A Dataflow Computing System for New Generation of Domestic Heterogeneous Many-Core Processors

Xiao Qian; Zhao Meijia; Li Mingfan; Shen Li; Chen Junshi; Zhou Wenhao; Wang Fei; An Hong

doi:10.7544/issn1000-1239.202220562

Journal of Computer Research and Development > 2023 > 60(10): 2405-2417. > DOI: 10.7544/issn1000-1239.202220562 CSTR: 32373.14.issn1000-1239.202220562

Xiao Qian, Zhao Meijia, Li Mingfan, Shen Li, Chen Junshi, Zhou Wenhao, Wang Fei, An Hong. A Dataflow Computing System for New Generation of Domestic Heterogeneous Many-Core Processors[J]. Journal of Computer Research and Development, 2023, 60(10): 2405-2417. DOI: 10.7544/issn1000-1239.202220562

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A Dataflow Computing System for New Generation of Domestic Heterogeneous Many-Core Processors

1.
Institute of Computer Science and Technology, University of Science and Technology of China, Hefei 230026
2.
National Suppercomputer Center in Wuxi, Wuxi, Jiangsu 214100
3.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084

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Author Bio:
Xiao Qian: born in 1988. PhD candidate. His main research interests include compiler optimization, data flow computing, and AI framework

Zhao Meijia: born in 1992. Master. Her main research interest includes AI framework

Li Mingfan: born in 1995. PhD candidate. His research interests include dataflow system, parallel and distributed computing in heterogeneous environments

Shen Li: born in 1981. PhD candidate. Her main research interests include compiler and AI basic software

Chen Junshi: born in 1990. PhD. His main research interests include high performance computing, general processor architecture, and optimization of scientific applications on large scale systems

Zhou Wenhao: born in 1992. Master. His main research interests include compiler optimization and many-core program environment

Wang Fei: born in 1981. PhD candidate. His main research interests include compiler optimization and many-core program environment

An Hong: born in 1963. PhD, professor, PhD supervisor. Her main research interests include parallel computing system and many-core chip architecture
Received Date: June 15, 2022
Revised Date: January 15, 2023
Available Online: May 22, 2023

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Abstract

Abstract

Today, scientific research has moved from the era of computational science to the era of data science. Discovering laws from massive data and breaking through bottlenecks in scientific development are the main goals of the data science paradigm. At the same time, high performance computers are also paying more and more attention on intelligent computing power. Integrating AI algorithms on the basis of traditional high performance computing methods (HPC+AI) is more conducive to solving practical science problems in the era of data science, and can give full play to the intelligent computing power of high performance computers. However, on domestic HPC systems, especially on HPC systems constructed by the new generation of domestic heterogeneous many-core processors, there are many challenges to support HPC+AI programs. In this paper, we propose a data flow computing system for domestic heterogeneous many-core processors, which is called swFLOWpro. The system supports the use of TensorFlow interface to build data flow programs, and realizes many-core parallel acceleration transparent to users, and implements two-level parallel strategy based on the whole processor perspective. Testing on sw26010pro processor, swFLOWpro can get up to 545 times single core group (CG) many-core speedup ratio for typical OP, 346 times for typical deep learning models. Compared with the single CG of sw26010pro, we execute ResNet50 model on all the 6 CGs for one whole processor, and the speedup ration is up to 4.96 times, whose parallel efficiency is 82.6%. Experiments show that swFLOWpro can support the efficient execution of data flow programs represented by deep learning on domestic heterogeneous many-core processors.
- dataflow,
- deep learning,
- heterogeneous many-core,
- swFLOWpro system,
- high performance computing

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References (24)

References

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