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Recent Trends comprehensive survey of Asynchronous Network and its Significant

Authors(2):

Ajitesh S. Baghel, Rakesh Kumar Katare
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Advancement in electronics and computer architecture has opened new domains of the parallel and distributed computing. The advent of the Multi Core CPU’s with the blending of the open MPI techniques has give the wings to the distributed computing with assurance of the parallelism. In this proposal, various important aspects of asynchronous algorithms and its data structures for parallel and distributed architecture will be investigated. This article has proposed and will examine networks of processor for asynchronous system to compute faster for more iteration. The complexity of interprocessor communication will be investigated. Hence efficient asynchronous algorithm is main concerned of the study for MPI systems.

Ajitesh S. Baghel, Rakesh Kumar Katare

Asynchronous Network, Distributed System, Parallel Computing, .

  1. Pengfei Yang and Biao Chen "To Listen or Not: Distributed Detection with Asynchronous Transmissions", IEEE SIGNAL PROCESSING LETTERS, VOL. 22, NO. 5, MAY 2015.
  2. Ryan K. Williams, Andrea Gasparri, Attilio Priolo, and Gaurav S. Sukhatme "Evaluating Network Rigidity in Realistic Systems: Decentralization, Asynchronicity, and Parallelization", IEEE TRANSACTIONS ON ROBOTICS, VOL. 30, NO. 4, AUGUST 2014.
  3. Alexandre Maurer and Sebastien Tixeuil "Containing Byzantine Failures with Control Zones", IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 26, NO. 2, FEBRUARY 2015.
  4. Haoyuan Li, Ali Ghodsi, Matei Zaharia, Scott Shenker and Ion Stoica "Reliable, Memory Speed Storage for Cluster Computing Frameworks", Technical Report No. UCB/EECS-2014-135
  5. Matthieu Dreher, Bruno Ran "A Flexible Framework for Asynchronous In Situ and In Transit Analytics for Scientic Simulations", 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, May 2014, Chicago, United States. IEEE Computer Science Press. <hal-00941413>
  6. Ruiliang Zhang and James T. Kwok "Asynchronous Distributed ADMM for Consensus Optimization", Proceedings of the 31 st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32.
  7. S. G. Akl. The Design and Analysis of Parallel Algorithms. Prentice Hall, Englewood Cliffs, 1997.
  8. Bertsekas, D.P., and J.N. Tsitsiklis (1989). Parallel and Distributed Computation: Numerical Methods, Prentice Hall, Englewood Cliffs, NJ.
  9. Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J. M. Hellerstein. Distributed graphlab: a framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment, 5(8):716-727, 2012.
  10. G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: a system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pages 135-146. ACM, 2010.
  11. S. Melnik, A. Gubarev, J. J. Long, G. Romer, S. Shivakumar, M. Tolton, and T. Vassilakis. Dremel: interactive analysis of web-scale datasets. Proceedings of the VLDB Endowment, 3(1-2):330- 339, 2010.
  12. R. Power and J. Li. Piccolo: Building Fast, Distributed Programs with Partitioned Tables. In Proceedings of the 9th USENIX conference on Operating systems design and implementation, pages 293-306. USENIX Association, 2010.
  13. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica. Resilient Distributed Datasets: A FaultTolerant Abstraction for In-Memory Cluster Computing. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation. USENIX Association, 2012.
  14. M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica. Discretized streams: Fault-tolerant streaming computation at scale. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, pages 423-438. ACM, 2013.
  15. Apache Oozie. http://incubator.apache.org/oozie/Luigi. https://github.com/spotify/luigi.
  16. Apache Crunch. http://crunch.apache.org/Apache Mahout. http://mahout.apache.org/
  17. A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, N. Zhang, S. Antony, H. Liu, and R. Murthy. Hive a petabyte scale data warehouse using hadoop. In Data Engineering (ICDE), 2010 IEEE 26th International Conference on, pages 996-1005. IEEE, 2010.
  18. C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig latin: a not-so-foreign language for data processing. In SIGMOD '08, pages 1099-1110.
  19. C. Chambers et al. FlumeJava: easy, efficient dataparallel pipelines. In PLDI 2010.
  20. Harel, D., Feldman, Y.: Algorithmics, the spirit of computing, 572 pages. Springer (2012).
