用GPU求解非编码DNA序列的最长重叠区问题
Solving The Longest Overlap Region Problem for Noncoding DNA Sequences with GPU
摘要：
GPU的早期硬件限制（缺乏同步基元和有限的内存缓存机制）可能会使基于GPU的计算效率低下。生物技术的发展为生物信息学和生物工程学的发展带来了新的机遇。本文介绍了一种在计算统一设备架构（CUDA）平台Intel（R）Core（TM）i3- 3110 m四核上求解非编码DNA序列最长重叠区的方法。与标准CPU实现相比，CUDA性能证明了非编码DNA最长重叠区识别方法是一种高性能生物信息学应用的有效途径。研究表明，GPU性能的效率是CPU串行实现的20倍以上的加速比。我们相信，我们的方法提供了一个具有成本效益的解决方案，生物信息学社区解决最长重叠区域识别问题和其他相关领域。
英文摘要：
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.
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