Parallel Programming Environment for OpenMP

Insung Park,Rudolf Eigenmann,Seon Wook Kim,Michael J Voss

doi:10.1155/2001/195437

Insung Park, Rudolf Eigenmann + Show 2 more

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https://doi.org/10.1155/2001/195437

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Journal: Scientific Programming	Publication Date: Jan 1, 2001
Citations: 34	License type: CC BY 3.0

Affiliation: Purdue University West Lafayette

Abstract

We present our effort to provide a comprehensive parallel programming environment for the OpenMP parallel directive language. This environment includes a parallel programming methodology for the OpenMP programming model and a set of tools (Ursa Minor and InterPol) that support this methodology. Our toolset provides automated and interactive assistance to parallel programmers in time-consuming tasks of the proposed methodology. The features provided by our tools include performance and program structure visualization, interactive optimization, support for performance modeling, and performance advising for finding and correcting performance problems. The presented evaluation demonstrates that our environment offers significant support in general parallel tuning efforts and that the toolset facilitates many common tasks in OpenMP parallel programming in an efficient manner.

Highlights

Today, new affordable multiprocessor workstations and high performance PCs are attracting a large number of users
Many new programmers are inexperienced and demand an easy programming model to harness the power of parallel computing
Perhaps the most serious of all is the lack of a good programming methodology

Summary

Introduction

New affordable multiprocessor workstations and high performance PCs are attracting a large number of users. Many new programmers are inexperienced and demand an easy programming model to harness the power of parallel computing. The recent parallel language standard for shared memory multiprocessor machines, OpenMP [7], promises a simple interface. Perhaps the most serious of all is the lack of a good programming methodology. A programmer who is to develop a parallel program faces a number of challenging questions: What are the known techniques for parallelizing this program? How much speedup can be expected from this program? What are the limitations for the parallelization of this program? It usually takes substantial experience to answer such questions. Many programmers do not have the time and resources to acquire this experience

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