OPMOS: Ordered Parallel Algorithm for Multi-Objective Shortest-Paths
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915242375643136 |
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| author | Gold, Leo Bienkowski, Adam Sidoti, David Pattipati, Krishna Khan, Omer |
| author_facet | Gold, Leo Bienkowski, Adam Sidoti, David Pattipati, Krishna Khan, Omer |
| contents | The Multi-Objective Shortest-Path (MOS) problem finds a set of Pareto-optimal solutions from a start node to a destination node in a multi-attribute graph. The literature explores multi-objective A*-style algorithmic approaches to solving the NP-hard MOS problem. These approaches use consistent heuristics to compute an exact set of solutions for the goal node. A generalized MOS algorithm maintains a "frontier" of partial paths at each node and performs ordered processing to ensure that Pareto-optimal paths are generated to reach the goal node. The algorithm becomes computationally intractable at a higher number of objectives due to a rapid increase in the search space for non-dominated paths and the significant increase in Pareto-optimal solutions. While prior works have focused on algorithmic methods to reduce the complexity, we tackle this challenge by exploiting parallelism to accelerate the MOS problem. The key insight is that MOS algorithms rely on the ordered execution of partial paths to maintain high work efficiency. The proposed parallel algorithm (OPMOS) unlocks ordered parallelism and efficiently exploits the concurrent execution of multiple paths in MOS. Experimental evaluation using the NVIDIA GH200 Superchip's 72-core Arm-based CPU shows the performance scaling potential of OPMOS on work efficiency and parallelism using a real-world application to ship routing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_16667 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OPMOS: Ordered Parallel Algorithm for Multi-Objective Shortest-Paths Gold, Leo Bienkowski, Adam Sidoti, David Pattipati, Krishna Khan, Omer Distributed, Parallel, and Cluster Computing Artificial Intelligence Hardware Architecture Data Structures and Algorithms Performance The Multi-Objective Shortest-Path (MOS) problem finds a set of Pareto-optimal solutions from a start node to a destination node in a multi-attribute graph. The literature explores multi-objective A*-style algorithmic approaches to solving the NP-hard MOS problem. These approaches use consistent heuristics to compute an exact set of solutions for the goal node. A generalized MOS algorithm maintains a "frontier" of partial paths at each node and performs ordered processing to ensure that Pareto-optimal paths are generated to reach the goal node. The algorithm becomes computationally intractable at a higher number of objectives due to a rapid increase in the search space for non-dominated paths and the significant increase in Pareto-optimal solutions. While prior works have focused on algorithmic methods to reduce the complexity, we tackle this challenge by exploiting parallelism to accelerate the MOS problem. The key insight is that MOS algorithms rely on the ordered execution of partial paths to maintain high work efficiency. The proposed parallel algorithm (OPMOS) unlocks ordered parallelism and efficiently exploits the concurrent execution of multiple paths in MOS. Experimental evaluation using the NVIDIA GH200 Superchip's 72-core Arm-based CPU shows the performance scaling potential of OPMOS on work efficiency and parallelism using a real-world application to ship routing. |
| title | OPMOS: Ordered Parallel Algorithm for Multi-Objective Shortest-Paths |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Hardware Architecture Data Structures and Algorithms Performance |
| url | https://arxiv.org/abs/2411.16667 |