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TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear

Oswaldo Artiles1, Fahad Saeed1

  • 1School of Computing and Information Sciences, Florida, International University, Miami, Florida, USA.

Proceedings of the ... ICPP Workshops On. International Conference on Parallel Processing Workshops
|April 20, 2022
PubMed
Summary
This summary is machine-generated.

We introduce TurboBC, a novel GPU-based algorithm for calculating betweenness centrality (BC) in large graphs. TurboBC offers significant speedups and memory efficiency, outperforming existing methods on complex network analysis.

Keywords:
CUDAGPUcentralitygraph parallel algorithmslinear algebra

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Area of Science:

  • Graph theory
  • Network analysis
  • High-performance computing

Background:

  • Betweenness centrality (BC) is crucial for analyzing influence in large networks like social or biological systems.
  • Implementing BC on Graphics Processing Units (GPUs) faces challenges due to memory limitations and irregular data access patterns, impacting performance.
  • Existing GPU and CPU libraries (e.g., Gunrock, Ligra) struggle with scalability and memory constraints for massive graphs.

Purpose of the Study:

  • To present the first linear-algebraic formulation and implementation of betweenness centrality optimized for GPUs.
  • To develop memory-efficient BC algorithms, named TurboBC, that achieve high performance and scalability on diverse graph structures.
  • To address the computational challenges of applying BC to modern, large-scale graphs.

Main Methods:

  • Developed TurboBC, a GPU-based, linear-algebraic approach for calculating betweenness centrality.
  • Implemented a set of memory-efficient algorithms designed for sparse, unweighted, and directed/undirected graphs.
  • Conducted experiments comparing TurboBC against sequential BC algorithms and state-of-the-art libraries (Gunrock, Ligra).

Main Results:

  • TurboBC algorithms achieved over 18 GTEPs and an average speedup of 31.9x compared to sequential BC.
  • TurboBC demonstrated an average speedup of 1.7x over Gunrock (GPU) and 2.2x over Ligra (CPU).
  • TurboBC successfully computed BC for large graphs that exceeded the memory capacity of the Gunrock library.

Conclusions:

  • TurboBC provides a highly performant and scalable solution for betweenness centrality computation on GPUs.
  • The memory efficiency of TurboBC enables analysis of larger graphs than previously feasible with existing GPU algorithms.
  • This work advances the practical application of centrality metrics in complex network analysis on modern hardware.