Desynchronization of simulation and optimization algorithms in HPC environment

• Autorzy: Starzec Mateusz , Starzec Grażyna , Paciorek Mateusz

Identyfikatory

DOI

10.7494/csci.2020.21.3.3772

• Języki publikacji: EN

Abstrakty

EN

The need for the scalability of an algorithm is essential when one wants to utilize an HPC infrastructure in an efficient and reasonable way. In such infrastructures, synchronization affects the efficiency of the parallel algorithms. However, one can consider introducing certain means of desynchronization in order to increase the scalability. Allowing certain messages to be omitted or delayed can be easily accepted in the case of metaheuristics. Furthermore, some simulations can also follow this pattern and thereby handle bigger environments. The paper presents a short survey on the desynchronization idea, pointing out already obtained results, or sketching out future work focused on scaling the parallel and distributed computing or simulation algorithms.

Słowa kluczowe

EN

scalability; desynchronization; simulations; optimization algorithms

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2020
• Tom: T. 21 (3)
• Strony: 319–333
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Starzec Mateusz

• AGH University of Science and Technology, Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Starzec Grażyna

• AGH University of Science and Technology, Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Paciorek Mateusz

• AGH University of Science and Technology, Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

• [1] Atashpendar A., Dorronsoro B., Danoy G., Bouvry P.: A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization, Journal of Parallel and Distributed Computing, vol. 112, pp. 111–125, 2018.
• [2] Bujas J., Dworak D., Turek W., Byrski A.: High-performance computing framework with desynchronized information propagation for large-scale simulations. Journal of Computational Science, vol. 32, pp. 70–86, 2019.
• [3] Byrski A., Dreżewski R., Siwik L., Kisiel-Dorohinicki M.: Evolutionary multiagent systems, The Knowledge Engineering Review, vol. 30(2), pp. 171–186, 2015.
• [4] Conway J.: The game of life, Scientific American, vol. 223(4), p. 4, 1970.
• [5] Dorigo M.: Optimization, learning and natural algorithms, PhD Thesis, Politecnico di Milano, 1992.
• [6] Ellis M., Pezaros D.P., Kypraios T., Perkins C.: A two-level Markov model for packet loss in UDP/IP-based real-time video applications targeting residential users, Computer Networks, vol. 70, pp. 384–399, 2014, https://doi.org/10.1016/ j.comnet.2014.05.013.
• [7] Grakova E., Slaninova K., Martinovic J., Krenek J., Hanzelka J., Svaton V.: Waste Collection Vehicle Routing Problem on HPC Infrastructure. In: IFIP International Conference on Computer Information Systems and Industrial Management, pp. 266–278, Springer, 2018.
• [8] Ilie S., Badica C.: Multi-agent approach to distributed ant colony optimization, Science of Computer Programming, vol. 78(6), pp. 762–774, 2013.
• [9] Kennedy J., Eberhart R.: Particle Swarm Optimization. In: Proceedings of ICNN’95 – International Conference on Neural Networks, vol. 4, pp. 1942–1948, IEEE, 1995.
• [10] McCool M., Robinson A., Reinders M.: Structured Parallel Programming: Patterns for Efficient Computation, Elsevier, 2013.
• [11] Nagano K., Collins T., Chen C.A., Nakano A.: Massively parallel inverse rendering using Multi-objective Particle Swarm Optimization, Journal of Visualization, vol. 20(2), pp. 195–204, 2017.
• [12] Paciorek M., Bujas J., Dworak D., Turek W., Byrski A.: Validation of signal propagation modeling for highly scalable simulations, Concurrency and Computation: Practice and Experience, p. e5718.
• [13] Skiba G., Starzec M., Byrski A., Rycerz K., Kisiel-Dorohinicki M., Turek W., Krzywicki D., Lenaerts T., Burguillo J.C.: Flexible asynchronous simulation of iterated prisoner’s dilemma based on actor model, Simulation Modelling Practice and Theory, vol. 83, pp. 75–92, 2018.
• [14] Ślaski M., Turek W., Gil A., Szafran B., Paciorek M., Byrski A.: Analysis of Distributed Systems Dynamics with Erlang Performance Lab, Computer Science, vol. 19, 2018.
• [15] Starzec G., Starzec M., Byrski A., Kisiel-Dorohinicki M., Burguillo J.C., Lenaerts T.: Towards Large-Scale Optimization of Iterated Prisoner Dilemma Strategies. In: Transactions on Computational Collective Intelligence XXXII, pp. 167–183, Springer, 2019.
• [16] Starzec M., Starzec G., Byrski A., Turek W.: Distributed ant colony optimization based on actor model, Parallel Computing, vol. 90, p. 102573.
• [17] Starzec M., Starzec G., Byrski A., Turek W., Kisiel-Dorohinicki M.: Distributed ant system for difficult transport problems, Journal of Intelligent & Fuzzy Systems, vol. 37(6), pp. 7347–7356.
• [18] Starzec M., Starzec G., Byrski A., Turek W., Piętak K.: Desynchronization in distributed Ant Colony Optimization in HPC environment, Future Generation Computer Systems, 2020.
• [19] Xie X.F., Zhang W.J., Yang Z.L.: Social cognitive optimization for nonlinear programming problems. In: Proceedings. International Conference on Machine Learning and Cybernetics, vol. 2, pp. 779–783, IEEE, 2002.