Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (8): 32-40.doi: 10.11896/jsjkx.200500093

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Parallelizing Multigrid Application Using Data-driven Programming Model

GUO Jie1, GAO Xi-ran2, CHEN Li2, FU You1, LIU Ying2,   

  1. 1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China
    2 State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:GUO Jie, born in 1996, postgraduate.His main research interests includeparal-lel optimization and parallel compilation.
    CHEN Li, born in 1970, Ph.D, associate professor, is a member of China Computer Federation.Her main research interests include parallel programming languages and parallelizing compiling techniques.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61521092), National Key R&D Program of China(2016YFB0200803) and Key R&D Project of Shandong Province(2019GGX101066).

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Abstract: Multigrid is an important family of algorithms to accelerate the convergence of iterative solvers for linear systems, and it plays an important role in large-scale scientific computing.At present, distributed-memory systems have evolved to large scale systems based on multi-core nodes or heterogeneous nodes with accelerators.Legacy applications face the urgent need to be ported to modern supercomputers with diverse node-level architectures.In this paper, a data-driven programming language, AceMesh is introduced, and using this directive language, NAS MG is ported to two home-made supercomputers which are Tianhe-2 and Sunway TaihuLight supercomputer.This paper shows how to taskify computation loops and communication-related codes in AceMesh, and analyzes the characteristics on its task graph and on its computation-communication overlapping.Experimental results show that compared with traditional programming models, the AceMesh versions achieve relative speedup up to 1.19X and 1.85X on Sunway TaihuLight and Tianhe-2 respectively.Analyses show that performance improvements come from two main reasons, memory-related optimization and communication overlapping optimization.At last, future directions are put forward to further optimize inter-process communications for the AceMesh version.

Key words: Computation-communication overlap, Data-driven task parallel programming model, Heterogeneous many-core, MPI legacy application, Multigrid

CLC Number: 

  • TP311
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