Automatic Optimization of Parallel Parameters for Sunway TaihuLight Super-computer Application Program

Abstract

The finite difference algorithm is often applied to Sunway TaihuLight to complete atmospheric simula-tion, oil exploration, and other tasks. However, due to the high communication cost and calculation density of the algorithm, the complex structure of Sunway system and large scale of application data, it is difficult to obtain reason-able parameters for data distribution during application construction and execution, and the performance of corres-ponding applications is difficult to be satisfactory. According to the hardware characteristics of Sunway 26010 processor, a parallel parameter automatic optimization method based on genetic algorithm is proposed. The data size parameter of message passing interface and the kernel are automatically optimized, and the two-dimensional finite difference algorithm is tested for high performance. The method finds the better solution in the 1 billion addressing space and achieves an acceleration ratio of 10.79 times compared with the automatic allocation of compiler system. In addition, compared with the automatic allocation of compiler system, this paper achieves 6.31 times acceleration for optimizing reverse time migration. This method realizes the automatic optimization of the application data scale parameters and provides useful guidance for the high-performance parallel optimization of domestic heterogeneous many-core processors.

Keywords

• parallel computing
• parameter automatic optimization
• genetic algorithm
• sunway heterogeneous multi-core processor
• finite difference algorithm