EFFICIENCY ESTIMATION OF PARALLEL ALGORITHM OF ENHANCED HISTORICAL DATA INTEGRATION ON COMPUTATIONAL GRID

Authors

  • V. Turchenko
  • C. Triki
  • Lucio Grandinetti
  • Anatoly Sachenko

DOI:

https://doi.org/10.47839/ijc.4.3.357

Keywords:

Sensor drift, integration historical data, neural networks, coarse-grain parallel algorithm, dynamic mapping, computational grids

Abstract

The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.

References

. A. Sachenko, V. Kochan, V. Turchenko, "Intelligent Distributed Sensor Network,” Proceedings of 15th IEEE Instrumentation and Measurement Technology Conference IMTC/98, St. Paul, USA, 1998, pp. 60-66.

. A. Sachenko, V. Kochan, V. Turchenko, "Instrumentation for Data Gathering,” IEEE I&M Magazine, vol. 6, no. 3, September 2003, pp. 34-40.

. J. Brignell, "Digital compensation of sensors,” Scientific Instruments, vol. 20, no 9, 1987, pp. 1097-1102.

. V. Turchenko, "Neural network-based methods and means for improving the effectiveness of distributed sensor data acquisition and processing networks,” Ph.D. Thesis, National University “Lvivska Politechnika,” Lviv, p. 188, 2001 (in Ukrainian).

. A. Alippi, A. Ferrero, V. Piuri, "Artificial Intelligence for Instruments & Applications,” IEEE I&M Magazine, June 98, pp. 9-17.

. P. Daponte, D. Grimaldi, "Artificial Neural Networks in Measurements,” Measurement, vol. 23, 1998, pp. 93-115.

. A. Sachenko, V. Kochan, V. Turchenko, V. Golovko, J. Savitsky, A. Dunets, T. Laopoulos, "Sensor Errors Prediction Using Neural Networks,” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks IJCNN'2000, Como (Italy), vol. IV, 2000, pp. 441-446.

. V. Golovko, A. Galushkin, "Neural Networks: training, models and applications,” Moscow, Radiotechnika, p. 256, 2001 (in Russian).

. A. Sachenko, V. Kochan, V. Turchenko, "Sensor Drift Prediction Using Neural Networks,” Proceedings of the International Workshop on Virtual and Intelligent Measurement Systems VIMS'2000, Annapolis, USA, 2000, pp. 88-92.

. V. Turchenko, V. Kochan and A. Sachenko, “Advanced Method of Historical Data Integration Using Neural Networks,” Sensor and Systems, Moscow, vol. 7 (38), 2002, pp. 35-38 (in Russian).

. V. Turchenko, "Static Mapping of Integrating Historical Data Neural Networks on Parallel Computer,” Proceedings of the 16th IASTED International Conference on Parallel and Distributed Computing and Systems, 2004, MIT, Cambridge, MA, USA, pp. 884-889.

. V. Turchenko, "Parallel Algorithm of Dynamic Mapping of Integrating Historical Data Neural Networks,” Information Technologies and Systems, vol. 7, no. 1, 2004, pp. 45-52.

. Patent #50380 Ukraine, IPC 7 G06F15/18, "Method of the training set formation for neural network predicting drift of data acquisition device,” A.Sachenko (UA), V.Kochan (UA), V.Turchenko (UA), V.Golovko (BY), J.Savitsky (BY), T.Laopoulos (GR), filled 04 Jan 2000, issued 15 Nov 2002, p. 14.

. A. Sachenko, V. Kochan, R. Kochan, V. Turchenko, K. Tsahouridis, Th. Laopoulos, "Error Compensation in an Intelligent Sensing Instrumentation System,” 18th IEEE Instrumentation and Measurement Technology Conference IMTC/2001, Budapest, Hungary, May 21-23, 2001, pp. 869-874.

. S. Wang, "Reducing the communication cost in simulating layered neural networks on a hypercube machine,” Proceedings of Parallel Computing, Elsevier, Amsterdam, 1989, pp. 375-380.

. A. Petrowski, G. Dreyfus, C. Girault, "Performance analysis of a pipelined back-propagation parallel algorithm,” IEEE Transactions on Neural Networks, vol. 4, 1993, pp. 970-981.

. H. Paugam-Moisy, "Optimal speedup conditions for a parallel back-propagation algorithm,” Proceedings of CONPAR’92-VAPP V, Lecture Notes in Computer Science, vol. 682, Springer-Verlag, 1992, pp. 719-724.

. H. Hopp, L. Prechelt, "CuPit-2: A Portable parallel programming language for artificial neural networks,” Proceedings of the 15th IMACS World Congress Scientific Computation Modeling and Applied Mathematics, vol. 6, Berlin, 1997, pp. 493-498.

. Z. Hanzalek, "A parallel algorithm for gradient training of feed-forward neural networks,” Parallel Computing, vol. 24, no. 5-6, 1998, pp. 823-839.

. J.M.J. Murre, "Transputers and neural networks: An analysis of implementation constraints and performance,” IEEE Transactions on Neural Networks, vol. 4, no. 2, 1993, pp. 284-292.

. I. Foster, C. Kesselman, "Globus: a metacomputing infrastructure toolkit,” International Journal of Supercomputer Application, vol. 11, no. 2, 1997, pp. 115-128.

. Global Grid Forum webpage. http://www.gridforum.org/

. J. Dongarra, D. Laforenza, S. Orlando (Eds), "Recent Advances in Parallel Virtual Machine and Message Passing Interface,” Lecture Notes in Computer Science, Berlin: Springer-Verlag, 2003, vol. 2840, ISBN 3-540-20149-1.

. N.T. Karonis, B. Toonen, I. Foster, "MPICH-G2: A Grid-enabled implementation of the Message Passing Interface,” Journal of Parallel and Distributed Computing, vol. 63, no. 5, 2003, pp. 551-563.

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Published

2014-08-01

How to Cite

Turchenko, V., Triki, C., Grandinetti, L., & Sachenko, A. (2014). EFFICIENCY ESTIMATION OF PARALLEL ALGORITHM OF ENHANCED HISTORICAL DATA INTEGRATION ON COMPUTATIONAL GRID. International Journal of Computing, 4(3), 9-19. https://doi.org/10.47839/ijc.4.3.357

Issue

2005, Volume 4, Issue 3

Section

Articles

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