HipBone: A performance-portable graphics processing unit-accelerated C++ version of the NekBone benchmark

IF 2.5 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE International Journal of High Performance Computing Applications Pub Date : 2023-05-31 DOI:10.1177/10943420231178552
N. Chalmers, Abhishek Mishra, Damon McDougall, T. Warburton
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引用次数: 2

Abstract

We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.
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HipBone:性能可移植的图形处理单元,加速了NekBone基准的c++版本
我们提出hipBone,一个开源的性能便携代理应用程序,用于Nek5000(和NekRS)计算流体动力学应用程序。HipBone是原始NekBone CPU代理应用程序的完全GPU加速c++实现,具有几种新颖的算法和实现改进,可优化其在现代细粒度并行GPU加速器上的性能。我们的优化包括以汇编形式存储问题自由度的转换,以减少主迭代期间移动的数据量,以及主泊松算子内核的可移植实现。我们在来自两个不同供应商的三种不同的现代GPU加速器上演示了算子内核的接近屋顶的性能。我们提出了一种分离泊松算子在gpu上的应用的新算法,该算法积极地隐藏了光晕交换和汇编所需的MPI通信。我们的最近邻MPI通信实现利用了几种不同的路由算法和GPU-Direct RDMA功能(如果可用),从而提高了基准测试的可伸缩性。我们在橡树岭国家实验室的三个不同集群上演示了hipBone的性能,即Summit超级计算机和Frontier早期访问集群Spock和Crusher。我们的测试证明了跨不同集群的可移植性和非常好的扩展效率,特别是在大型问题上。
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来源期刊
International Journal of High Performance Computing Applications
International Journal of High Performance Computing Applications 工程技术-计算机:跨学科应用
CiteScore
6.10
自引率
6.50%
发文量
32
审稿时长
>12 weeks
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