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SUMMARY:Parallelism-Driven Performance Analysis Techniques for Task Parallel Programs LOCATION:CoRE A (301) DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Performance analysis of parallel progra ms continues to be challenging for programmers. Programmers have to account for several factors to extract the best possible performance from parallel programs. First, programs must have adequate parallel computation that is evenly distributed to keep all processors busy during execution. Second, pr ograms must reduce secondary effects caused by interactions in hardware, wh ich can degrade performance. Third, performance problems due to inadequate parallel computation and secondary effects can get magnified when programs are executed at scale. Fourth, programs must ensure minimal overhead from o ther sources like runtime schedulers, lock contention, and heavyweight abst ractions in the software stack. To diagnose performance issues in parallel programs, programmers rely on profilers to obtain performance insights. Alt hough profiling is a well-researched area, existing profilers primarily pro vide information on where a program spends its time. They fail to highlight the parts of the program that matter for improving the performance and the scalability of a program.
This dissertation makes a case for using l ogical parallelism to identify parts of the program that matter for improvi ng the performance of task parallel programs. It makes the following contri butions. First, it describes a scalable and an efficient technique to compu te the logical parallelism of a program. Logical parallelism defines the sp eedup of a program in the limit. Logical parallelism is an ideal metric to assess if a program has adequate parallel computation to attain scalable sp eedup on any number of processors. Second, it introduces a novel performanc e model to compute the logical parallelism of a program. To enable parallel ism computation, the performance model encodes the series-parallel relation s in the program and fine-grain work performed in an execution. Third, it p resents a technique, called what-if analyses, that uses the parallelism com putation and the performance model to identify parts of a program that matt er for improving the parallelism. Finally, it describes a differential anal ysis technique that uses the performance model to identify parts of the pro gram that matter for addressing secondary effects.
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