Abstract
Understanding the I/O behavior of parallel applications is fundamental both to optimize and propose tuning strategies for improving the I/O performance. In this paper we present the outcome of an I/O optimization project carried out for the parallel astrophysical Plasma Physics application Acronym, a well-tested particle-in-cell code for astrophysical simulations. Acronym is used on several different supercomputers in combination with the HDF5 library, providing the output in form of self-describing files. To address the project, we did a characterization of the main parallel I/O sub-system operated at LRZ. Afterwards we have applied two different strategies that improve the initial performance, providing a solution with scalable I/O. The results obtained show that the total application time is 4.5x faster than the original version for the best case.
The authors thank the Acronym developer team for their contributions and the good teamwork. Special thanks goes to Gerald Mathias for reading the manuscript and providing valuable feedback. Computations for this project were done on SuperMUC at LRZ, a member of the Gauss Centre for Supercomputing (GCS).
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Mendez, S., Hammer, N.J., Karmakar, A. (2018). Analyzing the I/O Scalability of a Parallel Particle-in-Cell Code. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_1
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DOI: https://doi.org/10.1007/978-3-030-02465-9_1
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