The Case for Storage Optimization Decoupling in Deep Learning Frameworks
2021 IEEE International Conference on Cluster Computing (CLUSTER)(2021)
摘要
Deep Learning (DL) training requires efficient access to large collections of data, leading DL frameworks to implement individual I/O optimizations to take full advantage of storage performance. However, these optimizations are intrinsic to each framework, limiting their applicability and portability across DL solutions, while making them inefficient for scenarios where multiple applications compe...
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关键词
Training,Deep learning,Limiting,Conferences,Prototypes,Computer architecture,Cluster computing
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