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Solving the longest overlap region problem for noncoding DNA sequences with GPU

机译:使用GPU解决非编码DNA序列的最长重叠区域问题

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Early hardware limitations of GPU (lack of synchronization primitives and limited memory caching mechanisms) can make GPU-based computation inefficient. Now Bio-technologies bring more chances to Bioinformatics and Biological Engineering. Our paper introduces a way to solve the longest overlap region of non-coding DNA sequences on using the Compute Unified Device Architecture (CUDA) platform Intel(R) Core(TM) i3-3110m quad-core. Compared to standard CPU implementation, CUDA performance proves the method of the longest overlap region recognition of noncoding DNA is an efficient approach to high-performance bioinformatics applications. Studies show the fact that efficiency of GPU performance is more than 20 times speedup than that of CPU serial implementation. We believe our method gives a cost-efficient solution to the bioinformatics community for solving longest overlap region recognition problem and other related fields.
机译:GPU的早期硬件限制(缺少同步原语和有限的内存缓存机制)会使基于GPU的计算效率低下。现在,生物技术为生物信息学和生物工程带来了更多机会。本文介绍了一种使用Compute Unified Device Architecture(CUDA)平台Intel(R)Core(TM)i3-3110m四核解决非编码DNA序列最长重叠区域的方法。与标准CPU实施相比,CUDA性能证明了最长的非编码DNA重叠区识别方法是高性能生物信息学应用的有效方法。研究表明,GPU性能的效率是CPU串行实现效率的20倍以上。我们相信我们的方法为生物信息学界提供了一种经济有效的解决方案,以解决最长的重叠区域识别问题和其他相关领域。

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