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Performance Benchmarking of HPC systems is an ongoing effort that seeks toprovide information that will allow for increased performance and improve thejob schedulers that manage these systems. We develop a benchmarking tool thatutilizes machine learning models and gathers performance data onGPU-accelerated nodes while they perform material segmentation analysis. Thebenchmark uses a ML model that has been converted from Caffe to PyTorch usingthe MMdnn toolkit and the MINC-2500 dataset. Performance data is gathered ontwo ERDC DSRC systems, Onyx and Vulcanite. The data reveals that whileVulcanite has faster model times in a large number of benchmarks, and it isalso more subject to some environmental factors that can cause performancesslower than Onyx. In contrast the model times from Onyx are consistent acrossbenchmarks.

Benchmarking Performance of Deep Learning Model for Material Segmentation on Two HPC Systems