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Combinatorial optimization of DNA sequence analysis on heterogeneous systems
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (Parallel Computing ; DISA ; HPCC)
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (Parallel Computing ; DISA ; HPCC)
2017 (English)In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 29, no 7, article id e4037Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2017. Vol. 29, no 7, article id e4037
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-58995DOI: 10.1002/cpe.4037ISI: 000398712500007Scopus ID: 2-s2.0-85006508024OAI: oai:DiVA.org:lnu-58995DiVA, id: diva2:1056047
Conference
The 18th IEEE international conference on computational science and engineering (CSE2015)
Available from: 2016-12-13 Created: 2016-12-13 Last updated: 2019-09-06Bibliographically approved
In thesis
1. Programming and Optimization of Big-Data Applications on Heterogeneous Computing Systems
Open this publication in new window or tab >>Programming and Optimization of Big-Data Applications on Heterogeneous Computing Systems
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The next-generation sequencing instruments enable biological researchers to generate voluminous amounts of data. In the near future, it is projected that genomics will be the largest source of big-data. A major challenge of big data is the efficient analysis of very large data-sets. Modern heterogeneous parallel computing systems, which comprise multiple CPUs, GPUs, and Intel Xeon Phis, can cope with the requirements of big-data analysis applications. However, utilizing these resources to their highest possible extent demands advanced knowledge of various hardware architectures and programming frameworks. Furthermore, optimized software execution on such systems demands consideration of many compile-time and run-time system parameters.

In this thesis, we study and develop parallel pattern matching algorithms for heterogeneous computing systems. We apply our pattern matching algorithm for DNA sequence analysis. Experimental evaluation results show that our parallel algorithm can achieve more than 50x speedup when executed on host CPUs and more than 30x when executed on Intel Xeon Phi compared to the sequential version executed on the CPU.

Thereafter, we combine machine learning and search-based meta-heuristics to determine near-optimal parameter configurations of parallel matching algorithms for efficient execution on heterogeneous computing systems. We use our approach to distribute the workload of the DNA sequence analysis application across the available host CPUs and accelerating devices and to determine the system configuration parameters of a heterogeneous system that comprise Intel Xeon CPUs and Xeon Phi accelerator. Experimental results show that the execution that uses the resources of both host CPUs and accelerating device outperforms the host-only and the device-only executions.

Furthermore, we propose programming abstractions, a source-to-source compiler, and a run-time system for heterogeneous stream computing. Given a source code annotated with compiler directives, the source-to-source compiler can generate device-specific code. The run-time system can automatically distribute the workload across the available host CPUs and accelerating devices. Experimental results show that our solution significantly reduces the programming effort and the generated code delivers better performance than the CPUs-only or GPUs-only executions.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2018
Series
Linnaeus University Dissertations ; 335/2018
Keywords
Big Data, Heterogeneous Parallel Computing, Software Optimization, Source-to-source Compilation
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science; Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-79192 (URN)978-91-88898-14-2 (ISBN)978-91-88898-15-9 (ISBN)
Public defence
2018-12-20, D1136, Hus D, Växjö, 15:00 (English)
Opponent
Supervisors
Available from: 2018-12-17 Created: 2018-12-13 Last updated: 2018-12-17Bibliographically approved

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Memeti, SuejbPllana, Sabri

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