Special Issue "Algorithms for Multi Core Parallel Computation"

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A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 September 2013)

Special Issue Editor

Guest Editor
Prof. Dr. Ken Hawick

Institute of Natural & Mathematical Sciences, Massey University - Albany, Auckland, New Zealand
Website | E-Mail
Phone: +64 9 414 0800
Fax: +64 9 441 8181

Special Issue Information

Dear Colleagues,

Multi-cored CPUs and many-cored accelerators such as GPUs and FPGAs are becoming commonly affordable and available platforms for parallel computing. Developing highly-efficient algorithms and applications for these platforms is an exciting area and one that presents even greater challenges as core counts increase. Algorithms that make optimal use of hybrid many-cored processors and accelerators also stretch the capabilities of existing parallel software and tools. Approaches vary from classic data-parallelism algorithms deployed on massively-cored accelerators to thread or message-managed task communication on many-core central processing units. The close-coupling potential of having many cores on a single chip or device presents opportunities for developing new multi-core algorithms that must of necessity go beyond the parallel supercomputer algorithms of yester-year.

Hybrid algorithms making use of a whole spectrum of different parallel granularities are possible on platforms with multiple accelerators driven by multiple CPU cores. Recent developments have taken the number of economically feasible CPU cores well into double figures. It is likely that CPU and GPU solutions will continue to approach the many-core problem from different ends of the
granularity scale, but with interesting hybrids emerging and with a complex trade-off space where they meet in the middle. Algorithms that can exploit this topical granularity spectrum will grow in importance. Software that embodies such algorithms will have a profound impact in a number of application markets and industries including: simulation and modelling; games and animation; and many others where many cores can be kept busy.

Quality articles describing innovative and original work in algorithms that use many-cored devices are sought for a special issue on "Algorithms for Multi-Core Parallel Computation." Reported work on algorithms using topical new and emerging devices such as:
multi-cored CPUs; many-cored GPUs; or other CPU/accelerator combinations using many cores, is particularly sought.

Prof. Dr. Ken Hawick
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • many-core algorithms
  • many-core devices
  • performance accelerators
  • multi-accelerator algorithms
  • multi-cored CPU
  • algorithms

Published Papers (5 papers)

