The optimal resource allocation satisfies the needed capacity of the used resources. To such analysis we can use both analytical and simulation methods. Principally analytical methods (AM) belong to the preferred method in comparison to the simulation method, because of their potential ability of more general analysis and also of ability to analyze massive parallel computers. This article goes further in developing AM based on queuing theory results in relation to our published paper in [9]. The extensions are in extending derived AM to whole range of parallel computers and also to sum up public acceptance of our published paper. The article therefore describes deriving of correction factor of standard AM based on M/M/m and M/M/1queuing theory systems. In detail the paper describes derivation of a correction factor for standard AM to study more precise their performance. The paper contributions are in unified AM and in deriving correction factor in order to take into account real non-exponential nature of the inputs to the computing nodes and node’s communication channels. The derived analytical results were compared with performed simulation results in order to estimate the magnitude of improvement. Likewise the corrected AM were tested under various ranges of parameters, which influence the architecture of the parallel computers and its communication networks too. These results are very important in practical use.
| Published in | American Journal of Networks and Communications (Volume 3, Issue 1) |
| DOI | 10.11648/j.ajnc.20140301.11 |
| Page(s) | 1-12 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Parallel Computer, Communication System, Correction Factor, Analytical Model, Performance, Queuing System, Overhead Latencies, Modeling
| [1] | Abderazek A. B., Multicore systems on chip - Practical Software/Hardware design, Imperial college press, 200 pp., 2010 |
| [2] | Arora S., Barak B., Computational complexity - A modern Approach, Cambridge University Press, 573 pp., 2009 |
| [3] | Dattatreya G. R., Performance analysis of queuing and computer network, University of Texas, Dallas, USA, 472 pp., 2008 |
| [4] | Dubois M., Annavaram M., Stenstrom P., Parallel Computer Organization and Design, 560 pages, 2012 |
| [5] | Dubhash D.P., Panconesi A., Concentration of measure for the analysis of randomized algorithms, Cambridge University Press, UK, 2009 |
| [6] | Gelenbe E., Analysis and synthesis of computer systems, Imperial College Press, 324 pages, 2010 |
| [7] | Giambene G., Queuing theory and telecommunications, 585 pp., Springer, 2005 |
| [8] | Hager G., Wellein G., Introduction to High Performance Computing for Scientists and Engineers, 356 pages, July 2010 |
| [9] | Hanuliak M., Hanuliak P., Performance modeling of parallel computers NOW and Grid Vol. 2/5, Am. J. of Networks and Comm., Science GP, USA, 112-124 pp., 2013 |
| [10] | Hanuliak J., Hanuliak I., To performance evaluation of distributed parallel algorithms, Kybernetes, Volume 34, No. 9/10, UK, 1633-1650 pp., 2005 |
| [11] | Hanuliak P., Hanuliak I., Performance evaluation of iterative parallel algorithms, Kybernetes, Volume 39, No.1, UK, 107- 126 pp., 2010 |
| [12] | Hanuliak P., Analytical method of performance prediction in parallel algorithms, The Open Cybernetics and Systemic Journal, Vol. 6, Bentham Open, UK, 38-47 pp., 2012 |
| [13] | Hanuliak P., Complex performance evaluation of parallel Laplace equation, AD ALTA – Vol. 2, issue 2, Magnanimitas, Czech republic, 104-107 pp.,2012 |
| [14] | Harchol-Balter Mor, Performance modeling and design of computer systems, Cambridge University Press, UK, 576 pp., 2013 |
| [15] | Hillston J., A Compositional Approach to Performance Modeling, University of Edinburg, Cambridge University Press, UK, 172 pages, 2005 |
| [16] | Hwang K. and coll., Distributed and Parallel Computing, Morgan Kaufmann, 472 pages, 2011 |
| [17] | John L. K., Eeckhout L., Performance evaluation and benchmarking, CRC Press, 2005 |
| [18] | Kshemkalyani A. D., Singhal M., Distributed Computing, University of Illinois, Cambridge University Press, UK, 756 pages, 2011 |
| [19] | Kirk D. B., Hwu W. W., Programming massively parallel processors, Morgan Kaufmann, 280 pages, 2010 |
| [20] | Kostin A., Ilushechkina L., Modeling and simulation of distributed systems, Imperial College Press, 440 pages, 2010 |
| [21] | Kumar A., Manjunath D., Kuri J., Communication Networking , Morgan Kaufmann, 750 pp., 2004 |
| [22] | Kushilevitz E., Nissan N., Communication Complexity, Cambridge University Press, UK, 208 pages, 2006 |
| [23] | Kumar A., Manjunath D., Kuri J., Communication Networking , Morgan Kaufmann, 750 pp., 2004 |
| [24] | Kwiatkowska M., Norman G., and Parker D., PRISM 4.0: Verification of Probabilistic Real-time Systems, In Proc. of 23rd CAV’11, Vol. 6806, Springer, 585-591 pp., 2011 |
| [25] | Le Boudec Jean-Yves, Performance evaluation of computer and communication systems, CRC Press, 300 pages, 2011 |
| [26] | McCabe J., D., Network analysis, architecture, and design (3rd edition), Elsevier/ Morgan Kaufmann, 496 pages, 2010 |
| [27] | Miller S., Probability and Random Processes, 2nd edition, Academic Press, Elsevier Science, 552 pages, 2012 |
