Distributed deep learning system for cancerous region detection on Sunway TaihuLight

Abstract

To explore the potential of distributed training on deep neural networks, we implement several distributed algorithms with the basis of swFlow on the world-leading supercomputer, Sunway TaihuLight. Based on two naive designs of parameter server and ring all-reduce, we present the limitation of the communication model and discuss the optimizations for adapting the five-level interconnect architecture of Sunway system. To reduce the communication bottleneck on large scale system, multi-severs and hierarchical ring all-reduce models are introduced. With a benchmark from deep learning-based cancerous region detection algorithm, the average parallel efficiency obtains over 80% for at most 1024 processors. It reveals the great opportunity for joint combination of deep learning and HPC system.