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Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
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Particle Swarm Optimization on GPUs
Záň, Drahoslav ; Petrlík, Jiří (referee) ; Jaroš, Jiří (advisor)
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.
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A Comparison of Somatotype between Female Rugby Union Forwards and Backs
Vacková, Petra ; Vokounová, Šárka (advisor) ; Kinkorová, Ivana (referee)
Title: A Comparison of Somatotype between Female Rugby Union Forwards and Backs Objectives: To compare differences and similarities of somatotype between female forwards and backs of the Czech Rugby Union. Method: A total number of 30 elite female rugby players participated in this study. Somatic measurements necessary to specify one's somatotype and Heath- Carter method were used on all subjects. All collected data was analysed in Microsoft Excel spreadsheets with results demonstrated using bar charts, tables and somatographs. Results: No significant differences were found between selected groups. Mesomorphy was found to have the highest value in both groups followed by the endomorphic and ectomorphic component. The endomorphic and mesomorphic component was found to be higher in forwards, however the value of ectomorphy was slightly higher in backs. In comparison with research of Welsh female rugby players, mean somatotype of this study was more comparable to the elite group of players. Keywords: somatotype, Rugby Union, forwards, backs
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Particle Swarm Optimization on GPUs
Záň, Drahoslav ; Petrlík, Jiří (referee) ; Jaroš, Jiří (advisor)
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.
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|
|
Acceleration of Particle Swarm Optimization Using GPUs
Krézek, Vladimír ; Schwarz, Josef (referee) ; Jaroš, Jiří (advisor)
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
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