A Performance Data Collection Method for Computing Software in Heterogeneous Systems

Gu Beibei; Qiu Jiyan; Wang Ning; Chen Jian; Chi Xuebin

doi:10.7544/issn1000-1239.202440512

Journal of Computer Research and Development > 2025 > Accepted Manuscript > DOI: 10.7544/issn1000-1239.202440512 CSTR: 32373.14.issn1000-1239.202440512

Gu Beibei, Qiu Jiyan, Wang Ning, Chen Jian, Chi Xuebin. A Performance Data Collection Method for Computing Software in Heterogeneous Systems[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440512

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A Performance Data Collection Method for Computing Software in Heterogeneous Systems

Gu Beibei^{1, 2,},
Qiu Jiyan^{1, 2},
Wang Ning³,
Chen Jian³,
Chi Xuebin^1, ,

1.
Computer Network Information Center, Chinese Academy of Science, Beijing 100083
2.
University of Chinese Academy of Sciences, Beijing 101408
3.
Beijing Paratera Co., Ltd., Beijing 100094

Funds: This work was supported by the National Natural Science Foundation of China (62372428).

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Author Bio:
Gu Beibei: born in 1982. PhD candidate, associate professor. Member of CCF. Her main research interest includes performance evaluation and analysis of high performance computing software applications

Qiu Jiyan: born in 1998. PhD candidate. His main research interests include AI for science and high performance computing

Wang Ning: born in 1988. Bachelor. His main research interest includes performance analysis and optimization of general-purpose processors

Chen Jian: born in 1977. PhD. Vice chairman of CCF, standing committee member of CCF TCHPC, executive committee member of CCF TCAIPR. His main research interests include AI and high performance computing

Chi Xuebin: born in 1963. PhD, professor, PhD supervisor. His main research interest includes parallel computing
Received Date: June 16, 2023
Revised Date: January 14, 2025
Accepted Date: January 25, 2025
Available Online: January 25, 2025

Graphical Abstract

Abstract

Abstract

Supercomputing has rapidly developed from traditional CPU clusters to heterogeneous platforms. With the type conversion of hardware platforms, it faces significant challenges in optimizing computing software programs and performance evaluation. Currently, some international mainstream parallel program performance analysis tools and software generally have low compatibility with domestic supercomputing heterogeneous system processor products, often requiring instrumentation and recompilation of code, and low accuracy in single node performance data collection. To improve these shortcomings, this article proposes a floating-point performance data collection method for heterogeneous system computing software. This method is based on the domestic supercomputing system verification platform to develop and verify the floating-point performance collection prototype. At present, effective collection of single node and multi node performance indicator data has been achieved, and it is non-invasive to the original program. There is no need to modify the code of the monitored program for monitoring in a plug-in manner, making it highly versatile. Finally, we conducted comparative experimental analysis with three types of programs: rocHPL, Cannon, and mixbench, and conducted performance data collection monitoring research on ResNet (residual network, ResNet) program for AI computing. We have demonstrated that the collection method proposed in this article has high accuracy, achieves the expected collection effect in experiments, and has good reference value for program optimization, verifying the effectiveness of the proposed method.
- heterogeneous systems,
- performance indicators,
- floating-point data,
- collection program,
- performance evaluation

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References (13)

References

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