Porting and Parallel Optimization of Common Operators Based on Heterogeneous Programming Models

Ma Zhaojia; Shao En; Di Zhanyuan; Ma Lixian

doi:10.7544/issn1000-1239.202330869

Journal of Computer Research and Development > 2025 > Corrected proof > DOI: 10.7544/issn1000-1239.202330869 CSTR: 32373.14.issn1000-1239.202330869

Ma Zhaojia, Shao En, Di Zhanyuan, Ma Lixian. Porting and Parallel Optimization of Common Operators Based on Heterogeneous Programming Models[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330869

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Porting and Parallel Optimization of Common Operators Based on Heterogeneous Programming Models

Ma Zhaojia^{1, 2,},
Shao En^{1, 2, 3, ,},
Di Zhanyuan^{1, 2},
Ma Lixian^{1, 2}

1.
State Key Lab of Processors (Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Nanjing Institute of InforSuperBahn, Nanjing 211100

Funds: This work was supported by the National Key Research and Development Program of China (2021YFB0300202), the National Natural Science Foundation of China (62102396), the Beijing Nova Program (Z211100002121143, 20220484217), the Youth Innovation Promotion Association of Chinese Academy of Sciences (2021099), the CCF-Ant Research Fund (CCF-AFSGRF20230207), the Pilot for Major Scientific Research Facility of Jiangsu Province (BM2021800), and the Innovation Funding of ICT, CAS (E461030).

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Author Bio:
Ma Zhaojia: born in 2000. Master candidate. His main research interest includes high-performance computing

Shao En: born in 1988. PhD, senior engineer, master supervisor. Senior member of CCF. His main research interests include computer interconnect network, SYCL, and high-performance computing and systems software

Di Zhanyuan: born in 1999. PhD candidate. His main research interests include parallel computing, compilation optimization, and code generation

Ma Lixian: born in 1993. PhD candidate, engineer. His main research interests include high performance computing and machine learning
Received Date: October 30, 2023
Revised Date: July 01, 2024
Accepted Date: August 08, 2024
Available Online: August 13, 2024

Graphical Abstract

Abstract

Abstract

As the fundamental computing component in constructing large-scale supercomputing systems, GPUs are undergoing architectural diversity and heterogeneity. GPU accelerators from various chip manufacturers exhibit significant variations in their architectural designs. Accelerator diversity and programming model diversity are important technical trends for building large-scale supercomputing systems. Diverse accelerators require developers to provide high-performance software for multiple hardware platforms, resulting in software duplication. To reduce the cost of duplication, the unified programming model SYCL (system-wide compute language) adapts to multiple hardware platforms, but SYCL’s performance on different hardware is not as good as the native programming model of the platform, and SYCL’s performance needs to be further optimized. In order to be able to apply the mature and complete CUDA (compute unified device architecture) programming ideas and high-performance programs to SYCL, it is necessary to discuss the performance of high-performance CUDA programs ported to SYCL on multiple platforms and the ideas for further optimization. Based on software-hardware co-design, we propose paraTRANS: a common operator optimization system for the code migration process of cross-heterogeneous programming model SYCL, and give the optimization methods for the migrated SYCL GEMM (general matrix multiplication) in different scenarios. We evaluate the performance of SYCL GEMM optimized by paraTRANS, which can achieve 96.95% of CUDA’s FLOPS on the original NVIDIA RTX 3090, and 100.47% of CUDA’s hardware peak performance percentage on AMD MI100, both close to the level before migration. This paper provides ideas for porting high-performance CUDA code to SYCL and further optimization.
- SYCL,
- cross heterogeneous architecture,
- code migration,
- GEMM,
- parallel optimization

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References (21)

References

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