在英特尔众核（MIC）架构上高效计算基序发现。
Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture.
机构信息
College of Computer Science and Electronic Engineering & National Supercomputing Centre in Changsha, Hunan University, Changsha, 410082, China.
School of Computer Science, National University of Defense Technology, Changsha, 410073, China.
出版信息
BMC Bioinformatics. 2018 Aug 13;19(Suppl 9):282. doi: 10.1186/s12859-018-2276-1.
BACKGROUND
Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms.
RESULTS
In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors.
CONCLUSIONS
Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME .
背景
新的序列基序检测在计算生物学中变得越来越重要。然而，高计算成本极大地限制了大多数基序发现算法的效率。
结果
在本文中，我们针对 Intel Many Integrated Core (MIC) 架构加速了 MEME 算法，并基于混合 CPU/MIC 计算框架提出了 MEME 的并行实现，称为 MIC-MEME。我们的方法专注于并行化算法的起始点搜索方法和改进迭代更新策略。在具有两个 Xeon Phi 3120 协处理器的实验平台上进行基准测试时，MIC-MEME 分别在 ZOOPS 模型上实现了 26.6 的平均整体运行时加速和在 OOPS 模型上实现了 30.2 的平均整体运行时加速。
结论
此外，MIC-MEME 已经与最先进的方法进行了比较，并且在数据集大小和 MIC 数量方面具有良好的可扩展性。源代码：https://github.com/hkwkevin28/MIC-MEME。
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