Regularizing irregularly sparse point-to-point communications
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019
Association for Computing Machinery
1 - 14
Item Usage Stats
MetadataShow full item record
This work tackles the communication challenges posed by the latency-bound applications with irregular communication patterns, i.e., applications with high average and/or maximum message counts. We propose a novel algorithm for reorganizing a given set of irregular point-to-point messages with the objective of reducing total latency cost at the expense of increased volume. We organize processes into a virtual process topology inspired by the k-ary n-cube networks and regularize irregular messages by imposing regular communication pattern(s) onto them. Exploiting this process topology, we propose a flexible store-and-forward algorithm to control the trade-off between latency and volume. Our approach is able to reduce the communication time of sparse-matrix multiplication with latency-bound instances drastically: up to 22.6× for 16K processes on a 3D Torus network and up to 7.2× for 4K processes on a Dragonfly network, with its performance getting better with increasing number of processes.