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Multi-communication layered HPL model and its application to GPU clusters
ETRI Journal ( IF 1.4 ) Pub Date : 2021-06-23 , DOI: 10.4218/etrij.2020-0393
Young Woo Kim 1 , Myeong‐Hoon Oh 1 , Chan Yeol Park 2
Affiliation  

High-performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities of computing systems and has been used as a standard to compare the performance of computing systems since the early 1980s. In the initial system-design stage, it is critical to estimate the capabilities of a system quickly and accurately. However, the original HPL mathematical model based on a single core and single communication layer yields varying accuracy for modern processors and accelerators comprising large numbers of cores. To reduce the performance-estimation gap between the HPL model and an actual system, we propose a mathematical model for multi-communication layered HPL. The effectiveness of the proposed model is evaluated by applying it to a GPU cluster and well-known systems. The results reveal performance differences of 1.1% on a single GPU. The GPU cluster and well-known large system show 5.5% and 4.1% differences on average, respectively. Compared to the original HPL model, the proposed multi-communication layered HPL model provides performance estimates within a few seconds and a smaller error range from the processor/accelerator level to the large system level.

中文翻译:

多通信分层HPL模型及其在GPU集群中的应用

高性能 Linpack (HPL) 是评估计算系统能力的最流行的基准之一,自 1980 年代初以来一直被用作比较计算系统性能的标准。在初始系统设计阶段,快速准确地估计系统的能力至关重要。然而,基于单核和单通信层的原始 HPL 数学模型对包含大量核的现代处理器和加速器产生不同的精度。为了减少 HPL 模型与实际系统之间的性能估计差距,我们提出了一种用于多通信分层 HPL 的数学模型。通过将其应用于 GPU 集群和知名系统来评估所提出模型的有效性。结果显示性能差异为 1。单个 GPU 上的 1%。GPU 集群和知名大型系统的平均差异分别为 5.5% 和 4.1%。与原始 HPL 模型相比,所提出的多通信分层 HPL 模型可在几秒钟内提供性能估计,并且从处理器/加速器级别到大型系统级别的误差范围更小。
更新日期:2021-06-29
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