PARALLEL PROCESSING PROBLEM AND SOLUTION - A CASE STUDY ON MATLAB PARALLEL COMPUTING TOOLBOX DISTRIBUTED ARRAYS Wan Asywad bin Zainal Abidin #1, Mohamed Faidz Mohamed Said #2 # Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA 70300 Seremban, Negeri Sembilan, MALAYSIA 1 asywadzainal@gmail.com 2 faidzms@ieee.org Abstract—MATLAB is a software that provides a programming language used for calculations of the matrix, developing and running algorithms, creating user interfaces and visualisation. It is a matrix laboratory for solving many technical computing problems with its applications system. Its matrix laboratory is used to solve many technical computing problems with its applications system. The purpose of this research is to provide understanding about the Parallel Computing Toolbox (PCT) in MATLAB. Moreover, there are several related benefits of using parallel processing involving distributed arrays. During a large data processing, a great amount of computing power is required to process the data. The PCT is applied to present more solutions that distributed arrays can trade-off to easily provide the required performance of large data by using the toolbox provided. Finally, MATLAB parallel computing provides the needed solution for any given problem without taking extra time and cost. Thus, the uses of parallel processing or parallel computing have become one of the prevalent common ways to help users in solving many application problems nowadays. Keyword: MATLAB, distributed arrays, Parallel Computing Toolbox (PCT) REFERENCES [1] Wan Asywad Zainal Abidin. "170522 CSC580 WAZA", 2017. [Online]. Available: https://www.youtube.com/watch?v=LpzLX51gPH4 [Accessed : 19-Jun-2017]. [2] J. Dongarra, B. Tourancheau, and P. Luszczek, "Parallel Programming in MATLAB," The International Journal of High Performance Computing Applications, vol. 23, no. 3, pp. 277-283, 2009/08/01 2009. [3] J.-Y. Cho, H.-W. Jin, M. Lee, and K. Schwan, "Dynamic Core Affinity for High-Performance File Upload on Hadoop Distributed File System," Parallel Computing, vol. 40, no. 10, pp. 722-737, 2014. [4] C. Grothoff, J. Palsberg, and V. Saraswat, "A Type System for Distributed Arrays," Dept. of Computer Science University, IBM T.J. Watson Research Center, UCLA Computer Science Dept., 2007. [5] P. Miller, A. Becker, and L. Kalé, "Using Shared Arrays in Message-Driven Parallel Programs," Parallel Computing, vol. 38, no. 1-2, pp. 66-74, 2012. [6] J. Iverson, C. Kamath, and G. Karypis, "Evaluation of Connected-Component Labeling Algorithms for Distributed-Memory Systems," Parallel Computing, vol. 44, pp. 53-68, 2015. [7] D. Houcque, "Introduction to Matlab for Engineering Students," p. 74, n.d. 2005. [8] C. Wang, C. Yu, S. Tang, J. Xiao, J. Sun, and X. Meng, "A General and Fast Distributed System for Large-Scale Dynamic Programming Applications," Parallel Computing, vol. 60, pp. 1-21, 2016. [9] D. Khaldi, P. Jouvelot, and C. Ancourt, "Parallelizing with Bdsc, A Resource-Constrained Scheduling Algorithm for Shared and Distributed Memory Systems," Parallel Computing, vol. 41, pp. 66-89, 2015. [10] R. D. I. Keller, D. A. Kramer, and J.-P. Weiss, Facing The Multicore-Challenge III, Berlin ;: Springer, 2013. [Online]. Available: SpringerLink http://dx.doi.org/10.1007/978-3-642-35893-7. [11] R. Ayari et al., "Multi-Objective Mapping of Full-Mission Simulators on Heterogeneous Distributed Multi-Processor Systems," Journal of Defense Modeling and Simulation: Application, pp. 1–12, 2016. [12] G. S. Choi and C. R. Das, "A Superscalar Software Architecture Model for Multi-Core Processors (Mcps)," Journal of Systems and Software, vol. 83, no. 10, pp. 1823-1837, 2010. [13] David Jenn, Yong Loke, Tong Chin Hong Matthew, Yeo Eng Choon, O. C. Siang, and Y. S. Yam, "Distributed Phased Arrays and Wireless Beamforming Networks," International Journal of Distributed Sensor Networks, vol. 5, pp. 283–302, 2009. [14] S. Varoglu and S. Jenks, "Architectural Support for Thread Communications in Multi-Core Processors," Parallel Computing, vol. 37, no. 1, pp. 26-41, 2011.