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swRodinia: A Benchmark Suite for Exploiting Architecture Properties of Sunway Processor

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Benchmarking, Measuring, and Optimizing (Bench 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12614))

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Abstract

The Sunway processor has been demonstrated with superior performance by various scientific applications, domain specific frameworks and numerical algorithms. However, the optimization techniques that can fully exploit the architecture features are usually buried deep in large code bases, which prevents average programmers to understand such optimization techniques. Thus, the existing complex software fails to provide guidance for more programs embracing the computation power of Sunway processor. In this paper, we build a benchmark suite swRodinia by porting and optimizing the well-known Rodinia benchmark on Sunway processor. Specifically, we demonstrate several optimization techniques by tailoring the benchmarks to better leverage the architecture features for higher performance. Moreover, based on the optimization experiences, we derive several useful insights from both software and hardware perspectives, that not only guide the better utilization of current Sunway processor, but also reveal the direction of hardware improvements for future Sunway processor. We open source the swRodinia benchmark suite and encourage the community to enhance the benchmark with us continuously.

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Notes

  1. 1.

    https://github.com/JackMoriarty/rodinia_3.1_SW.

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Acknowledgment

This work is supported by National Key R&D Program of China (Grant No. 2020YFB150001), National Natural Science Foundation of China (Grant No. 62072018, 61502019 and 61732002), and the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (Grant No. 2019A12).

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Correspondence to Hailong Yang .

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Chen, B. et al. (2021). swRodinia: A Benchmark Suite for Exploiting Architecture Properties of Sunway Processor. In: Wolf, F., Gao, W. (eds) Benchmarking, Measuring, and Optimizing. Bench 2020. Lecture Notes in Computer Science(), vol 12614. Springer, Cham. https://doi.org/10.1007/978-3-030-71058-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-71058-3_2

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