Scalability of GPA-ES Algorithm
This contribution is dedicated to analysis of GPA-ES scalability both from analytical and experimental viewpoint. The paper tries to identify limits of the algorithm, sources of possible limitations and practical ways of its application. The paper is focused to standard implementation of GPA-ES algorithm and tests were provided on task of symbolical regression of Lorenz attractor system. After introduction onto the problem, Amdahl’s law is discussed in Sect. 2, characterization of GPA-ES algorithm in Sect. 3, analysis if its computational complexity in Sect. 4, experiment results analysis to determine computing time dependency on number of threads both HW and SW in Sect. 5 and analysis of influence of population size in Sect. 6. Presented results underlines that except one anomaly all experiments concludes applicability of Amdahl’s law onto GPA-ES algorithm even if it is implemented on multi-core and many-core systems, which are out of original scope of this law.
KeywordsGenetic programming algorithm Efficiency Scalability Parallel algorithm Amdahl’s law
Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”.
Computational resources were supplied by the Ministry of Education, Youth and Sports of the Czech Republic under the Projects CESNET (Project No. LM2015042) and CERIT-Scientific Cloud (Project No. LM2015085) provided within the program Projects of Large Research, Development and Innovations Infrastructures.
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