  21. Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Communications of the ACM 21(7), 558-565 (1978).
  22. Raynal, M.: Distributed algorithms for message-passing systems, 515 pages. Springer, ISBN:978-3-642-38122-5.
  23. Lamport, L.: On inter-process communications, Part I: Basic formalism. Distributed Computing 1(2), 77-85 (1986).
  24. Herlihy, M.P.: Wait-free synchronization. ACM Transactions on Programming Languages and Systems 13(1), 124-149 (1991).
  25. Fischer, M.J., Lynch, N.A., Paterson, M.S.: Impossibility of distributed consensus with one faulty process. Journal of the ACM 32(2), 374-382 (1985).
  26. Loui, M., Abu-Amara, H.: Memory requirements for agreement among unreliable asynchronous processes. Advances in Computing Research 4, 163-183 (1987).
  27. G. Zhao, J. R. Perilla, E. L. Yufenyuy, X. Meng, B. Chen, J. Ning, J. Ahn, A. M. Gronenborn, K. Schulten, and C. Aiken, "Mature HIV-1 Capsid Structure by Cryo-electron Microscopy and All-Atom Molecular Dynamics," pp. 643-646, 2013.
  28. H. Yu, C. Wang, R. Grout, J. Chen, and K.-L. Ma, "In situ visualization for large-scale combustion simulations," Computer Graphics and Applications, IEEE, vol. 30, no. 3, pp. 45-57, 2010.
  29. W. Gu, G. Eisenhauer, K. Schwan, and J. Vetter, "Falcon: On-line monitoring for steering parallel programs," in In Ninth International Conference on Parallel and Distributed Computing and Systems (PDCS'97), 1998, pp. 699-736.
  30. J. C. Bennett, H. Abbasi, P.-T. Bremer, R. Grout, A. Gyulassy, T. Jin, S. Klasky, H. Kolla, M. Parashar, V. Pascucci, P. Pebay, D. Thompson, H. Yu, F. Zhang, and J. Chen, "Combining in-situ and in-transit processing to enable extreme-scale scientific analysis," in nternational Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press, 2012, pp. 49:1-49:9.
  31. Shalev-Shwartz, S., Singer, Y., and Srebro, N. Pegasos: Primal estimated sub-gradient solver for SVM. In Proceedings of the 24th  International Conference on Machine Learning, pp. 807-814, 2007.
  32. Niu, F., Recht, B., R' e, C., and Wright, S.J. Hogwild!: A lock-free approach to parallelizing stochastic gradient descent. In Advances in Neural Information Processing Systems 24, 2011.
  33. Langford, J., Smola, A., and Zinkevich, M. Slow learners are fast. In Advances in Neural Information Processing Systems 22, 2009.
  34. Agarwal, A. and Duchi, J.C. Distributed delayed stochastic optimization. In Advances in Neural Information Processing Systems 24, 2011.
  35. Ho, Q., Cipar, J., Cui, H., Lee, S., Kim, J.K., Gibbons, P.B., Gibson, G.A., Ganger, G., and Xing, E. More effective distributed ML via a stale synchronous parallel parameter server. In Advances  in Neural Information Processing Systems 26, pp. 1223-1231, 2013.
  36. Li, M., Andersen, D.G., and Smola, A. Distributed delayed proximal gradient methods. In NIPS Workshop on Optimization for Machine Learning, 2013.
  37. Bertsekas, D.P. and Tsitsiklis, J.N. Parallel and Distributed Computation. Prentice Hall, 1989.
  38. Zhu, H., Cano, A., and Giannakis, G.B. Distributed consensus-based demodulation: Algorithms and error analysis. IEEE Transactions on Wireless Communications, 9(6): 2044-2054, 2010.
  39. Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations  and Trends in Machine Learning, 3(1):1-122, 2011.
  40. D. Chazan and W. Miranker, Chaotic relaxation, Linear Algebra Appl., 2 (1969), pp. 199-222.
  41. G. Baudet, Asynchronous iterative methods for multiprocessors, J. Assoc. Comput. Mach., 25 (1978), pp. 226{244. -46.
  42. Bertsekas, D.P. (1982). Distributed Dynamic Programming. IEEE Transactions on Automatic Control, AC-27,610-16.
  43. J. N. Tsitsiklis, On the stability of asynchronous iterative processes, Math. Systems Theory, 20 (1987), 137-153.
  44. B. Parhami and M. Rakov, "Perfect Difference Networks and Related Interconnection Structures for Parallel and Distributed Systems", IEEE Trans. on Parallel and Distributed Systems, vol. 16, no. 8,pp. 714-724, Aug. 2005.

Publication Details

Published in : Volume 1 | Issue 4 | July-August - 2015
Date of Publication Print ISSN Online ISSN
2015-08-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
114-123 IJSRSET151418   Technoscience Academy

Cite This Article

Ajitesh S. Baghel, Rakesh Kumar Katare, "Recent Trends comprehensive survey of Asynchronous Network and its Significant", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 4, pp.114-123, July-August-2015.
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