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PaREM: a Novel Approach for Parallel Regular Expression Matching
Linnaeus University, Faculty of Technology, Department of Computer Science.
Linnaeus University, Faculty of Technology, Department of Computer Science.
2014 (English)In: 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE), IEEE Press, 2014, p. 690-697Conference paper, Published paper (Refereed)
Abstract [en]

Regular expression matching is essential for many applications, such as finding patterns in text, exploring substrings in large DNA sequences, or lexical analysis. However, sequential regular expression matching may be time-prohibitive for large problem sizes. In this paper, we describe a novel algorithm for parallel regular expression matching via deterministic finite automata. Furthermore, we present our tool PaREM that accepts regular expressions and finite automata as input and automatically generates the corresponding code for our algorithm that is amenable for parallel execution on shared-memory systems. We evaluate our parallel algorithm empirically by comparing it with a commonly used algorithm for sequential regular expression matching. Experiments on a dual-socket shared-memory system with 24 physical cores show speed-ups of up to 21× for 48 threads.

Place, publisher, year, edition, pages
IEEE Press, 2014. p. 690-697
National Category
Engineering and Technology Information Systems
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-41014DOI: 10.1109/CSE.2014.146ISI: 000380512100111Scopus ID: 2-s2.0-84925250594ISBN: 978-1-4799-7980-6 (print)OAI: oai:DiVA.org:lnu-41014DiVA, id: diva2:796452
Conference
17th International Conference on Computational Science and Engineering (CSE), 19-21 Dec. 2014, Chengdu
Available from: 2015-03-19 Created: 2015-03-19 Last updated: 2018-12-13Bibliographically 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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Publisher's full textScopushttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7023656

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Memeti, SuejbPllana, Sabri

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