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A machine learning approach for accelerating DNA sequence analysis
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Parallel Computing ; DISA ; HPCC)
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Parallel Computing ; DISA ; HPCC)
2018 (English)In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 32, no 3, p. 363-379Article in journal (Refereed) Published
Abstract [en]

The DNA sequence analysis is a data and computationally intensive problem and therefore demands suitable parallel computing resources and algorithms. In this paper, we describe an optimized approach for DNA sequence analysis on a heterogeneous platform that is accelerated with the Intel Xeon Phi. Such platforms commonly comprise one or two general purpose host central processing units (CPUs) and one or more Xeon Phi devices. We present a parallel algorithm that shares the work of DNA sequence analysis between the host CPUs and the Xeon Phi device to reduce the overall analysis time. For automatic worksharing we use a supervised machine learning approach, which predicts the performance of DNA sequence analysis on the host and device and accordingly maps fractions of the DNA sequence to the host and device. We evaluate our approach empirically using real-world DNA segments for human and various animals on a heterogeneous platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P device with 61 cores.

Place, publisher, year, edition, pages
Sage Publications, 2018. Vol. 32, no 3, p. 363-379
Keywords [en]
DNA sequence analysis, machine learning, heterogeneous parallel computing
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-54385DOI: 10.1177/1094342016654214ISI: 000432133100005Scopus ID: 2-s2.0-85046803969OAI: oai:DiVA.org:lnu-54385DiVA, id: diva2:944398
Available from: 2016-06-29 Created: 2016-06-29 Last updated: 2019-08-29Bibliographically 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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