Issue No. 12 - Dec. (2016 vol. 27)
Sascha Hunold , Vienna University of Technology, Faculty of Informatics, Research Group for Parallel Computing, Vienna, Austria
Alexandra Carpen-Amarie , Vienna University of Technology, Faculty of Informatics, Research Group for Parallel Computing, Vienna, Austria
The Message Passing Interface (MPI) is the prevalent programming model used on today's supercomputers. Therefore, MPI library developers are looking for the best possible performance (shortest run-time) of individual MPI functions across many different supercomputer architectures. Several MPI benchmark suites have been developed to assess the performance of MPI implementations. Unfortunately, the outcome of these benchmarks is often neither reproducible nor statistically sound. To overcome these issues, we show which experimental factors have an impact on the run-time of blocking collective MPI operations and how to measure their effect. Finally, we present a new experimental method that allows us to obtain reproducible and statistically sound measurements of MPI functions.
Benchmark testing, Time measurement, Statistical analysis, Supercomputers
S. Hunold and A. Carpen-Amarie, "Reproducible MPI Benchmarking is Still Not as Easy as You Think," in IEEE Transactions on Parallel & Distributed Systems, vol. 27, no. 12, pp. 3617-3630, 2016.