Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 43, Pages: 1-7
J. Sairabanu1*, M. Rajasekhara Babu1 , Arunava Kar1 and Aritra Basu2
1School of Computer Science and Engineering (SCOPE), VIT University, Vellore - 632014, Tamil Nadu, India;[email protected], [email protected], [email protected] 2School of Electronics Engineering (SENSE), VIT University, Vellore - 632014, Tamil Nadu, India; [email protected]
*Author for correspondence
J. Sairabanu School of Computer Science and Engineering (SCOPE), VIT University, Vellore - 632014, Tamil Nadu, India; [email protected]
Objective: Due to the recent development of multiple parallel programming tools with varying features, it is difficult to choose the best tool according to the needs of the user. Methods: This problem is addressed by making a comparative analysis study of different features like license type, source code availability, targeted platforms and languages supportedby these diverse tools. There are different parallel programming languages that support the present multi-core architecture like Message Passing Interface (MPI) and Open Multi-Processing (OpenMP). These are widely used to provide different performance characteristics of parallelism in different test cases. The new architecture and the complexity strengthens the need to monitor and analyze the performance of the various OpenMP kernels and constructs on multi-core processors. Findings: There are many papers that have been published in the past but non of them focuses on a comparative study among the performance analysis tools (PATs) that we mostly opt for. This paper intends to analyze the parallel computing ability of OpenMP and MPI, besides helping the user to understand which tool suites his task the best. Improvement: This study shows that MPI offers the best performance characteristics in the field of shared memory programming whereas OpenMP is a better choice because of the global style of the resulting program. It also provides a roadmap to select the best tool when designing a parallel programming system.
Keywords: MPI, Multi-Core System, Multi-Threaded System,OpenMP, PAT, Parallelization,
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