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Communication Dans Un Congrès Année : 2024

GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP

Résumé

In the context of exascale programming, the PGAS-based Chapel is among the rare languages targeting the holistic handling of high-performance computing issues including the productivity-aware harnessing of Nvidia and AMD GPUs. In this paper, we propose a pioneering proof-of-concept dealing with this latter issue in the context of tree-based exact optimization. Actually, we revisit the design and implementation of a generic multi-pool GPU-accelerated tree-search algorithm using Chapel. This algorithm is instantiated on the backtracking method and experimented on the N-Queens problem. For performance evaluation, the Chapel-based approach is compared to Nvidia CUDA and AMD HIP low-level counterparts. The reported results show that in a single-GPU setting, the high GPU abstraction of Chapel results in a loss of only 8% (resp. 16%) compared to CUDA (resp. HIP). In a multi-GPU setting, up to 80% (resp. 71%) of the baseline speed-up is achieved for coarse-grained problem instances on Nvidia (resp. AMD) GPUs.
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hal-04551856 , version 1 (11-06-2024)

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  • HAL Id : hal-04551856 , version 1

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Guillaume Helbecque, Ezhilmathi Krishnasamy, Nouredine Melab, Pascal Bouvry. GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP. 14th IEEE Workshop Parallel / Distributed Combinatorics and Optimization (PDCO 2024), May 2024, San Francisco, United States. ⟨hal-04551856⟩
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