Performance Analysis of Floyd-Warshall Algorithm: Sequential and Parallel Execution Using Intel oneAPI

The Floyd-Warshall algorithm is a widely utilized graph-based technique designed to address the all-pairs shortest path problem. However, its cubic time complexity $O\left(n^{3}\right)$ creates performance bottlenecks when applied to large graphs, especially in sequential execution. This paper explores the use of Intel oneAPI’s Data Parallel C++ (DPC++) programming model to optimize the performance of the Floyd-Warshall algorithm through parallel processing. A comparative performance analysis between sequential and parallel implementations on an Intel Xeon(R) CPU highlights up to a $43 \%$ improvement in execution time for graphs with 800 nodes. The results underscore the potential of parallel computing in significantly reducing computational time for large-scale graph algorithms.

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Published
2024-11-06
DOI
https://doi.org/10.1109/iceca63461.2024.10800787
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
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article

Performance Analysis of Floyd-Warshall Algorithm: Sequential and Parallel Execution Using Intel oneAPI

Sanjay Kumar, S. R. Srilakshmi, S. Karthik, P Dharun Viginesh
Parallel Computing and Optimization Techniques
article

Performance Analysis of Floyd-Warshall Algorithm: Sequential and Parallel Execution Using Intel oneAPI

Sanjay Kumar, S. R. Srilakshmi, S. Karthik, P Dharun Viginesh
article en

Abstract

The Floyd-Warshall algorithm is a widely utilized graph-based technique designed to address the all-pairs shortest path problem. However, its cubic time complexity $O\left(n^{3}\right)$ creates performance bottlenecks when applied to large graphs, especially in sequential execution. This paper explores the use of Intel oneAPI’s Data Parallel C++ (DPC++) programming model to optimize the performance of the Floyd-Warshall algorithm through parallel processing. A comparative performance analysis between sequential and parallel implementations on an Intel Xeon(R) CPU highlights up to a $43 \%$ improvement in execution time for graphs with 800 nodes. The results underscore the potential of parallel computing in significantly reducing computational time for large-scale graph algorithms.

SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 75%
Parallel Computing and Optimization Techniques
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