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Co-designing OpenMP Features Using OMPT and Simulation Tools

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OpenMP: Portable Multi-Level Parallelism on Modern Systems (IWOMP 2020)

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Abstract

The design of future HPC systems is trending towards more heterogeneity with different types of accelerators, special purpose instructions sets, system-on-chip designs, complex memory hierarchies, and multiple memory coherence domains. This complexity exacerbates the design challenges and testing of programming models which aim to provide a high-level interface while also producing high performance programs. In this paper we describe how to use full-system architectural simulation of OpenMP applications to provide a platform for experimenting with OpenMP extensions on future architecture designs. Furthermore, we put forward the concept of integrating the OpenMP Tools API (OMPT) in conjunction with other tools (performance, emulators, etc.) to help speed up the use of architectural simulators with new OpenMP implementations. In this work, we evaluate an initial implementation of this simulation testbed design using gem5, an open source full system simulation, with the EPCC OpenMP micro-benchmarks that are instrumented with OMPT. We show that OMPT can be a powerful tool for the codesign of future systems models and programming model features.

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Acknowledgments

This research was funded by the Laboratory Directed Research and Development (LDRD) Program of the Oak Ridge National Laboratory managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. This research also used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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Correspondence to Matthew Baker .

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Baker, M., Hernandez, O., Young, J. (2020). Co-designing OpenMP Features Using OMPT and Simulation Tools. In: Milfeld, K., de Supinski, B., Koesterke, L., Klinkenberg, J. (eds) OpenMP: Portable Multi-Level Parallelism on Modern Systems. IWOMP 2020. Lecture Notes in Computer Science(), vol 12295. Springer, Cham. https://doi.org/10.1007/978-3-030-58144-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-58144-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58143-5

  • Online ISBN: 978-3-030-58144-2

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