| [28] | Misra Ch. S.,Woungang I., Selected topics in communication network and distributed systems, Imperial college press, 808 pages, 2010 |
| [29] | Natarajan Gautam, Analysis of Queues: Methods and Applications, CRC Press, 802 pages, 2012 |
| [30] | Peterson L. L., Davie B. C., Computer networks – a system approach, Morgan Kaufmann, 920 pages, 2011 |
| [31] | Resch M. M., Supercomputers in Grids, Int. J. of Grid and HPC, No.1, 1 - 9 pp., 2009 |
| [32] | Riano l., McGinity T.M., Quantifying the role of complexity in a system´s performance, Evolving Systems, Springer Verlag, 189 – 198 pp., 2011 |
| [33] | Ross S. M., Introduction to Probability Models, 10th edition, Academic Press, Elsevier Science, 800 pages, 2010 |
| [34] | Wang L., Jie Wei., Chen J., Grid Computing: Infrastructure, Service, and Application, CRC Press, 2009 |
| [35] | www pages |
| [36] | www.top500.org |
| [37] | www. intel.com |
APA Style
Michal Hanuliak. (2014). Unified Analytical Models of Parallel and Distributed Computing. American Journal of Networks and Communications, 3(1), 1-12. https://doi.org/10.11648/j.ajnc.20140301.11
ACS Style
Michal Hanuliak. Unified Analytical Models of Parallel and Distributed Computing. Am. J. Netw. Commun. 2014, 3(1), 1-12. doi: 10.11648/j.ajnc.20140301.11
AMA Style
Michal Hanuliak. Unified Analytical Models of Parallel and Distributed Computing. Am J Netw Commun. 2014;3(1):1-12. doi: 10.11648/j.ajnc.20140301.11
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Download@article{10.11648/j.ajnc.20140301.11,
author = {Michal Hanuliak},
title = {Unified Analytical Models of Parallel and Distributed Computing},
journal = {American Journal of Networks and Communications},
volume = {3},
number = {1},
pages = {1-12},
doi = {10.11648/j.ajnc.20140301.11},
url = {https://doi.org/10.11648/j.ajnc.20140301.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20140301.11},
abstract = {The optimal resource allocation satisfies the needed capacity of the used resources. To such analysis we can use both analytical and simulation methods. Principally analytical methods (AM) belong to the preferred method in comparison to the simulation method, because of their potential ability of more general analysis and also of ability to analyze massive parallel computers. This article goes further in developing AM based on queuing theory results in relation to our published paper in [9]. The extensions are in extending derived AM to whole range of parallel computers and also to sum up public acceptance of our published paper. The article therefore describes deriving of correction factor of standard AM based on M/M/m and M/M/1queuing theory systems. In detail the paper describes derivation of a correction factor for standard AM to study more precise their performance. The paper contributions are in unified AM and in deriving correction factor in order to take into account real non-exponential nature of the inputs to the computing nodes and node’s communication channels. The derived analytical results were compared with performed simulation results in order to estimate the magnitude of improvement. Likewise the corrected AM were tested under various ranges of parameters, which influence the architecture of the parallel computers and its communication networks too. These results are very important in practical use.},
year = {2014}
}
TY - JOUR T1 - Unified Analytical Models of Parallel and Distributed Computing AU - Michal Hanuliak Y1 - 2014/02/28 PY - 2014 N1 - https://doi.org/10.11648/j.ajnc.20140301.11 DO - 10.11648/j.ajnc.20140301.11 T2 - American Journal of Networks and Communications JF - American Journal of Networks and Communications JO - American Journal of Networks and Communications SP - 1 EP - 12 PB - Science Publishing Group SN - 2326-8964 UR - https://doi.org/10.11648/j.ajnc.20140301.11 AB - The optimal resource allocation satisfies the needed capacity of the used resources. To such analysis we can use both analytical and simulation methods. Principally analytical methods (AM) belong to the preferred method in comparison to the simulation method, because of their potential ability of more general analysis and also of ability to analyze massive parallel computers. This article goes further in developing AM based on queuing theory results in relation to our published paper in [9]. The extensions are in extending derived AM to whole range of parallel computers and also to sum up public acceptance of our published paper. The article therefore describes deriving of correction factor of standard AM based on M/M/m and M/M/1queuing theory systems. In detail the paper describes derivation of a correction factor for standard AM to study more precise their performance. The paper contributions are in unified AM and in deriving correction factor in order to take into account real non-exponential nature of the inputs to the computing nodes and node’s communication channels. The derived analytical results were compared with performed simulation results in order to estimate the magnitude of improvement. Likewise the corrected AM were tested under various ranges of parameters, which influence the architecture of the parallel computers and its communication networks too. These results are very important in practical use. VL - 3 IS - 1 ER -
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