CHALLENGES IN PARALLEL GRAPH PROCESSING

TLDR

Graph algorithms are increasingly important for scientific computing, data mining, and other domains, and as these problems grow in scale, parallel computing resources are required to meet computational and memory demands, yet existing mainstream parallel scientific application tools are not necessarily effective for large‑scale graph problems. The study aims to map the inter‑relationships among graph problems, software, and parallel hardware, highlight inherent challenges in large‑scale graph processing, and propose a research agenda for scalable high‑performance graph software. The authors analyze the inter‑relationships among graph problems, software, and parallel hardware in the current state of the art to identify inherent challenges in large‑scale graph processing.

Abstract

Graph algorithms are becoming increasingly important for solving many problems in scientific computing, data mining and other domains. As these problems grow in scale, parallel computing resources are required to meet their computational and memory requirements. Unfortunately, the algorithms, software, and hardware that have worked well for developing mainstream parallel scientific applications are not necessarily effective for large-scale graph problems. In this paper we present the inter-relationships between graph problems, software, and parallel hardware in the current state of the art and discuss how those issues present inherent challenges in solving large-scale graph problems. The range of these challenges suggests a research agenda for the development of scalable high-performance software for graph problems.