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TurboBFS: GPU Based Breadth-First Search (BFS) Algorithms in the Language of Linear Algebra.

Oswaldo Artiles1, Fahad Saeed1

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

IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum : [Proceedings]. IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum
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Summary
This summary is machine-generated.

This study introduces TurboBFS, a novel GPU-accelerated Breadth-First Search (BFS) algorithm for analyzing large graphs. TurboBFS offers significant speedups for complex network analysis, outperforming existing methods.

Keywords:
BFSCUDAGPUgraph parallel algorithmslinear algebra

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Area of Science:

  • Computer Science
  • Graph Theory
  • High-Performance Computing

Background:

  • Large-scale graphs are crucial for modeling complex systems like brain networks and social interactions.
  • Manual analysis of these vast graphs is infeasible.
  • The Breadth-First Search (BFS) algorithm is fundamental for graph analysis but computationally expensive for large datasets.

Purpose of the Study:

  • To develop an efficient GPU-based implementation of BFS for large-scale graph analysis.
  • To address the computational challenges and memory limitations of using GPUs for graph processing.
  • To present a linear-algebraic approach for BFS acceleration.

Main Methods:

  • Developed TurboBFS, a GPU-based linear-algebraic formulation of BFS.
  • Implemented and tested TurboBFS on unweighted, undirected, and directed sparse graphs of arbitrary structure.
  • Evaluated performance against state-of-the-art algorithms on various libraries.

Main Results:

  • TurboBFS demonstrates excellent scalability for large graph analysis.
  • Achieved up to 40 Giga Traversed Edges Per Second (GTEPs).
  • Outperformed existing algorithms by 15.7x (SuiteSparse:GraphBLAS), 5.8x (GraphBLAST), and 1.8x (gunrock) on average.

Conclusions:

  • TurboBFS provides a highly efficient and scalable solution for BFS on GPUs.
  • The linear-algebraic approach effectively overcomes GPU memory and irregular graph structure challenges.
  • This method significantly advances the analysis of massive graphs in various scientific domains.