WAPM: A parallel programming model in large scale Internet distributed computing environments

2009 ◽  
Vol 29 (8) ◽  
pp. 2161-2166 ◽  
Author(s):  
Chong-guo FU ◽  
Sheng-chao XU
Author(s):  
Masaaki Suzuki ◽  
Hiroshi Okuda ◽  
Genki Yagawa

The authors have applied Message Passing Interface (MPI) / OpenMP hybrid parallel programming model to molecular dynamics (MD) method for simulating a protein structure on a symmetric multiprocessor (SMP) cluster architecture. In that architecture, it can be expected that the hybrid parallel programming model, which uses the message passing library such as MPI for inter-SMP node communication and the loop directives such as OpenMP for intra-SMP node parallelization, is the most effective one. In this study, the parallel performance of the hybrid style has been compared with that of conventional flat parallel programming style, which uses only MPI, both in case that the fast multipole method (FMM) is employed for computing long-distance interactions and that is not employed. The computer environments used here are Hitachi SR8000/MPP placed at the University of Tokyo. The results of calculation are as follows: Without using FMM, the parallel efficiency using 16 SMP nodes (128 PEs) is: - 90% with the hybrid style, - 75% with the flat-MPI style, for MD simulation with 33,402 atoms. With FMM, the parallel efficiency using 16 SMP nodes (128 PEs) is: - 60% with the hybrid style, - 48% with the flat-MPI style, for MD simulation with 117,649 atoms.


2015 ◽  
Vol 44 (4) ◽  
pp. 832-866 ◽  
Author(s):  
Ren Li ◽  
Haibo Hu ◽  
Heng Li ◽  
Yunsong Wu ◽  
Jianxi Yang

2016 ◽  
Vol 43 ◽  
pp. 95-103 ◽  
Author(s):  
James A. Ross ◽  
David A. Richie ◽  
Song J. Park ◽  
Dale R. Shires

2021 ◽  
Vol 24 (1) ◽  
pp. 157-183
Author(s):  
Никита Андреевич Катаев

Automation of parallel programming is important at any stage of parallel program development. These stages include profiling of the original program, program transformation, which allows us to achieve higher performance after program parallelization, and, finally, construction and optimization of the parallel program. It is also important to choose a suitable parallel programming model to express parallelism available in a program. On the one hand, the parallel programming model should be capable to map the parallel program to a variety of existing hardware resources. On the other hand, it should simplify the development of the assistant tools and it should allow the user to explore the parallel program the assistant tools generate in a semi-automatic way. The SAPFOR (System FOR Automated Parallelization) system combines various approaches to automation of parallel programming. Moreover, it allows the user to guide the parallelization if necessary. SAPFOR produces parallel programs according to the high-level DVMH parallel programming model which simplify the development of efficient parallel programs for heterogeneous computing clusters. This paper focuses on the approach to semi-automatic parallel programming, which SAPFOR implements. We discuss the architecture of the system and present the interactive subsystem which is useful to guide the SAPFOR through program parallelization. We used the interactive subsystem to parallelize programs from the NAS Parallel Benchmarks in a semi-automatic way. Finally, we compare the performance of manually written parallel programs with programs the SAPFOR system builds.


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