Reinforced Manta Ray Foraging Optimiser for Determining the Optimal Number of Threads in Multithreaded Applications

Authors

Keywords:

Parallel programs, threads, optimisation, nature-inspired

Abstract

Thread management affects operating system factors, resulting in execution time delays. These factors may improve or degrade depending on the design of the program and the number of threads. Therefore, for any multithreaded application, changes in these factors indicate whether the selected thread count is appropriate or not. This paper proposes a method that combines manta ray foraging optimisation and the Thread-reinforcer algorithm. The new algorithm predicts the best thread count by using three manta ray foraging strategies: chain, cyclone, and somersault; however, it selects the thread count with the highest fitness value as the best solution. The fitness function computes the fitness value by analysing OS factors such as CPU utilisation, context switching rate, CPU migration rate, page fault rate, and execution time. The multithreaded applications are run multiple times with a small data size to collect values for these factors. We tested the proposed work on fifteen programs in the PARSEC 3.0 benchmark suit. The results show that the optimal thread count for seven programs is greater than the number of processors and equal to the number of processors for the remaining eight programs. This study also demonstrates that the proposed approach takes less time to determine the solution than the Thread-reinforcer.

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References

Navarro, Cristobal, et al. A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures. Com- munications in Computational Physics; 2013.

V.A. Chouliaras,T.R. Jacobs, J.L. Nu´n˜ez-Yanez, K. Manolopoulos, K. Nakos, D. Reisis. Thread-Parallel MPEG-2 and MPEG-4 Encoders for Shared-Memory System-On-Chip Multiprocessors. International Journal of Computers and Applications: Taylor & Francis; 2007. vol. 29. no. 4. p. 353–361.

S H Malave. Squid-SMP: Design & implementation of squid proxy server for the parallel platform. International Conference on Information Communication and Embedded Systems (ICICES2014); 2014. p. 1–6

Sajib Barua, Ruppa K. Thulasiram, Parimala Thulasiraman. High- Performance Computing for a Financial Application Using Fast Fourier Transform. Quality Technology & Quantitative Management: Taylor & Francis; 2014. vol 11. no, 1. p. 185–202

Ching-Kuang Shene. Multithreaded Programming Can Strengthen an Operating Systems Course. Computer Science Education: Routledge; 2002. vo. 12. no. 4. p. 275-299

Sethuraman S. Analysis of Fork-Join Systems. Network of Queues with Precedence Constraints (1st ed.) CRC Press; 2022.

Bhabani Shankar, Prasad Mishra, Satchidananda Dehuri. Parallel Com- puting Environments: A Review. IETE Technical Review: Taylor & Francis; 2011. vol. 28 no. 43 p. 240–247.

Mattson Tim. An introduction to openMP. Conference: Cluster Com- puting and the Grid, 2001. Proceedings. First IEEE/ACM International Symposium; 2001.

Randell, Brian. Operating Systems: The Problems Of Performance and Reliability. 1971. p. 281–290.

Torsten Hoefler,Timo Schneider,Andrew Lumsdaine. Accurately mea- suring overhead, communication time and progression of blocking and nonblocking collective operations at a massive scale. International Journal of Parallel, Emergent and Distributed Systems: Taylor & Francis; 2010. vol. 24. no. 4. p. 241–258.

M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. in IEEE Computational Intelligence Magazine; 2006. vol. 1. no. 4. p. 28–39.

Xin-She Yang, Particle Swarm Optimization. in IEEE Computational Intelligence Magazine. Academic Press; 2021. chapter 8. p. 111–121.

Weiguo Zhao, Zhenxing Zhang, Liying Wang. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering appli- cations. Engineering Applications of Artificial Intelligence: 2020. vol. 87.

Min-Yuan Cheng, Doddy Prayogo. Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures; 2014. vol 139. p. 98–112.

Xian-Bing Meng, X.Z. Gao, et al. A new bio-inspired optimisation al- gorithm: Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence: Taylor & Francis; 2016. vol. 28 no. 4. p. 673–687.

Sukhpal Singh Gill, Rajkumar Buyya. Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges. Big Data Analytics for Intelligent Healthcare Management, Academic Press; 2019. p. 1–17

C. Bienia, S. Kumar, J. P. Singh and K. Li. The PARSEC benchmark suite: Characterization and architectural implications. 2008 Interna- tional Conference on Parallel Architectures and Compilation Techniques (PACT): IEEE; 2008. p. 72–81.

Pusukuri Kishore Kumar, Gupta Rajiv, Bhuyan Laxmi N. Thread Reinforcer: Dynamically Determining Number of Threads via OS Level Monitoring. IEEE Computer Society: 2011.

Qin Henry, et al. Arachne: Core-aware thread management. 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 2018.

S. De Pestel, S. Van den Steen, S. Akram and L Eeckhout. RPPM: Rapid Performance Prediction of Multithreaded Workloads on Multicore Processors. 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS); 2019, p. 257–267.

Xin Wei, Liang Ma, Huizhen Zhang, Yong Liu. Multi-core multi- thread-based optimization algorithm for large-scale travelling salesman problem. Alexandria Engineering Journal; 2021. vol. 60. no 1. p. 189–197,

R. Nath, D. Tullsen. Accurately modelling GPGPU frequency scaling with the CRISP performance model. In Emerging Trends in Computer Science and Applied Computing, Advances in GPU Research and Prac- tice Morgan Kaufmann. 2017. chapter 18. p. 471-505.[22] R. Nath, D. Tullsen. Accurately modelling GPGPU frequency scaling with the CRISP performance model. In Emerging Trends in Computer Science and Applied Computing, Advances in GPU Research and Prac- tice Morgan Kaufmann. 2017. chapter 18. p. 471-505.

Streamcluster benchmark execution time with thread counts ranging from 10 to 24

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Published

27.12.2022

How to Cite

Malave, S. H. ., & Shinde, S. K. . (2022). Reinforced Manta Ray Foraging Optimiser for Determining the Optimal Number of Threads in Multithreaded Applications. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 17–26. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2406

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Research Article