多异构gpu的细粒度并行兆基序列比较

E. Sandes, Guillermo Miranda, A. Melo, X. Martorell, E. Ayguadé
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引用次数: 7

摘要

本文提出并评估了一种在异构多gpu平台上执行兆基DNA序列精确Smith-Waterman (SW)算法的并行策略。在我们的策略中,单个巨大的SW矩阵的计算分布在多个gpu上,这些gpu使用隐藏通信开销的循环缓冲机制将边界元素传递给邻居。我们使用2种不同的GPU环境比较了4对人类-黑猩猩同源染色体,在3种异构GPU环境下获得了高达140.36 GCUPS(每秒处理十亿细胞)的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fine-grain parallel megabase sequence comparison with multiple heterogeneous GPUs
This paper proposes and evaluates a parallel strategy to execute the exact Smith-Waterman (SW) algorithm for megabase DNA sequences in heterogeneous multi-GPU platforms. In our strategy, the computation of a single huge SW matrix is spread over multiple GPUs, which communicate border elements to the neighbour, using a circular buffer mechanism that hides the communication overhead. We compared 4 pairs of human-chimpanzee homologous chromosomes using 2 different GPU environments, obtaining a performance of up to 140.36 GCUPS (Billion of cells processed per second) with 3 heterogeneous GPUS.
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