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  1. Proceedings of the 8th International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM'17)
  2. High Performance Detection of Strongly Connected Components in Sparse Graphs on GPUs
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Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs
TaskInsight: Understanding Task Schedules Effects on Memory and Performance
A high-performance portable abstract interface for explicit SIMD vectorization
PETRAS: Performance, Energy and Thermal Aware Resource Allocation and Scheduling for Heterogeneous Systems
Reduction to Tridiagonal Form for Symmetric Eigenproblems on Asymmetric Multicore Processors
High Performance Detection of Strongly Connected Components in Sparse Graphs on GPUs
Towards Composable GPU Programming: Programming GPUs with Eager Actions and Lazy Views
Assessing One-to-One Parallelism Levels Mapping for OpenMP Offloading to GPUs
A Framework for Developing Parallel Applications with high level Tasks on Heterogeneous Platforms

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High Performance Detection of Strongly Connected Components in Sparse Graphs on GPUs

Content Provider ACM Digital Library
Author Shen, Jie Yang, Canqun Chen, Xuhao Tang, Tao Li, Pingfan Fang, Jianbin
Abstract Detecting strongly connected components (SCC) has been broadly used in many real-world applications. To speedup SCC detection for large-scale graphs, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations are able to get speedup on synthetic graph instances, but show limited performance when applied to large-scale real-world datasets. In this paper, we present a parallel SCC detection implementation on GPUs that achieves high performance on both synthetic and real-world graphs. We use a hybrid method that divides the algorithm into two phases. Our method is able to dynamically change parallelism strategies to maximize performance for each algorithm phase. We then orchestrates the graph traversal kernel with customized strategy for each phase, and employ algorithm extensions to handle the serialization problem caused by irregular graph properties. Our design is carefully implemented to take advantage of the GPU hardware. Evaluation with diverse graphs on the NVIDIA K20c GPU shows that our proposed implementation achieves an average speedup of 5.0x over the serial Tarjan's algorithm. It also outperforms the existing OpenMP implementation with a speedup of 1.4x.
Starting Page 48
Ending Page 57
Page Count 10
File Format PDF
ISBN 9781450348836
DOI 10.1145/3026937.3026941
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-02-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Real-world graphs Strongly connected components Gpu
Content Type Text
Resource Type Article
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