SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Authors: 

Hang Liu, University of Massachusetts Lowell; H. Howie Huang, George Washington University

Abstract: 

With high computation power and memory bandwidth, graphics processing units (GPUs) lend themselves to accelerate data-intensive analytics, especially when such applications fit the single instruction multiple data (SIMD) model. However, graph algorithms such as breadth-first search and k-core, often fail to take full advantage of GPUs, due to irregularity in memory access and control flow. To address this challenge, we have developed SIMD-X, for programming and processing of single instruction multiple, complex, data on GPUs. Specifically, the new Active-Compute-Combine (ACC) model not only provides ease of programming to programmers, but more importantly creates opportunities for system-level optimizations. To this end, SIMD-X utilizes just-in-time task management which filters out inactive vertices at runtime and intelligently maps various tasks to different amount of GPU cores in pursuit of workload balancing. In addition, SIMD-X leverages push-pull based kernel fusion that, with the help of a new deadlock-free global barrier, reduces a large number of computation kernels to very few. Using SIMD-X, a user can program a graph algorithm in tens of lines of code, while achieving 3x, 6x, 24x, 3x speedup over Gunrock, Galois, CuSha, and Ligra, respectively.

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BibTeX
@inproceedings {234942,
author = {Hang Liu and H. Howie Huang},
title = {SIMD-X: Programming and Processing of Graph Algorithms on GPUs},
booktitle = {2019 {USENIX} Annual Technical Conference ({USENIX} {ATC} 19)},
year = {2019},
isbn = {978-1-939133-03-8},
address = {Renton, WA},
pages = {411--428},
url = {https://www.usenix.org/conference/atc19/presentation/liu-hang},
publisher = {{USENIX} Association},
}