Collective communication algorithms are an important component of distributed computation. Indeed, in the case of deep-learning, collective communication is the Amdahl's bottleneck of data-parallel training. This paper introduces SCCL (for Synthesized Collective Communication Library), a systematic approach to synthesize collective communication algorithms that are explicitly tailored to a particular hardware topology. SCCL synthesizes algorithms along the Pareto-frontier spanning from latency-optimal to bandwidth-optimal implementations of a collective. The paper demonstrates how to encode SCCL's synthesis as a quantifier-free SMT formula which can be discharged to a theorem prover. We further demonstrate how to scale our synthesis by exploiting symmetries in topologies and collectives. We synthesize and introduce novel latency and bandwidth optimal algorithms not seen in the literature on two popular hardware topologies. We also show how SCCL efficiently lowers algorithms to implementations on two hardware architectures (NVIDIA and AMD) and demonstrate competitive performance with hand optimized collective communication libraries.
@article{arxiv.2008.08708,
title = {Synthesizing Optimal Collective Algorithms},
author = {Zixian Cai and Zhengyang Liu and Saeed Maleki and Madan Musuvathi and Todd Mytkowicz and Jacob Nelson and Olli Saarikivi},
journal= {arXiv preprint arXiv:2008.08708},
year = {2021}
}
Comments
Both Zixian Cai and Zhengyang Liu contributed equally to the paper. The work was done during internships at Microsoft Research. To appear at PPoPP 2021