优化用于DNA序列比对的数据密集型通用图形处理器计算
Optimizing Data Intensive GPGPU Computations for DNA Sequence Alignment.
作者信息
Trapnell Cole, Schatz Michael C
机构信息
Center for Bioinformatics and Computational Biology, University of Maryland.
出版信息
Parallel Comput. 2009 Aug 1;35(8):429-440. doi: 10.1016/j.parco.2009.05.002.
Abstract
MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of the alignment code and nearly 4× faster in total computation time than MUMmerGPU 1.0. We exhaustively examined 128 GPU data layout configurations to improve register footprint and running time and conclude higher occupancy has greater impact than reduced latency. MUMmerGPU is available open-source at http://mummergpu.sourceforge.net.
摘要
MUMmerGPU利用高度并行的商用图形处理单元（GPU）来加速数据密集型计算，即将下一代DNA序列数据与参考序列进行比对，以用于疾病基因分型和个人基因组学等各种应用。MUMmerGPU 2.0具有一个新的无栈深度优先搜索打印内核，比对代码的速度比串行CPU版本快13倍，总计算时间比MUMmerGPU 1.0快近4倍。我们详尽地研究了128种GPU数据布局配置，以改善寄存器占用情况和运行时间，并得出结论：更高的占用率比降低延迟的影响更大。MUMmerGPU可在http://mummergpu.sourceforge.net上获取开源版本。
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