Abstract
In 2008 task based parallelism was added to OpenMP as the major update for version 3.0. Tasks provide an easy way to express dynamic parallelism in OpenMP applications. However, achieving a good performance with OpenMP task-parallel programs is a challenging task. OpenMP runtime systems are free to schedule, interrupt and resume tasks in many different ways, thereby complicating the prediction of the program behavior by the programmer. Hence, it is important for a programmer to get support from performance tools to understand the performance characteristics of his application.Different performance tools follow different approaches to collect this information and to present it to the programmer. Important differences are the amount of information which is gathered and stored and the amount of overhead that is introduced. We identify typical usage patterns of OpenMP tasks in application codes. Then we compare the usability of several performance tools for task-parallel applications. We concentrate our investigations on two topics, the amount and usefulness of the measured data and the overhead introduced by the performance tool.
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Acknowledgements
Parts of this work were funded by the German Federal Ministry of Research and Education (BMBF) under Grant No. 01IH11006 (LMAC).
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Schmidl, D., Terboven, C., an Mey, D., Müller, M.S. (2014). Suitability of Performance Tools for OpenMP Task-Parallel Programs. In: Knüpfer, A., Gracia, J., Nagel, W., Resch, M. (eds) Tools for High Performance Computing 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-08144-1_3
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