Combinatorial optimization of DNA sequence analysis on heterogeneous systems

Abstract

Summary 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 1.6 × and 2 × compared with the host‐only and device‐only execution. Copyright © 2016 John Wiley & Sons, Ltd.

Customizing Pareto Simulated Annealing for Multi-Objective Optimization of Control Cabinet Layout

“…Memeti et al [10], [23] use Simulated Annealing for optimization of DNA sequence analysis on heterogeneous computing systems that comprise a host with multi-core processors and one or more many-core devices. The optimization procedure aims at determining 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.…”

“…The Simulated Annealing is widely used for combinatorial optimization of complex systems, such as, decentralized scheduling in Grid computing environments [9], optimization of DNA sequence analysis on heterogeneous computing systems [10], gate assignment problem in the context of an airport [11], furniture arrangement [12], or hybrid vehicle routing [13].…”

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

“…With regards to static scheduling, the attention of recent research that use machine learning and meta-heuristics is in the following optimization objectives: mapping program parallelism to multi-core architectures [98], mapping applications to the most appropriate processing device [40,71], mapping threads to specific cores [15], and determining workload distribution on heterogeneous parallel computing systems [62][63][64][65].…”

“…While such approaches consider application specific features, researchers have demonstrated positive improvement results in approaches that do not require code analysis. Instead, they rely on features such as the available system resources and program input size during the optimization process (that is determining the workload distribution of data-parallel applications) [62][63][64].…”

Foreword to the special issue of the 18th IEEE international conference on computational science and engineering (CSE2015)

“…The article ‘Combinatorial Optimization of DNA Sequence Analysis on Heterogeneous Systems’ presents an experimental study of counting the occurrences of query patterns in DNA sequences in parallel. DNA sequence analysis has many important practical applications, and is both data and computation intensive.…”