Performance analysis of parallel master-slave Evolutionary strategies (μ,λ) model python implementation for CPU and GPGPU

Evolutionary strategies is a heuristic, guided-search based evolutionary algorithm, widely used as optimization technique for computationally intensive problems. Python is a high-level programming language known for code readability, reusability and the ease of use, making it preferable choice for q...

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Bibliographic Details
Published in2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) pp. 1609 - 1613
Main Authors Zubanovic, D., Hidic, A., Hajdarevic, A., Nosovic, N., Konjicija, S.
Format Conference Proceeding
LanguageEnglish
Published MIPRO 01.05.2014
Subjects
Acceleration
Computational modeling
GPU
Graphics processing units
Heuristic algorithms
Master-slave
Multithreading
Parallel computing
Python-Anaconda
Sociology
Statistics
Test functions for optimization
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DOI10.1109/MIPRO.2014.6859822

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Summary:Evolutionary strategies is a heuristic, guided-search based evolutionary algorithm, widely used as optimization technique for computationally intensive problems. Python is a high-level programming language known for code readability, reusability and the ease of use, making it preferable choice for quick and robust software development, although it is lacking in performance and concurrency area. Emerging technologies such as Anaconda Accelerate Python compiler attempt to combine Python's ease of use with both declarative and explicit parallelization and high performance in computationally intensive problems. In this paper an example of master - slave parallel Evolutionary strategy ES(μ,λ) implementation in Python is given, and its performance on CPU and GPU are analyzed.
DOI:10.1109/MIPRO.2014.6859822