Manyfold actors: extending the C++ actor framework to heterogeneous many-core machines using OpenCL

Raphael Hiesgen, Dominik Charousset, T. Schmidt
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引用次数: 4

Abstract

The processing power of modern many core hardware such as graphics processing units (GPUs) or coprocessors is increasingly available for general-purpose computation. The seamless way of actor systems to addresses concurrent and distributed programming makes it an attractive approach to integrate these novel architectures. In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework (CAF). This offers a high level interface for accessing any OpenCL device without leaving the actor paradigm. The new type of actor is integrated into the runtime environment of CAF and gives rise to transparent message passing in distributed systems on heterogeneous hardware. New actors are instantiated by the function spawn_cl, while the runtime environment handles the discovery and setup of OpenCL devices in the background. Our evaluations on a commodity GPU, an Nvidia TESLA, and an Intel PHI reveal the expected linear scaling behavior when offloading larger work items. For sub-second duties, the efficiency of offloading was found to largely differ between devices. Moreover, our findings indicate a negligible overhead over programming the native OpenCL API.
多重参与者:使用OpenCL将c++参与者框架扩展到异构多核机器
现代许多核心硬件(如图形处理单元(gpu)或协处理器)的处理能力越来越多地用于通用计算。参与者系统解决并发和分布式编程的无缝方式使其成为集成这些新颖体系结构的一种有吸引力的方法。在这项工作中,我们将启用opencl的Actor引入到c++ Actor框架(CAF)。这为访问任何OpenCL设备提供了一个高级接口,而无需离开actor范式。这种新型参与者被集成到CAF的运行时环境中,在异构硬件上的分布式系统中实现了透明的消息传递。新的actor由函数spawn_cl实例化,而运行时环境在后台处理OpenCL设备的发现和设置。我们对商用GPU、Nvidia TESLA和Intel PHI的评估揭示了在卸载较大的工作项时预期的线性缩放行为。对于亚秒级的任务,卸载的效率被发现在设备之间有很大的不同。此外,我们的研究结果表明,编写本机OpenCL API的开销可以忽略不计。
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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