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Research

Open AccessArticle Solving Matrix Equations on Multi-Core and Many-Core Architectures
Algorithms 2013, 6(4), 857-870; doi:10.3390/a6040857
Received: 27 September 2013 / Revised: 12 November 2013 / Accepted: 18 November 2013 / Published: 25 November 2013
Cited by 3 | PDF Full-text (226 KB) | HTML Full-text | XML Full-text
Abstract
We address the numerical solution of Lyapunov, algebraic and differential Riccati equations, via the matrix sign function, on platforms equipped with general-purpose multicore processors and, optionally, one or more graphics processing units (GPUs). In particular, we review the solvers for these equations, as
[...] Read more.
We address the numerical solution of Lyapunov, algebraic and differential Riccati equations, via the matrix sign function, on platforms equipped with general-purpose multicore processors and, optionally, one or more graphics processing units (GPUs). In particular, we review the solvers for these equations, as well as the underlying methods, analyze their concurrency and scalability and provide details on their parallel implementation. Our experimental results show that this class of hardware provides sufficient computational power to tackle large-scale problems, which only a few years ago would have required a cluster of computers. Full article
(This article belongs to the Special Issue Algorithms for Multi Core Parallel Computation)
Open AccessArticle PMS6MC: A Multicore Algorithm for Motif Discovery
Algorithms 2013, 6(4), 805-823; doi:10.3390/a6040805
Received: 15 September 2013 / Revised: 8 November 2013 / Accepted: 11 November 2013 / Published: 18 November 2013
Cited by 2 | PDF Full-text (260 KB) | HTML Full-text | XML Full-text
Abstract
We develop an efficient multicore algorithm, PMS6MC, for the (l; d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. PMS6MC is based
[...] Read more.
We develop an efficient multicore algorithm, PMS6MC, for the (l; d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. PMS6MC is based on PMS6, which is currently the fastest single-core algorithm for motif discovery in large instances. The speedup, relative to PMS6, attained by our multicore algorithm ranges from a high of 6.62 for the (17,6) challenging instances to a low of 2.75 for the (13,4) challenging instances on an Intel 6-core system. We estimate that PMS6MC is 2 to 4 times faster than other parallel algorithms for motif search on large instances. Full article
(This article belongs to the Special Issue Algorithms for Multi Core Parallel Computation)
Open AccessArticle New Parallel Sparse Direct Solvers for Multicore Architectures
Algorithms 2013, 6(4), 702-725; doi:10.3390/a6040702
Received: 9 August 2013 / Revised: 16 October 2013 / Accepted: 21 October 2013 / Published: 1 November 2013
Cited by 7 | PDF Full-text (315 KB) | HTML Full-text | XML Full-text
Abstract
At the heart of many computations in science and engineering lies the need to efficiently and accurately solve large sparse linear systems of equations. Direct methods are frequently the method of choice because of their robustness, accuracy and potential for use as black-box
[...] Read more.
At the heart of many computations in science and engineering lies the need to efficiently and accurately solve large sparse linear systems of equations. Direct methods are frequently the method of choice because of their robustness, accuracy and potential for use as black-box solvers. In the last few years, there have been many new developments, and a number of new modern parallel general-purpose sparse solvers have been written for inclusion within the HSL mathematical software library. In this paper, we introduce and briefly review these solvers for symmetric sparse systems. We describe the algorithms used, highlight key features (including bit-compatibility and out-of-core working) and then, using problems arising from a range of practical applications, we illustrate and compare their performances. We demonstrate that modern direct solvers are able to accurately solve systems of order 106 in less than 3 minutes on a 16-core machine. Full article
(This article belongs to the Special Issue Algorithms for Multi Core Parallel Computation)
Open AccessArticle Multi-Core Parallel Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings
Algorithms 2013, 6(4), 747-761; doi:10.3390/a6040747
Received: 17 September 2013 / Revised: 16 October 2013 / Accepted: 21 October 2013 / Published: 1 November 2013
PDF Full-text (1431 KB) | HTML Full-text | XML Full-text
Abstract
Gradual patterns aim at describing co-variations of data such as the higher the size, the higher the weight. In recent years, such patterns have been studied more and more from the data mining point of view. The extraction of such patterns relies on
[...] Read more.
Gradual patterns aim at describing co-variations of data such as the higher the size, the higher the weight. In recent years, such patterns have been studied more and more from the data mining point of view. The extraction of such patterns relies on efficient and smart orderings that can be built among data, for instance, when ordering the data with respect to the size, then the data are also ordered with respect to the weight. However, in many application domains, it is hardly possible to consider that data values are crisply ordered. When considering gene expression, it is not true from the biological point of view that Gene 1 is more expressed than Gene 2, if the levels of expression only differ from the tenth decimal. We thus consider fuzzy orderings and fuzzy gamma rank correlation. In this paper, we address two major problems related to this framework: (i) the high memory consumption and (ii) the precision, representation and efficient storage of the fuzzy concordance degrees versus the loss or gain of computing power. For this purpose, we consider multi-precision matrices represented using sparse matrices coupled with parallel algorithms. Experimental results show the interest of our proposal. Full article
(This article belongs to the Special Issue Algorithms for Multi Core Parallel Computation)
Open AccessArticle Multi-Threading a State-of-the-Art Maximum Clique Algorithm
Algorithms 2013, 6(4), 618-635; doi:10.3390/a6040618
Received: 15 August 2013 / Revised: 13 September 2013 / Accepted: 18 September 2013 / Published: 3 October 2013
Cited by 14 | PDF Full-text (244 KB) | HTML Full-text | XML Full-text
Abstract We present a threaded parallel adaptation of a state-of-the-art maximum clique algorithm for dense, computationally challenging graphs. We show that near-linear speedups are achievable in practice and that superlinear speedups are common. We include results for several previously unsolved benchmark problems. Full article
(This article belongs to the Special Issue Algorithms for Multi Core Parallel Computation)

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