GC3: An Optimizing Compiler for GPU Collective Communication

Abstract

Machine learning models made up of millions or billions of parameters are trained and served on large multi-GPU systems. As models grow in size and execute on more GPUs, the collective communications used in these applications become a bottleneck. Custom collective algorithms optimized for both particular network topologies and application specific communication patterns can alleviate this bottleneck and help these applications scale. However, correctly and efficiently implementing custom algorithms is challenging. This paper introduces GC3, a system for programmable GPU communication. GC3 provides a domain specific language for writing collective communication algorithms and an optimizing compiler for lowering them to an executable form, which can be executed efficiently and flexibly in an interpreter based runtime. We used GC3 to write novel collective algorithms for AllReduce and AllToAll that are up to $1.9\times$ and $1.3\times$ faster than hand-optimized implementations, respectively.