Improving sparse data movement performance using multiple paths on the Blue Gene/Q supercomputer

Abstract

In situ analysis has been proposed as a promising solution to glean faster insights and reduce the amount of data to storage. A critical challenge here is that the reduced dataset is typically located on a subset of the nodes and needs to be written out to storage. Data coupling in multiphysics codes also exhibits a sparse data movement pattern wherein data movement occurs among a subset of nodes. We evaluate the performance of data movement for sparse data patterns on the IBM Blue Gene/Q supercomputing system "Mira" and identify performance bottlenecks. We propose a multipath data movement algorithm for sparse data patterns based on an adaptation of a maximum flow algorithm together with breadth-first search that fully exploits all the underlying data paths and I/O nodes to improve data movement. We demonstrate the efficacy of our solutions through a set of microbenchmarks and application benchmarks on Mira scaling up to 131,072 compute cores. The results show that our approach achieves up to 5 x improvement in achievable throughput compared with the default mechanisms. (C) 2015 Elsevier B.V. All rights reserved.