Tigr

Author:

Nodehi Sabet Amir Hossein1,Qiu Junqiao1,Zhao Zhijia1

Affiliation:

1. University of California, Riverside, Riverside, CA, USA

Abstract

Graph analytics delivers deep knowledge by processing large volumes of highly connected data. In real-world graphs, the degree distribution tends to follow the power law -- a small portion of nodes own a large number of neighbors. The high irregularity of degree distribution acts as a major barrier to their efficient processing on GPU architectures, which are primarily designed for accelerating computations on regular data with SIMD executions. Existing solutions to the inefficiency of GPU-based graph analytics either modify the graph programming abstraction or rely on changes to the low-level thread execution models. The former requires more programming efforts for designing and maintaining graph analytics; while the latter couples with the underlying architectures, making it difficult to adapt as architectures quickly evolve. Unlike prior efforts, this work proposes to address the above fundamental problem at its origin -- the irregular graph data itself. It raises a critical question in irregular graph processing: Is it possible to transform irregular graphs into more regular ones such that the graphs can be processed more efficiently on GPU-like architectures, yet still producing the same results? Inspired by the question, this work introduces Tigr -- a graph transformation framework that can effectively reduce the irregularity of real-world graphs with correctness guarantees for a wide range of graph analytics. To make the transformations practical, Tigr features a lightweight virtual transformation scheme, which can substantially reduce the costs of graph transformations, while preserving the benefits of reduced irregularity. Evaluation on Tigr-based GPU graph processing shows significant and consistent speedup over the state-of-the-art GPU graph processing frameworks for a spectrum of irregular graphs.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Cited by 20 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PckGNN: Optimizing Aggregation Operators with Packing Strategies in Graph Neural Networks;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

2. Core Graph: Exploiting Edge Centrality to Speedup the Evaluation of Iterative Graph Queries;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

3. GraFlex: Flexible Graph Processing on FPGAs through Customized Scalable Interconnection Network;Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays;2024-04

4. MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02

5. Graph Processing Scheme Using GPU With Value-Driven Differential Scheduling;IEEE Access;2024

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