Analysis and optimization of the sizes of the iteration space tiles during the parallelization of program loop operators

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dc.citation.epage6
dc.citation.issue1
dc.citation.spage1
dc.contributor.affiliationPukhov Institute for Modeling in Energy Engineering
dc.contributor.authorChemeris, Alexander
dc.contributor.authorSushko, Sergii
dc.coverage.placenameЛьвів
dc.date.accessioned2020-02-18T13:14:51Z
dc.date.available2020-02-18T13:14:51Z
dc.date.created2018-02-01
dc.date.issued2018-02-01
dc.description.abstractThe analysis of the dependency of influence of the tile sizes of iteration space has been represented. It involves program loop operators’ modification during parallelization for multithreading architectures of the computation systems. The particle swarm optimization method has been considered as a method of the minimization program execution time for tiling speed up.
dc.format.extent1-6
dc.format.pages6
dc.identifier.citationChemeris A. Analysis and optimization of the sizes of the iteration space tiles during the parallelization of program loop operators / Alexander Chemeris, Sergii Sushko // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2018. — Vol 3. — No 1. — P. 1–6.
dc.identifier.citationenChemeris A. Analysis and optimization of the sizes of the iteration space tiles during the parallelization of program loop operators / Alexander Chemeris, Sergii Sushko // Advances in Cyber-Physical Systems. — Lviv Politechnic Publishing House, 2018. — Vol 3. — No 1. — P. 1–6.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45676
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 1 (3), 2018
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dc.relation.references2. P. Feautrier and C. Lengauer, “The Polyhedron Model” in Encyclopedia of Parallel Computing, Springer-Verlag, 2011, pp. pp. 1581-1592.
dc.relation.references3. Xue, J. Loop Tiling for Parallelism. Kluwer Academic Publishers. 2000.-p. 256.
dc.relation.references4. S. Sushko, O. Chemerys “Influence of the tiles’ sizes operations on the computer program execution time”, Modeling and informational technologies. vol. 82, pp. 110-117, 2018, (in Ukrainian).
dc.relation.references5. L.- N. Pouchet, (2018, Dec) The polyhedral benchmark suite [Online]. Available: http://web.cse.ohio-state.edu/pouchet.2/software/polybench/.
dc.relation.references6. V. Kureichik, A. Kazharov “Using a swarm intellect in solving NP-problems” News of YUFU. Technical sciences. - No. 7 (120). pp. 30-37. 2011, (in Russian).
dc.relation.references7. A. Filipova, E.Diaminova, E.Andreeva, E.Laptenok Matrix particle swarm algorithm for support adoption of decision for scheduling service teams. Proceedings of Sixth All-Russian scientific conference “Informational technology of intellectual support of decision adoption”, Ufa-Stavropol’, pp. 125-128, 2018, (in Russian).
dc.relation.references8. B. Lebedev, V. Lebedev Splitting based on swarm intelligence and genetic evolution. Proceedings of V International Scientific and Practical Conference Integrated Models and Soft Computing in Artificial Intelligence, Kolomna, Fizmatlit, 2009, (in Russian).
dc.relation.references9. J. McConnel, Fundamentals of modern algorithms. Moscow Technosfera, 2004, (in Russian).
dc.relation.references10. Engelbrecht A. P. Fundamentals of Computational Swarm Intelligence. Chichester, UK, John Wiley & Sons, 2005.
dc.relation.references11. V. Kureichik, A. Kazharov Algorithms of evolutionary swarm intelligence in solving problem of partitioning of graph. Taganrog. Ed. TRTU, 2012, (in Russian).
dc.relation.references12. G. Samigulina, Zh. Masimkanova “Review of modern methods of swarm intelligence for computer molecular design of drugs”, Computer science problems, No. 2 (31), - pp. 50-61, 2016, (in Russian).
dc.relation.references13. Clerc M. Particle Swarm Optimization. ISTE, London, UK, 2006.
dc.relation.references14. P. Matrenin, V. Sekaev “System description of the swarm intelligence algorithms”, Software Engineering, Theoretical and Applied Scientific and Technical Journal, ISSN 2220-3397, vol. 12, pp. 39^5, 2013, (in Russian).
dc.relation.references15. I. Kovalev, E. Soloviev, D. Kovalev, K. Bahmareva, A. Demish. “Using the particle swarm method to form a multiversion software composition”, Instruments and systems. Management, control, diagnostics, Moscow, Nauchtechlitizdat, ISSN: 2073-0004, No. 3, - pp. 1-6, 2013, (in Russian).
dc.relation.references16. G. Beni, J. Wang, “Swarm Intelligence in Cellular Robotic Systems,” Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26-30, 1989.
