Essentials of Parallel Graph Analytics
arXiv:2212.08200 · doi:10.1109/IPDPSW55747.2022.00061
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.
Proceedings of the Workshop on Graphs, Architectures, Programming, and Learning