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VOCL: An optimized environment for transparent virtualization of graphics processing units

机译:VOCL:用于图形处理单元透明虚拟化的优化环境

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Graphics processing units (GPUs) have been widely used for general-purpose computation acceleration. However, current programming models such as CUDA and OpenCL can support GPUs only on the local computing node, where the application execution is tightly coupled to the physical GPU hardware. In this work, we propose a virtual OpenCL (VOCL) framework to support the transparent utilization of local or remote GPUs. This framework, based on the OpenCL programming model, exposes physical GPUs as decoupled virtual resources that can be transparently managed independent of the application execution. The proposed framework requires no source code modifications. We also propose various strategies for reducing the overhead caused by data communication and kernel launching and demonstrate about 85% of the data write bandwidth and 90% of the data read bandwidth compared to data write and read, respectively, in a native nonvirtualized environment. We evaluate the performance of VOCL using four real-world applications with various computation and memory access intensities and demonstrate that compute-intensive applications can execute with negligible overhead in the VOCL environment.
机译:图形处理单元(GPU)已被广泛用于通用计算加速。但是,当前的编程模型(例如CUDA和OpenCL)只能在本地计算节点上支持GPU,在本地计算节点上,应用程序的执行与物理GPU硬件紧密耦合。在这项工作中,我们提出了一个虚拟的OpenCL(VOCL)框架来支持对本地或远程GPU的透明利用。该框架基于OpenCL编程模型,将物理GPU公开为解耦的虚拟资源,可以独立于应用程序执行情况进行透明管理。所提出的框架不需要修改源代码。我们还提出了各种策略来减少由数据通信和内核启动引起的开销,并证明与在本机非虚拟化环境中分别进行数据写入和读取相比,分别约有85%的数据写入带宽和90%的数据读取带宽。我们使用具有各种计算和内存访问强度的四个实际应用程序评估VOCL的性能,并演示了计算密集型应用程序在VOCL环境中的执行开销可忽略不计。

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