从GPU到CPU(以及其他):通过sycl启发的接口扩展GPUSPH中的硬件支持

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Concurrency and Computation-Practice & Experience Pub Date : 2024-10-28 DOI:10.1002/cpe.8313
Giuseppe Bilotta
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引用次数: 0

摘要

虽然大多数软件最初是为在CPU上串行或并行执行而设计的,并且在其开发后期才移植到GPU上,但GPUSPH是从底层开始设计的,使用CUDA在GPU上运行。通过引入对其他计算硬件(特别是CPU)的支持,让更广泛的受众可以访问它,这与将CPU代码移植到GPU时通常面临的挑战是互补的。我们提出了在GPUSPH中支持cpu作为计算设备的方法,只需最少的代码更改和较少的开发人员工作。详细的基准测试说明了实现的性能及其在单套接字和NUMA配置下跨多核的可伸缩性,显示了良好的强伸缩性和弱伸缩性。
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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From GPU to CPU (and Beyond): Extending Hardware Support in GPUSPH Through a SYCL-Inspired Interface

While most software is originally designed for serial or parallel execution on CPU, and porting to GPU comes later in its development, GPUSPH was designed from the ground up to run on GPUs using CUDA. Making it accessible to a wider audience by introducing support for other computational hardware, and in particular CPUs, poses challenges that are complementary to the ones normally faced when porting CPU code to GPU. We present the approach we have adopted to support CPUs as computational devices in GPUSPH with minimal code changes and low developer effort. Detailed benchmarks illustrating the performance of the implementation and its scalability across multiple cores in both single-socket and NUMA configurations show good strong and weak scaling.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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