Distributed Deep Learning With GPU-FPGA Heterogeneous Computing

Abstract

In distributed deep learning (DL), collective communication algorithms, such as Allreduce, used to share training results between graphical processing units (GPUs) are an inevitable bottleneck. We hypothesize that a cache access latency occurred at every Allreduce is a significant bottleneck in the current computational systems with high-bandwidth interconnects for distributed DL. To reduce this frequency of latency, it is important to aggregate data at the network interfaces. We implement a data aggregation circuit in a field-programmable gate array (FPGA). Using this FPGA, we proposed novel Allreduce architecture and training strategy without accuracy degradation. Results of the measurement show Allreduce latency reduction to 1/4. Our system can also conceal about 90% of the communication overhead and improve scalability by 20%. The end-to-end time consumed for training in distributed DL with ResNet-50 and ImageNet is reduced to 87.3% without any degradation in validation accuracy.