Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2021

Abstract

GPU’s massive computing power offers unprecedented opportunities to enable large graph analysis. Existing studies proposed various preprocessing approaches that convert the input graphs into dedicated structures for GPU-based optimizations. However, these dedicated approaches incur significant preprocessing costs as well as weak programmability to build general graph applications. In this paper, we introduce SAGE, a self-adaptive graph traversal on GPUs, which is free from preprocessing and operates on ubiquitous graph representations directly. We propose Tiled Partitioning and Resident Tile Stealing to fully exploit the computing power of GPUs in a runtime and self-adaptive manner. We also propose Sampling-based Reordering to further optimize the memory efficiency of SAGE through a lightweight and effective node reordering technique on the fly. Extensive experiments demonstrate that SAGE can achieve superior graph traversal performance over existing approaches under different architectural scenarios, i.e., singleGPU, out-of-core, and multi-GPU.

Keywords

Graph Processing, GPGPU, Parallel Task Scheduling

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2021 International Conference on Management of Data, Virtual Conference, 2021 June 20-25

First Page

1558

Last Page

1570

Identifier

10.1145/3448016.3457279

Publisher

ACM

City or Country

USA

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