PARALLEL PROCESSING PROBLEM AND SOLUTION - A CASE STUDY ON MATLAB PARALLEL COMPUTING TOOLBOX BATCH PROCESSING

Nazurah Kamil #1, Mohamed Faidz Mohamed Said #2

# Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA
70300 Seremban, Negeri Sembilan, MALAYSIA

1 nazurahkamil95@gmail.com
2 faidzms@ieee.org

 
Abstract—MATLAB or matrix laboratory is a multi-paradigm mathematical calculation and fourth-creation programming language. Parallel Computing Toolbox (PCT) solve computationally and data-intensive problems utilising multicore processors, GPUs, and computer clusters. Batch processing machines manage several jobs synchronously as a batch. The toolbox provides twelve workers, MATLAB computational engines, to execute applications locally on a multicore desktop. The same application on a computer cluster or a grid computing service can be run without changing the code. In the parallel-batching scheduling issue, some jobs can be processed as a batch at the same time on a machine at one time. When a batch machine completes the facilities of a batch and there is at least one product waiting in the queue, a real-time control decision is made to choose whether to start a job with a limited batch or wait until future job arrivals arise. This paper provides reviews of the case study on MATLAB Parallel Computing Toolbox batch processing.

Keywords: batch processing, job arrivals, MATLAB



REFERENCES

[1]	https://www.mathworks.co.uk/searchresults/?search_submit=matla	bcentral&query=matlab+parallel+computation+example&q=matlab+parallel+computation+example&c[]=matlabcentral	

[2]	Roth, P.J. and J.D. Daniels, Doing More with Less- Exploring Batch Processing and Outsourcing in Academic. Management and Administration: p. 361-365.

[3]	Li, L., et al., A MATLAB-based image processing algorithm for analyzing cupping profiles of two-layer laminated wood products. Measurement, 2014. 53: p. 234-239.

[4]	Li, X., et al., Scheduling unrelated parallel batch processing machines with non-identical job sizes Computer & Operations Research, 2013. 40: p. 2983-2990.

[5]	Bhullar, D. and N. Verma, Review Paper on Batch Processing and Streaming Processing. International Journal Of Technology And Computing(IJTC), 2017. 3: p. 44-48.

[6]	Bingmann, T., et al., Thrill : High-Performance Algorithmic Distributed Batch Data Processing with C++. 2012.

[7]	Suhaimi, N., C. Nguyen, and P. Damodaran, Lagrangian approach to minmize makespan of non-identical parallel batch processing machines. Computers & Industrial Engineering 2016. 101: p. 295-302.

[8]	Zhou, S., et al., An effective discrete differential evolution algorithm for scheduling uniform parallel batch processing machines with non-identical capacities and arbitrary job sizes. Int. J. Production Economics, 2016. 179: p. 1-11.

[9]	Basnet, C., Heuristics for batching and sequencing in batch processing machines. Croatian Operational Research Review, 2016. 7: p. 291-302.

[10]	Koo, P.-H. and D.H. Moon, A Review On Control Strategies of Batch Processing Machines in Semiconductor Manufacturing. International Federation of Automatic Control, 2013: p. 1690-1695.

[11]	Salem, M.A., L. Bedoya-Valencia, and G. Rabadi, Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem. Mathematical Problems in Engineering, 2016: p. 1-11.

[12]	Dai, J.G. and C. Li, Stabilizing Batch-Processing Networks. Operations Research 2003. 51: p. 123-136.

[13]	Jana, A.K., A new divided-wall heat integrated distillation column (HIDiC) for batch processing : Feasibility and analysis. Applied Energy, 2016. 172: p. 199-206.

[14]	M, M. and A.I. Sivakumar, A literature review, classification and simple meta-analysis on schedulling of batch processors in semiconductor. International Journal Of Advanced Manufacturing Technology, 2006. 29: p. 990-1001.

[15]	https://www.youtube.com/watch?v=EgTWBHcxxZs&t=5s