Improving the Performance of Distributed TensorFlow with RDMA
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TensorFlow is an open-source software library designed for Deep Learning using dataflow graph computation. Thanks to the flexible architecture of TensorFlow, users can deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. In a distributed TensorFlow work process, it uses gRPC to connect between different nodes. However, when deploying training tasks on high performance computing clusters, the performance of gRPC becomes a bottleneck of distributed TensorFlow system. HPC clusters are usually equipped with Infiniband network, in addition to traditional TCP/IP network. But open-sourced TensorFlow has not taken this advantage. We present a RDMA-capable design of TensorFlow. By porting the Tensor send/receive parts of TensorFlow into RDMA verbs, we finally get nearly 6\(\times \) performance improvements over the original distributed TensorFlow, based on gRPC. The TensorFlow system with RDMA support shows a great scalability among the training scale.
KeywordsDistributed TensorFlow RDMA Infiniband Optimization
This research is conducted under Advanced Computer System Architecture (ACSA) Laboratory of University of Science and Technology of China, supported by the National Key Research and Development Program of China (Grant No. 2016YFB1000403).
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