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Today, deep learning is an essential technology for our life. To solve morecomplex problems with deep learning, both sizes of training datasets and neuralnetworks are increasing. To train a model with large datasets and networks,distributed deep neural network (DDNN) training technique is necessary. Forlarge-scale DDNN training, HPC clusters are a promising computationenvironment. In large-scale DDNN on HPC clusters, I/O performance is criticalbecause it is becoming a bottleneck. Most flagship-class HPC clusters havehierarchical storage systems. For designing future HPC storage systems, it isnecessary to quantify the performance improvement effect of the hierarchicalstorage system on the workloads. This paper demonstrates the quantitativeperformance analysis of the hierarchical storage system for DDNN workload in aflagship-class supercomputer. Our analysis shows how much performanceimprovement and volume increment of the storage will be required to meet theperformance goal.

Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning