Essentials of Parallel Graph Analytics

TL;DR

This paper identifies core components of graph analytics systems, proposes a unifying abstraction for various programming models, and demonstrates its effectiveness through a C++ implementation of shortest path algorithms.

Contribution

It introduces a comprehensive abstraction capturing diverse graph analytics models, unifying them under a common framework.

Findings

Unified abstraction for graph analytics models

Successful implementation of shortest path algorithm

Demonstrates versatility of the proposed framework

Abstract

We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulk-synchronous, asynchronous, shared-memory, message-passing, and push vs. pull traversals. Finally, we demonstrate the power of our abstraction with an elegant modern C++ implementation of single-source shortest path and its required components.