克服科学应用中gpgpu计算的局限性

Connor Kenyon, Glenn Volkema, G. Khanna
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引用次数: 1

摘要

离散通用图形处理单元(gpgpu)的性能一直在快速提高。控制系统主机内存和GPU之间数据通信的PCIe互连没有得到快速改进,由于GPU在等待PCIe数据传输时停机,导致性能下降。在本文中,我们探讨了两种替代有限的PCIe带宽、NVIDIA NVLink互连和用于共享内存异构系统架构(HSA)设备的零复制算法。OpenCL SHOC基准测试套件用于在各种科学应用内核上测量每个设备的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overcoming Limitations of GPGPU-Computing in Scientific Applications
The performance of discrete general purpose graphics processing units (GPGPUs) has been improving at a rapid pace. The PCIe interconnect that controls the communication of data between the system host memory and the GPU has not improved as quickly, leaving a gap in performance due to GPU downtime while waiting for PCIe data transfer. In this article, we explore two alternatives to the limited PCIe bandwidth, NVIDIA NVLink interconnect, and zero-copy algorithms for shared memory Heterogeneous System Architecture (HSA) devices. The OpenCL SHOC benchmark suite is used to measure the performance of each device on various scientific application kernels.
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