An In-Network Parameter Aggregation using DPDK for Multi-GPU Deep Learning

Abstract

In distributed deep neural network using remote GPU nodes, communication occurs iteratively between remote nodes for gradient aggregation. This communication latency limits the benefit of distributed training with faster GPUs. In distributed deep learning using the remote GPUs, workload of gradient aggregation is imposed on a host machine. In this paper, we therefore propose to offload the gradient aggregation to a DPDK (Data Plane Development Kit) based network switch between the host machine and remote GPUs. In this approach, the aggregation process is completed in the network using extra computation resources in the network switch. We evaluate the proposed switch when GPUs and the host communicate with a standard IP communication and a PCI Express (PCIe) over 40Gbit Ethernet (40GbE) product, respectively. The evaluation results using a standard IP communication show that the aggregation is accelerated by 2.2-2.5x compared to the aggregation executed by a host machine. The results using the PCIe over 40GbE product show that the proposed switch outperforms the aggregation done by the host machine by 1.16x. This approach is thus useful for distributed training with multiple GPUs.