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Combinatorial optimization of DNA sequence analysis on heterogeneous systems

机译：异构系统上DNA序列分析的组合优化

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Analysis of DNA sequences is a data and computational intensive problem, and therefore, it requires suitable parallel computing resources and algorithms. In this paper, we describe our parallel algorithm for DNA sequence analysis that determines how many times a pattern appears in the DNA sequence. The algorithm is engineered for heterogeneous platforms that comprise a host with multi-core processors and one or more many-core devices. For combinatorial optimization, we use the simulated annealing algorithm. The optimization goal is to determine the number of threads, thread affinities, and DNA sequence fractions for host and device, such that the overall execution time of DNA sequence analysis is minimized. We evaluate our approach experimentally using real-world DNA sequences of various organisms on a heterogeneous platform that comprises two Intel Xeon E5 processors and an Intel Xeon Phi 7120P co-processing device. By running only about 5% of possible experiments, our optimization method finds a near-optimal system configuration for DNA sequence analysis that yields with average speedup of 1:6u0002 and 2u0002 compared with the host-only and device-only execution. Copyright.
机译：DNA序列分析是一个数据和计算密集型问题，因此，它需要合适的并行计算资源和算法。在本文中，我们描述了用于DNA序列分析的并行算法，该算法确定模式在DNA序列中出现多少次。该算法是为异构平台设计的，该平台包括具有多核处理器和一个或多个多核设备的主机。对于组合优化，我们使用模拟退火算法。优化目标是确定主机和设备的线程数，线程亲和力和DNA序列分数，以使DNA序列分析的总执行时间最小化。我们在包含两个Intel Xeon E5处理器和一个Intel Xeon Phi 7120P协同处理设备的异构平台上，使用各种生物的真实世界DNA序列对实验方法进行了实验评估。通过仅运行大约5％的可能实验，我们的优化方法为DNA序列分析找到了接近最佳的系统配置，与纯主机和纯设备执行相比，平均配置提速为1：6u0002和2u0002。版权。

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