of GPU jobs is proposed. The CPU jobs are migrated to other nodes to free GPU resources to GPU jobs, which does not affect the running of CPU jobs and avoids the waiting of GPU jobs. The virtual machine-based GPU computing resource management system realizes the cooperative management and scheduling of processor resources and GPU resources at the virtual machine level, which effectively combines the good characteristics of virtual machines and the powerful computing power of GPU. The test of the system shows that the GPU computing power in the virtualized environment is kept at 85% of that of the physical machine, and the utilization of the whole system increases by 17% after the CPU-GPU hybrid scheduling strategy is applied to the CPU and GPU co-scheduling strategy.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP315
本文编号：2441733
[Abstract]:The unified computing architecture allows the graphics processor (GPU) to upgrade to a general-purpose graphics processor, sharing the common computing tasks that were originally handled by the central processing unit (CPU), and obtaining several or even tens of times the acceleration ratio of the CPU. At the same time, with the maturity of virtualization technology, the combination of virtualization technology and GPU computing is more and more popular in high-performance computing. However, at present, there is no virtual machine-based GPU computing management system, either can not effectively use the powerful computing power of GPU in a virtualized environment, or the physical machine-based cluster is inconvenient and effective management of resources, Vulnerable to a malicious user and unable to recover quickly after crashing. For GPU computing based on virtualization technology, GPU computing resource management system (VMGPURMS) based on virtual machine mainly includes supporting GPU computing in virtual machine, and adopts CPU-GPU mixed scheduling strategy to improve system utilization. The system uses shared memory to realize the data communication between virtual machine and privileged domain, and reduces the loss of GPU computing performance caused by data communication. This paper proposes a hybrid scheduling strategy for CPU-GPU: in view of the fact that CPU resources are far more than GPU resources, the least resource strategy is adopted to schedule CPU and GPU jobs reasonably, so as to reduce the waiting time that resources do not benefit to occupy GPU jobs; According to the characteristic that CPU is needed in the operation of GPU job, a resource reservation strategy of processor is put forward to prepare the supporting processor and GPU resource for GPU job. In order to solve the problem that CPU jobs occupy the resources of GPU node processor, the strategy of preemption of GPU jobs is proposed. The CPU jobs are migrated to other nodes to free GPU resources to GPU jobs, which does not affect the running of CPU jobs and avoids the waiting of GPU jobs. The virtual machine-based GPU computing resource management system realizes the cooperative management and scheduling of processor resources and GPU resources at the virtual machine level, which effectively combines the good characteristics of virtual machines and the powerful computing power of GPU. The test of the system shows that the GPU computing power in the virtualized environment is kept at 85% of that of the physical machine, and the utilization of the whole system increases by 17% after the CPU-GPU hybrid scheduling strategy is applied to the CPU and GPU co-scheduling strategy.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP315
【参考文献】
相关期刊论文 前1条
1 刘真;;虚拟机技术的复兴[J];计算机工程与科学;2008年02期
本文编号：2441733
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