I / O Bottleneck Investigation in Deep Learning Systems
semanticscholar(2018)
摘要
As deep learning systems continue to grow in importance, several researchers have been analyzing approaches to make such systems efficient and scalable on high-performance computing platforms. As computational parallelism increases, however, data I/O becomes the major bottleneck limiting the overall system scalability. In this paper, we present a detailed analysis of the performance bottlenecks of Caffe on large supercomputing systems and propose an optimized I/O plugin, namely LMDBIO. Throughout the paper, we present six sophisticated data I/O techniques that address five major problems in state of the art I/O subsystem. Our experimental results show that Caffe/LMDBIO can improve the overall execution time of Caffe/LMDB by 77-fold compared with LMDB.
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