A More Scalable Sparse Dynamic Data Exchange

TL;DR

This paper introduces a new MPI extension API and locality-aware algorithms for sparse dynamic data exchange, significantly improving performance and scalability in parallel applications with irregular communication patterns.

Contribution

It presents a novel MPI extension API and locality-aware algorithms that enhance sparse dynamic data exchange efficiency in parallel computing.

Findings

Speedups up to 20x with locality-aware algorithms

Improved scalability in irregular communication patterns

Effective utilization of modern parallel architectures

Abstract

Parallel architectures are continually increasing in performance and scale, while underlying algorithmic infrastructure often fail to take full advantage of available compute power. Within the context of MPI, irregular communication patterns create bottlenecks in parallel applications. One common bottleneck is the sparse dynamic data exchange, often required when forming communication patterns within applications. There are a large variety of approaches for these dynamic exchanges, with optimizations implemented directly in parallel applications. This paper proposes a novel API within an MPI extension library, allowing for applications to utilize the variety of provided optimizations for sparse dynamic data exchange methods. Further, the paper presents novel locality-aware sparse dynamic data exchange algorithms. Finally, performance results show significant speedups up to 20x with the novel locality-aware algorithms.