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Efficient parallel algorithm for compound comparisons on multi-GPUs

机译:用于多GPU上化合物比较的高效并行算法

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Compound comparison is an important task for computational chemistry. By the comparison reulsts, potential inhibitors can be found and then used for the following experiments. The time complexity of a pairwise compound comparison is O(n), where n is the maximal length of compounds. In general, the compound length is small, and the cost of computation time is short. However, more and more compounds have been synthesized and extracted now, even more than ten of millions. Therefore, it still will be time-consuming when comparing with a large amount of compounds (multiple compound comparisons). In this paper, we propose a parallel algorithm for multiple compound comparisons on multi-GPUs. Four load-balancing strategies were considered in the proposed algorithm in order to accelerate the computation speed among thread blocks on GPUs. The proposed algorithm was implemented by C+OpenMP+CUDA, and achieved more than 50 times speedup by comparing with its CPU version under the experiemtal results.
机译:化合物比较是计算化学的重要任务。通过比较结果,可以发现潜在的抑制剂,然后将其用于以下实验。成对化合物比较的时间复杂度为O(n),其中n是化合物的最大长度。通常,复合长度小,计算时间短。但是,现在已经有越来越多的化合物被合成和提取,甚至超过一千万。因此,与大量化合物进行比较(多次化合物比较)时,仍将非常耗时。在本文中,我们提出了一种并行算法,用于在多GPU上进行多个化合物比较。提出的算法考虑了四种负载均衡策略,以加快GPU上线程块之间的计算速度。该算法是由C + OpenMP + CUDA实现的,在实验结果下,与CPU版本相比,实现了50倍以上的加速。

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