Abstract
In high-performance computing, choosing the right thread count has a big impact on execution time and energy consumption. It is typically considered that the total number of threads should equal the number of cores to achieve maximum speedup on multicore processor systems. Changes in thread count at the hardware and OS levels influence memory bandwidth utilization, thread migration rate, cache miss rate, thread synchronization, and context switching rate. As a result, analyzing these parameters for complex multithreaded applications and finding the optimal number of threads is a major challenge. The suggested technique in this paper is an improvement on the traditional Manta Ray Foraging Optimization, a bio-inspired algorithm that has been used to handle a variety of numerical optimization problems. To determine the next probable solutions based on the present best solution, the suggested approach uses three foraging steps: chain, cyclone, and somersault. The proposed work is simulated on NVIDIA-DGX Intel Xeon-E5 2698-v4 using the well-known benchmark suite The Princeton Application Repository for Shared Memory Computers (PARSEC). The results show that, compared to the existing approach, the novel AMRFO-based prediction model can determine the ideal number of threads with very low overheads.
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References
Hossein S, Homayoun H (2017) Scheduling multithreaded applications onto heterogeneous composite cores architecture. In: 2017 Eighth international green and sustainable computing conference (IGSC). IEEE
Rinard M (2001) Analysis of multithreaded programs. Int Static Anal Symp 2126:1–19
De Supinski BR, Scogland TRW, Duran A, Klemm M, Bellido SM, Olivier SL, Terboven C, Mattson TG (2018) The ongoing evolution of openmp. In: Proceedings of the IEEE
Langmead B, Wilks C, Antonescu V, Charles R (2019) Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics 421–432
Nagasakaa Y, Matsuoka S, Azad A, Buluc A (2019) Performance optimisation, modeling and analysis of sparse matrix-matrix products on multi-core and many-core processors. Parallel Comput
Moore RW, Childers BR, Xue J (2015) Performance modeling of multithreaded programs for mobile asymmetric chip multiprocessors. In: 2015 IEEE 17th international conference on high performance computing and communications, pp 957–963
Weiguo Z, Zhang Z, Wang L (2014) Manta ray foraging optimisation: an effective bio-inspired optimiser for engineering applications. Eng Appl Artif Intell
Mohammad H, Swaleha Z (2021) Mantaray modified multi-objective Harris hawk optimisation algorithm expedites optimal load balancing in cloud computing. J King Saud Univ Comput Inf Sci
Nora A, Hanan T, Halawani SM, Abdelkhalek A, Laxmi L (2022) Manta ray foraging optimisation with vector quantization based microarray image compression technique. Intell Neurosci
Lakshmi N, Krishnamurthy M (2022) Association rule mining based fuzzy manta ray foraging optimisation algorithm for frequent itemset generation from social media. Concurrency Computat Pract Exper
Saleh A, Omran WA, Hasanien HM, Tostado-Véliz M, Alkuhayli A, Jurado F (2022) Manta Ray foraging optimisation for the virtual inertia control of islanded microgrids including renewable energy sources. Sustainability
Min-Yuan C, Doddy P (2014) Symbiotic organisms search: a new metaheuristic optimisation algorithm. Comput Struct 139
Mohammed A, Ngadi MA, Shafi’i Muhammad A (2016) Symbiotic organism search optimisation based task scheduling in cloud computing environment. Future Gener Comput Syst 56:640–650
Dorigo M, Stützle T (2019) Ant colony optimisation: overview and recent advances. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics. In: International series in operations research & management science, vol 272. Springer, Cham
Patricia G, Osorio RR, Pardo XC, Banga JR, Ramón D (2022) An efficient ant colony optimisation framework for HPC environments. Appl Soft Comput 114
Chauhan R, Sharma N, Sharma H (2022) An ant system algorithm based on dynamic pheromone evaporation rate for solving 0/1 knapsack problem. In: Saraswat M, Sharma H, Balachandran K, Kim JH, Bansal JC (eds) Congress on intelligent systems. Lecture notes on data engineering and communications technologies, vol 114. Springer, Singapore
Idris H, Ezugwu AJ, Sahalu A, Aderemi A (2017) An improved ant colony optimisation algorithm with fault tolerance for job scheduling in grid computing systems
Kassab A, Nicod J, Philippe L, Rehn-Sonigo V (2018) Assessing the use of genetic algorithms to schedule independent tasks under power constraints. In: 2018 International conference on high performance computing & simulation (HPCS), pp 252–259
Kennedy J, Eberhart R (1995) Particle swarm optimisation. In: Proceedings of ICNN'95—international conference on neural networks, vol 4, pp 1942–1948
Gupta S, Kumari R, Kumar S (2022) Limaco̧n inspired particle swarm optimisation for large-scale optimisation problem. In: Saraswat M, Sharma H, Balachandran K, Kim JH, Bansal JC (eds) Congress on intelligent systems. Lecture notes on data engineering and communications technologies, vol 111. Springer, Singapore
Kaushik R, Singh V, Kumari R (2021) A review of nature-inspired algorithm-based multi-objective routing protocols. In: Sharma H, Saraswat M, Kumar S, Bansal JC (eds) Intelligent learning for computer vision. CIS 2020. Lecture notes on data engineering and communications technologies, vol 61. Springer, Singapore
Solmaz S, Lu L (201) Memory bandwidth prediction for HPC applications in NUMA architecture. In: IEEE 5th international conference on data science and systems (HPCC/SmartCity/DSS), pp 1115–1122
Pestel SD, Den Steen SV, Akram S, Eeckhout L (2018) Rppm: rapid performance prediction of multithreaded applications on multicore hardware. In: IEEE international symposium on performance analysis of systems and software (ISPASS), pp 183–186
Agarwal N, Jain T, Zahran M (2019) Performance prediction for multi-threaded applications. In: International workshop on AI-assisted design for architecture
Tao J, Zhang Y, Zhang X, Du X, Dong X (2019) Energy-efficient thread mapping for heterogeneous many-core systems via dynamically adjusting the thread count
Jihyun P, Choi B, Jang S (2020) Dynamic analysis method for concurrency bugs in multi-process/multi-thread environments. Int J Parallel Prog 48:1032–1060
Xin W, Ma L, Zhang H, And Liu Y (2021) Multi-core-, multi-thread-based optimisation algorithm for large-scale traveling salesman problem. Alexandria Eng J 60:189–197
Demetrios C, Georgiou, K (2020) Performance and energy trade-offs for parallel applications on heterogeneous multi-processing systems. In: IFIP/IEEE 27th international conference on very large scale integration (VLSI-SoC), pp 232–233
Bienia C, Kumar S, Singh JP, In KL (2018) The parsec benchmark suite: characterization and architectural implications. In: Proceedings of the 17th international conference on parallel architectures and compilation techniques
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Malave, S.H., Shinde, S.K. (2023). Adaptive Manta Ray Foraging Optimizer for Determining Optimal Thread Count on Many-core Architecture. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 613. Springer, Singapore. https://doi.org/10.1007/978-981-19-9379-4_17
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