Benchmarking Performance of Deep Learning Model for Material
  Segmentation on Two HPC Systems
Warren R. Williams S. Ross Glandon Luke L. Morris Jing-Ru C. Cheng
Performance Benchmarking of HPC systems is an ongoing effort that seeks to
provide information that will allow for increased performance and improve the
job schedulers that manage these systems. We develop a benchmarking tool that
utilizes machine learning models and gathers performance data on
GPU-accelerated nodes while they perform material segmentation analysis. The
benchmark uses a ML model that has been converted from Caffe to PyTorch using
the MMdnn toolkit and the MINC-2500 dataset. Performance data is gathered on
two ERDC DSRC systems, Onyx and Vulcanite. The data reveals that while
Vulcanite has faster model times in a large number of benchmarks, and it is
also more subject to some environmental factors that can cause performances
slower than Onyx. In contrast the model times from Onyx are consistent across
benchmarks.