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International Journal of Parallel Programming
Erschienen in: International Journal of Parallel Programming 4/2013

01.08.2013

GPU-Friendly Parallel Genome Matching with Tiled Access and Reduced State Transition Table

verfasst von: Yunho Oh, Doohwan Oh, Won W. Ro

Erschienen in: International Journal of Parallel Programming | Ausgabe 4/2013

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Abstract

In this paper, we propose a new parallel genome matching algorithm using graphics processing units (GPUs). Our proposed approach is based on the Aho–Corasick algorithm and it was developed based on a consideration of the architectural features of existing GPUs with a hundred or more cores. Thus, we provide an appropriate task partitioning method that runs on multiple threads and we fully utilize the cache memory and the shared memory structures available in GPUs. Especially, we propose a tiled access method for rapid data transfer from the global memory to the shared memory. We also provide new models for cache-friendly state transition table to improve performance of pattern matching operations on GPUs. The maximum throughput we achieved in various experiments was 15.3 Gbps. Moreover, we showed that our proposed design outperformed an earlier approach with a 15.4 % performance improvement.
Vorheriger Artikel Analytical Performance Models for MapReduce Workloads
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Metadaten
Titel
GPU-Friendly Parallel Genome Matching with Tiled Access and Reduced State Transition Table
verfasst von
Yunho Oh
Doohwan Oh
Won W. Ro
Publikationsdatum
01.08.2013
Verlag
Springer US
Erschienen in
International Journal of Parallel Programming / Ausgabe 4/2013
Print ISSN: 0885-7458
Elektronische ISSN: 1573-7640
DOI
https://doi.org/10.1007/s10766-012-0234-5