International Journal of Mathematics Trends and Technology
Volume 42 | Number 1 | Year 2017 | Article Id. IJMTT-V42P502 | DOI : https://doi.org/10.14445/22315373/IJMTT-V42P502
Parallel computing systems write job breakdown schemes in a true parallel processing manner. Such arrangements apportion the algorithmic program and auctioning unit as computing imaginations which leads to highly inter process communications theory capacities. We concentrate on real-time and non preemptive arrangements. A large assortment of experiments has been carried on the advised algorithmic program. Goal of calculation example is to allow a realistic histrionics of the costs of programming. The research paper constitutes the optimum iterative aspect job division programming in the broadcast heterogeneous surroundings. Main goal of the algorithm is to amend the performance of the schedule in the form of iteration using results from previous looping. The algorithmic program first applies the b-level calculation to compute the initial schedule and then amend it iteratively. The consequences demonstrate the gain of the job breakdown. The main features of our method are optimum programming and strong associate between breakdown, programming and communication. Some significant examples for job breakdown are also talked about in the paper. We aim the algorithmic program for job breakdown which amend the inter action communication among the jobs and use the appeals of the arrangement in the effective manner. The proposed algorithmic program conduces the inter-process communicating cost reduction between the accomplishing processes. This paper is the broadened version of [1].
[1] Javed A.,Rafiqul Z. K., “Dynamic Task Partitioning Model in Parallel Computing Systems”, Proceeding First International conference on Advanced Information Technology (ICAIT- 2012),Coimbatore, Tamil Nadu,
[2] David J. Lilja, ‘‘Experiments with a Task Partitioning Model for Heterogeneous Computing,’’ University of Minnesota AHPCRC Preprint no. 92-142, Minneapolis, MN, December 1992.
[3] L. G. Valiant. ‘‘A bridging model for parallel computation’’. Communications of the ACM, 33(8):103-111, August 1990.
[4] B. H. H. Juurlink and H. A. G. Wijshoff. ‘‘Communication primitives for BSP Computers’’ Information Processing Letters, 58:303-310, 1996.
[5] H. EI-Rewini and H.Ali, ‘‘The Programming Problem with Communication’’ ,Technical Report ,University Of Nebraska at Omaha,pp 78-89,1993.
[6] D. Menasce and V. Almeida, ‘‘Cost-Performance Analysis of Heterogeneity in Supercomputer Architectures ’’, Proc. Supercomputing ’90, pp. 169-177, 1990.
[7] T.L. Adam, K.M. Chandy, and J.R. Dickson, “A Comparison of List Schedules for Parallel Processing Systems,” Comm. ACM, vol. 17, pp. 685-689, 1974.
[8] L. G. Valiant. ‘‘A bridging model for parallel computation’’. Communications of the ACM, 33(8):103-111, August 1990.
[9] H. El-Rewini,T. G. Lewis, Hesham H. Ali , ‘‘ Task Programming in Parallel and Distributed Systems”,Prentice Hall Series in Innovative Technology,pp 48-50.1994.
[10] M. D. Ercegovac, ‘‘Heterogeneity in Supercomputer Architectures,’’ Parallel Computing, No. 7, pp.367-372, 1988.
[11] P.B. Gibbons. A more practical pram model. In Pro-ceedings of the i989 Symposium on Parallel Algorithms and Architectures, pages 158-168, Santa Fe, NM, June 1989.
[12] Y. Aumann and M. O. Rabin. ‘‘Clock construction in fully asynchronous parallel systems and PRAM simulation”. In Proc. 33rd IEEE Symp. on Foundations of Computer Science, pages 147-156, October 1992.
[13] R. M. Karp and V. Ramachandran. ,‘‘ Parallel algorithms for shared-memory machines”. In J. van Leeuwen, editor, Handbook of Theoretical Computer Science, Volume A, pages 869-941. Elsevier Science Publishers B.V., Amsterdam, The Netherlands, 1990.
[14] H.Topcuoglu, S. Hariri, and M.Y. Wu, “Performance-Effective and Low-Complexity Task Programming for Heterogeneous Computing,”IEEE Trans. Parallel and Distributed Systems, Vol. 13, No.3, pp. 250-271, March 2002.
[15] N. Islam and A. Prodromidis and M. S. Squillante, ‘‘Dynamic Partitioning in Different Distributed- Memory Environments’’, Proceedings of the 2nd Workshop on Job Programming Strategies for Parallel Processing, pages 155-170,April 1996.
[16] G. Liu, K. Poh, M. Xie, “Iterative list programming for heterogeneous computing, J. Parallel Distrib. Comput”. 65 (5) (200 Manik Sharma, Smriti, “Static and Dynamic BNP.
[17] Parallel Programming Algorithms For Distributed Database”, IJCT, Vol 1, No.1, 2011. 5) 658– 663.
Akhil Kumar, Prof D.P Singh, "Optimum Job Segmentation Example in Broadcast Heterogenous Parallel Computing Surroundings," International Journal of Mathematics Trends and Technology (IJMTT), vol. 42, no. 1, pp. 10-15, 2017. Crossref, https://doi.org/10.14445/22315373/IJMTT-V42P502
PDF 
Abstract 
Keywords 
References 
Citation