SAFusion: Efficient Tensor Fusion with Sparsification Ahead for High-Performance Distributed DNN Training

Distributed deep neural networks (DNN) training systems deployed across workers have been widely used in various domains, while the communication overhead among workers for synchronizing gradient tensors often becomes the performance bottleneck. To optimize communication efficiency, state-of-the-art studies often apply both two techniques: i) gradient sparsification compression, which truncates the gradient to its largest elements to reduce the communication traffic, and ii) tensor fusion, which merges multiple gradient tensors within a fusion buffer to transmit them together to reduce the communication startup overhead. However, we find that existing studies often apply gradient sparsification after tensor fusion (we call sparsification-behind tensor fusion), which leads to a fact that a lot of fused gradient tensors are missed after the sparsification, thus impairing the convergence performance.