학술논문

Essentials of Parallel Graph Analytics
Document Type
Working Paper
Source
Subject
Computer Science - Distributed, Parallel, and Cluster Computing
Computer Science - Data Structures and Algorithms
Language
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.
Comment: Proceedings of the Workshop on Graphs, Architectures, Programming, and Learning