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GPU Computing in Chapel: Application to Tree-Search Algorithms

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Titel: GPU Computing in Chapel: Application to Tree-Search Algorithms
Autoren: Helbecque, Guillaume, Krishnasamy, Ezhilmathi, Melab, Nouredine, Bouvry, Pascal
Weitere Verfasser: Helbecque, Guillaume
Verlagsinformationen: 2024.
Publikationsjahr: 2024
Schlagwörter: Backtracking, Chapel, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], GPU Programming, N-Queens, Tree-Search
Beschreibung: We investigate the design and implementation of a GPU-accelerated tree-search algorithm in Chapel. The latter is motivated by the emerging GPU support of Chapel, which stands as an alternative to traditional low-level programming environments, such as CUDA. The algorithm is based on a general multi-pool approach equipped with a load balancing mechanism. It is experimented on the N-Queens problem and compared to a CUDA baseline implementation using up to 8 GPUs. Both Nvidia and AMD GPU architectures are considered. We demonstrate that the Chapel's high level of abstraction causes a performance loss of only 10% in our experiments, and our algorithm achieves up to 75% of the linear speed-up in best scenarios.
Publikationsart: Conference object
Sprache: English
Zugangs-URL: https://hal.science/hal-04551844v1
Dokumentencode: edsair.dedup.wf.002..80dafcfaf1d70a63971b9a8fee2f97d5
Datenbank: OpenAIRE
Beschreibung
Abstract:We investigate the design and implementation of a GPU-accelerated tree-search algorithm in Chapel. The latter is motivated by the emerging GPU support of Chapel, which stands as an alternative to traditional low-level programming environments, such as CUDA. The algorithm is based on a general multi-pool approach equipped with a load balancing mechanism. It is experimented on the N-Queens problem and compared to a CUDA baseline implementation using up to 8 GPUs. Both Nvidia and AMD GPU architectures are considered. We demonstrate that the Chapel's high level of abstraction causes a performance loss of only 10% in our experiments, and our algorithm achieves up to 75% of the linear speed-up in best scenarios.