Adaptive Space-efficient Collectives for Dynamic and Unstructured Sparsity on GPU Platforms

Abstract

High-performance collective communication primitives are necessary for a variety of high performance computing (HPC) and machine learning (ML) workloads. State-of-the-art collective communication libraries such as NCCL optimize exclusively for dense data. However, when sending sparse data, we can reduce communication volume by not sending zeros. Unfortunately, explicitly handling sparsity introduces challenges such as format conversion overheads and densification during collectives that involve reductions. In this paper, we introduce sparsity-exploiting algorithms for three collectives that address these challenges: all-gather, reduce-scatter, and all-reduce. Our collective implementations are backed by a new bitvector-based format, Pici, designed for low overhead and fast (de)compression at moderate sparsities. Further, our algorithms adapt to the level of sparsity in data, modifying its representation during the course of the collective. At 99% input sparsity, our collectives achieve up to 5.25x, 2.5x, and 2.66x speedups over NCCL for all-gather, reduce-scatter, and all-reduce, respectively.