Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11): 32-40.doi: 10.11896/jsjkx.230300123

• High Performance Computing • Previous Articles     Next Articles

Study on Cross-platform Heterogeneous Parallel Computing for Lattice Boltzmann Multi-phase Flow Simulations Based on SYCL

DING Yue, XU Chuanfu, QIU Haozhong, DAI Weixi, WANG Qingsong, LIN Yongzhen, WANG Zhenghua   

  1. College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2023-03-14 Revised:2023-06-12 Online:2023-11-15 Published:2023-11-06
  • About author:DING Yue,born in 1999,postgraduate.Her main research interests include parallel and high performance computing applications and so on.XU Chuanfu,born in 1980,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include parallel computing and applications and so on.

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Abstract: Heterogeneous parallel architecture is an important technology trend in current high-performance computing.Since various heterogeneous platforms usually support different programming models,the development of cross-platform performance portable heterogeneous parallel application is difficult.SYCL is a single-source cross-platform parallel programming open standard based on C++ language.The current research on SYCL mainly focuses on the performance comparison with other parallel programming models,but there are few researches on the different parallel kernel implementations provided in SYCL and their performance optimization.To address this situation,the open source multi-phase flow simulation software openLBMflow is implemented based on the SYCL programming model for cross-platform heterogeneous parallel simulation.The performance optimization methods of SYCL parallel applications are systematically summarized by comparing the basic parallel version,the fine-grained tuned ND-range parallel version and many-to-one mapping computation to work-items method.The results show that on Intel Xeon Platinum 9242 CPU and NVIDIA Tesla V100 GPU,the basic parallel kernel achieves a speedup of 2.91 on CPU without additional tuning compared to the optimized OpenMP parallel implementation,indicating the out-of-the-box performance advantage of SYCL.Using the basic parallel version as a baseline,the ND-range parallel version achieves up to 1.45x speedup on the CPU and 2.23x speedup on the GPU respectively by changing the work-group size and shape.By changing and optimizing the number and shape of lattices processed per work-item,the many-to-one mapping computation to work-items method achieves up to 1.57x speedup on the CPU and 1.34x speedup on the GPU respectively compared to the basic parallel version.The results show that SYCL parallel applications are more suitable for many-to-one mapping computation to work-items method on the CPU and ND-range parallel kernels on the GPU to improve performance.

Key words: SYCL, Lattice Boltzmann method, Multi-phase flows imulation, Heterogeneous parallel computing, Cross-platform parallel programming model

CLC Number: 

  • TP391
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