Abstract
Recent HPC systems utilize parallel file systems such as GPFS and Lustre to cope with the huge demand of data-intensive applications. Although most of the HPC systems provide performance tuning tools on compute nodes, there is not enough chance to tune I/O activities on parallel file systems including high-speed interconnects among compute nodes and file systems. We propose an I/O performance optimization framework using log data of parallel file systems and interconnects in a holistic way for improving performance of HPC systems including I/O nodes and parallel file systems. We demonstrate our framework at the K computer with two I/O benchmarks for the original and the enhanced MPI-IO implementations. Its I/O analysis has revealed that I/O performance improvements achieved by the enhanced MPI-IO implementation are due to effective utilization of parallel file systems and interconnects among I/O nodes compared with the original MPI-IO implementation.
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This research used computational resources of the K computer provided by the RIKEN Center for Computational Science.
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Tsujita, Y., Furutani, Y., Hida, H., Yamamoto, K., Uno, A. (2020). Characterizing I/O Optimization Effect Through Holistic Log Data Analysis of Parallel File Systems and Interconnects. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science(), vol 12321. Springer, Cham. https://doi.org/10.1007/978-3-030-59851-8_11
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DOI: https://doi.org/10.1007/978-3-030-59851-8_11
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