High-Performance Parallelization of Dijkstra's Algorithm Using MPI and CUDA
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915228919267328 |
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| author | Song, Boyang |
| author_facet | Song, Boyang |
| contents | This paper investigates the parallelization of Dijkstra's algorithm for computing the shortest paths in large-scale graphs using MPI and CUDA. The primary hypothesis is that by leveraging parallel computing, the computation time can be significantly reduced compared to a serial implementation. To validate this, I implemented three versions of the algorithm: a serial version, an MPI-based parallel version, and a CUDA-based parallel version. Experimental results demonstrate that the MPI implementation achieves over 5x speedup, while the CUDA implementation attains more than 10x improvement relative to the serial benchmark. However, the study also reveals inherent challenges in parallelizing Dijkstra's algorithm, including its sequential logic and significant synchronization overhead. Furthermore, the use of an adjacency matrix as the data structure is examined, highlighting its impact on memory consumption and performance in both dense and sparse graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Performance Parallelization of Dijkstra's Algorithm Using MPI and CUDA Song, Boyang Distributed, Parallel, and Cluster Computing This paper investigates the parallelization of Dijkstra's algorithm for computing the shortest paths in large-scale graphs using MPI and CUDA. The primary hypothesis is that by leveraging parallel computing, the computation time can be significantly reduced compared to a serial implementation. To validate this, I implemented three versions of the algorithm: a serial version, an MPI-based parallel version, and a CUDA-based parallel version. Experimental results demonstrate that the MPI implementation achieves over 5x speedup, while the CUDA implementation attains more than 10x improvement relative to the serial benchmark. However, the study also reveals inherent challenges in parallelizing Dijkstra's algorithm, including its sequential logic and significant synchronization overhead. Furthermore, the use of an adjacency matrix as the data structure is examined, highlighting its impact on memory consumption and performance in both dense and sparse graphs. |
| title | High-Performance Parallelization of Dijkstra's Algorithm Using MPI and CUDA |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.03667 |