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.

HPC系统性能基准测试中发现，使用机器学习模型对使用GPU加速节点进行材料分割分析时，Onyx和Vulcanite两个ERDC DSRC系统的表现存在差异，Onyx在各项测试中的模型时间保持一致，而Vulcanite在大多数基准测试中速度较快，但也受环境因素影响导致部分性能较慢。

深度学习模型在两个高性能计算系统上的材料分割性能基准测试