Exploring the Suitability of Remote GPGPU Virtualization for the OpenACC Programming Model Using rCUDA

Adrián Castelló, Antonio J. Peña, R. Mayo, P. Balaji, E. S. Quintana‐Ortí
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引用次数: 4

Abstract

OpenACC is an application programming interface (API) that aims to unleash the power of heterogeneous systems composed of CPUs and accelerators such as graphic processing units (GPUs) or Intel Xeon Phi coprocessors. This directive-based programming model is intended to enable developers to accelerate their application's execution with much less effort. Coprocessors offer significant computing power but in many cases these devices remain largely underused because not all parts of applications match the accelerator architecture. Remote accelerator virtualization frameworks introduce a means to address this problem. In particular, the remote CUDA virtualization middleware rCUDA provides transparent remote access to any GPU installed in a cluster. Combining these two technologies, OpenACC and rCUDA, in a single scenario is naturally appealing. In this work we explore how the different OpenACC directives behave on top of a remote GPGPU virtualization technology in two different hardware configurations. Our experimental evaluation reveals favorable performance results when the two technologies are combined, showing low overhead and similar scaling factors when executing OpenACC-enabled directives.
利用rCUDA探讨远程GPGPU虚拟化对OpenACC编程模型的适用性
OpenACC是一个应用程序编程接口(API),旨在释放由cpu和加速器(如图形处理单元(gpu)或Intel Xeon Phi协处理器)组成的异构系统的功能。这种基于指令的编程模型旨在使开发人员能够以更少的工作量加速其应用程序的执行。协处理器提供了强大的计算能力,但在许多情况下,这些设备在很大程度上仍未得到充分利用,因为并非应用程序的所有部分都与加速器架构相匹配。远程加速器虚拟化框架引入了一种解决此问题的方法。特别是,远程CUDA虚拟化中间件rCUDA提供了对集群中安装的任何GPU的透明远程访问。将这两种技术(OpenACC和rCUDA)结合在一个场景中自然具有吸引力。在这项工作中,我们探讨了在两种不同的硬件配置下,不同的OpenACC指令如何在远程GPGPU虚拟化技术之上运行。我们的实验评估显示,当这两种技术结合使用时,会产生良好的性能结果,在执行启用openacc的指令时显示出低开销和相似的缩放因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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