dc.relation.references17. A. Karpenko, E. Selivestrov, Overview of the particle swarm methods for the global optimization problem, Scientic ed. of MGTU Baumana “Science and education”, GFBOU VPO "MGTU of Bauman". ISSN 1994-0408. No. 3, 2009, (in Russian).
dc.relation.referencesen1. A. Aho, M. Lam, R. Sethi, J. Ullman Compilers: Principles, Techniques and Tools. 2nd ed. Moscow "Williams", p. 1184, ISBN 978-5-8459-1349-4, 2008.
dc.relation.referencesen2. P. Feautrier and C. Lengauer, "The Polyhedron Model" in Encyclopedia of Parallel Computing, Springer-Verlag, 2011, pp. pp. 1581-1592.
dc.relation.referencesen3. Xue, J. Loop Tiling for Parallelism. Kluwer Academic Publishers. 2000.-p. 256.
dc.relation.referencesen4. S. Sushko, O. Chemerys "Influence of the tiles’ sizes operations on the computer program execution time", Modeling and informational technologies. vol. 82, pp. 110-117, 2018, (in Ukrainian).
dc.relation.referencesen5. L, N. Pouchet, (2018, Dec) The polyhedral benchmark suite [Online]. Available: http://web.cse.ohio-state.edu/pouchet.2/software/polybench/.
dc.relation.referencesen6. V. Kureichik, A. Kazharov "Using a swarm intellect in solving NP-problems" News of YUFU. Technical sciences, No. 7 (120). pp. 30-37. 2011, (in Russian).
dc.relation.referencesen7. A. Filipova, E.Diaminova, E.Andreeva, E.Laptenok Matrix particle swarm algorithm for support adoption of decision for scheduling service teams. Proceedings of Sixth All-Russian scientific conference "Informational technology of intellectual support of decision adoption", Ufa-Stavropol’, pp. 125-128, 2018, (in Russian).
dc.relation.referencesen8. B. Lebedev, V. Lebedev Splitting based on swarm intelligence and genetic evolution. Proceedings of V International Scientific and Practical Conference Integrated Models and Soft Computing in Artificial Intelligence, Kolomna, Fizmatlit, 2009, (in Russian).
dc.relation.referencesen9. J. McConnel, Fundamentals of modern algorithms. Moscow Technosfera, 2004, (in Russian).
dc.relation.referencesen10. Engelbrecht A. P. Fundamentals of Computational Swarm Intelligence. Chichester, UK, John Wiley & Sons, 2005.
dc.relation.referencesen11. V. Kureichik, A. Kazharov Algorithms of evolutionary swarm intelligence in solving problem of partitioning of graph. Taganrog. Ed. TRTU, 2012, (in Russian).
dc.relation.referencesen12. G. Samigulina, Zh. Masimkanova "Review of modern methods of swarm intelligence for computer molecular design of drugs", Computer science problems, No. 2 (31), pp. 50-61, 2016, (in Russian).
dc.relation.referencesen13. Clerc M. Particle Swarm Optimization. ISTE, London, UK, 2006.
dc.relation.referencesen14. P. Matrenin, V. Sekaev "System description of the swarm intelligence algorithms", Software Engineering, Theoretical and Applied Scientific and Technical Journal, ISSN 2220-3397, vol. 12, pp. 39^5, 2013, (in Russian).
dc.relation.referencesen15. I. Kovalev, E. Soloviev, D. Kovalev, K. Bahmareva, A. Demish. "Using the particle swarm method to form a multiversion software composition", Instruments and systems. Management, control, diagnostics, Moscow, Nauchtechlitizdat, ISSN: 2073-0004, No. 3, pp. 1-6, 2013, (in Russian).
dc.relation.referencesen16. G. Beni, J. Wang, "Swarm Intelligence in Cellular Robotic Systems," Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26-30, 1989.
dc.relation.referencesen17. A. Karpenko, E. Selivestrov, Overview of the particle swarm methods for the global optimization problem, Scientic ed. of MGTU Baumana "Science and education", GFBOU VPO "MGTU of Bauman". ISSN 1994-0408. No. 3, 2009, (in Russian).
dc.relation.urihttp://web.cse.ohio-state.edu/pouchet.2/software/polybench/
dc.rights.holder© Національний університет “Львівська політехніка”, 2018
dc.rights.holder© Chemeris A., Sushko S., 2018
dc.subjectsoftware optimization
dc.subjectloop parallelization
dc.subjecttiling
dc.subjectparticle swarm optimization
dc.titleAnalysis and optimization of the sizes of the iteration space tiles during the parallelization of program loop operators
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2018. – Vol. 3, No. 1