Streamlining distributed Deep Learning I/O with ad hoc file systems
2021 IEEE International Conference on Cluster Computing (CLUSTER)(2021)
摘要
With evolving techniques to parallelize Deep Learning (DL) and the growing amount of training data and model complexity, High-Performance Computing (HPC) has become increasingly important for machine learning engineers. Although many compute clusters already use learning accelerators or GPUs, HPC storage systems are not suitable for the I/O requirements of DL workflows. Therefore, users typically ...
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关键词
Training,Deep learning,High-speed networks,File systems,Training data,Distributed databases,Computer architecture
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