Abstract
Optimizing a parallel program is often difficult. This is true, in particular, for inexperienced programmers who lack the knowledge and intuition of advanced parallel programmers. We have developed a framework that addresses this problem by automating the analysis of static program information and performance data, and offering active suggestions to programmers. Our tool enables experts to transfer programming experience to new users. It complements today’s parallelizing compilers in that it helps to tune the performance of a compiler-optimized parallel program. To show its applicability, we present two case studies that utilize this system. By simply following the suggestions of our system, we were able to reduce the execution time of benchmark programs by as much as 39%.
This work was supported in part by NSF grants #9703180-CCR, #9872516-EIA, and #9975275-EIA. This work is not necessarily representative of the positions or policies of the U. S. Government
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Kim, S.W., Park, I., Eigenmann, R. (2001). A Performance Advisor Tool for Shared-Memory Parallel Programming. In: Midkiff, S.P., et al. Languages and Compilers for Parallel Computing. LCPC 2000. Lecture Notes in Computer Science, vol 2017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45574-4_18
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DOI: https://doi.org/10.1007/3-540-45574-4_18
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