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2010•arXiv (Cornell University)Open access

A Hybrid Parallelization of AIM for Multi-Core Clusters: Implementation Details and Benchmark Results on Ranger

Fangzhou Wei, Ali E. Yılmaz

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Abstract

This paper presents implementation details and empirical results for a hybrid message passing and shared memory paralleliziation of the adaptive integral method (AIM). AIM is implemented on a (near) petaflop supercomputing cluster of quad-core processors and its accuracy, complexity, and scalability are investigated by solving benchmark scattering problems. The timing and speedup results on up to 1024 processors show that the hybrid MPI/OpenMP parallelization of AIM exhibits better strong scalability (fixed problem size speedup) than pure MPI parallelization of it when multiple cores are used on each processor.

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What this paper is about

This paper presents implementation details and empirical results for a hybrid message passing and shared memory paralleliziation of the adaptive integral method (AIM). AIM is implemented on a (near) petaflop supercomputing cluster of quad-core processors and its accuracy, complexity, and scalability are investigated by solving benchmark scattering problems. The timing and speedup results on up to 1024 processors show that the hybrid MPI/OpenMP parallelization of AIM exhibits better strong scalability (fixed problem size speedup) than pure MPI parallelization of it when multiple cores are used on each processor.

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Available abstract

This paper presents implementation details and empirical results for a hybrid message passing and shared memory paralleliziation of the adaptive integral method (AIM). AIM is implemented on a (near) petaflop supercomputing cluster of quad-core processors and its accuracy, complexity, and scalability are investigated by solving benchmark scattering problems. The timing and speedup results on up to 1024 processors show that the hybrid MPI/OpenMP parallelization of AIM exhibits better strong scalability (fixed problem size speedup) than pure MPI parallelization of it when multiple cores are used on each processor.

Key concepts: Speedup, Parallel computing, Benchmark (surveying), Computer science, Scalability, Multi-core processor, Cluster (spacecraft), Message passing

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