A parallel MPI plus OpenMP plus OpenCL algorithm for hybrid supercomputations of incompressible flows

被引:22
|
作者
Gorobets, A. V. [1 ,2 ]
Trias, F. X. [1 ]
Oliva, A. [1 ]
机构
[1] Tech Univ Catalonia, ETSEIAT, Heat & Mass Transfer Technol Ctr, Terrassa 08222, Spain
[2] Keldysh Inst Appl Math, Moscow 125047, Russia
关键词
MPI; OpenMP; OpenCL; GPU; Parallel CFD; Turbulence; SCHUR-FOURIER DECOMPOSITION; GPU; COMPUTERS; SOLVER;
D O I
10.1016/j.compfluid.2013.05.021
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The work is devoted to the development of efficient parallel algorithms for large-scale simulations of incompressible flows on hybrid supercomputers based on massively-parallel accelerators. The governing equations are discretized using a high-order finite-volume scheme for Cartesian staggered meshes with the only restriction that, at least, one direction is periodic. Its "classical" MPI + OpenMP parallel implementation for CPUs was designed to scale till 100,000 CPU cores. The new hybrid algorithm is developed on a base of a multi-level parallel model that exploits several layers of parallelism of a modern hybrid supercomputer. In this model, MPI and OpenMP are used on the first two levels to couple nodes of a supercomputer and to engage its CPU cores. Then, computing accelerators are further used by means of the hardware independent OpenCL computing standard. In this way, the implementation is adapted to a general computing model with central processors and math co-processors. In this paper the work is focused on adapting the basic operations of the algorithm to architectures of Graphics Processing Units (GPU) without considering the multi-CPU communication scheme. Technology of porting the code to OpenCL is described, certain optimization approaches are presented and relevant performance results obtaining up to 80-90 GFLOPS on a GPU accelerator are demonstrated. Moreover, the experience with different CPU architectures is summarized and a comparison based on the particular application is given for AMD and NVIDIA GPUs as well as for CUDA and OpenCL frameworks. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:764 / 772
页数:9
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