Bloom Filter Performance on Graphics Engines

Lin Ma, R. Chamberlain, J. Buhler, M. Franklin
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引用次数: 29

Abstract

Bloom filters are a probabilistic technique for large-scale set membership tests. They exhibit no false negative test results but are susceptible to false positive results. They are well-suited to both large sets and large numbers of membership tests. We implement the Bloom filters present in an accelerated version of BLAST, a genome biosequence alignment application, on NVIDIA GPUs and develop an analytic performance model that helps potential users of Bloom filters to quantify the inherent tradeoffs between throughput and false positive rates.
布隆过滤器性能的图形引擎
布隆过滤器是一种用于大规模集合隶属度测试的概率技术。他们没有假阴性测试结果,但容易出现假阳性结果。它们非常适合于大集合和大量的成员测试。我们在NVIDIA gpu上实现了加速版BLAST(基因组生物序列比对应用程序)中的Bloom过滤器,并开发了一个分析性能模型,帮助Bloom过滤器的潜在用户量化吞吐量和假阳性率之间的固有权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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