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GPU Accelerated Adversarial Search
Brehovský, Martin ; Bošanský, Branislav (advisor) ; Bída, Michal (referee)
General purpose graphical processing units were proven to be useful for accelerating computationally intensive algorithms. Their capability to perform massive parallel computing significantly improve performance of many algorithms. This thesis focuses on using graphical processors (GPUs) to accelerate algorithms based on adversarial search. We investigate whether or not the adversarial algorithms are suitable for single instruction multiple data (SIMD) type of parallelism, which GPU provides. Therefore, parallel versions of selected algorithms accelerated by GPU were implemented and compared with the algorithms running on CPU. Obtained results show significant speed improvement and proof the applicability of GPU technology in the domain of adversarial search algorithms.
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Artificial Intelligence for Go on Non-standard Topologies
Červeň, Martin ; Gemrot, Jakub (advisor) ; Brunetto, Robert (referee)
Go is a popular strategic game for two players. It is usually played on a squared board of 19x19. The aim of this thesis is to create an application allowing a user to play Go on any board defined by a graph, such as sphere and torus. We created a web based client-server application, written in JavaScript and Node.js, that is using protocol Websockets for fast communication. Application allows user to play against other players on the Internet. Server can support multiple concurrent games at the same time. Boards can be created by modelling tool Blender and then imported into the application. Our application supports 3D rendering of these boards in web client using WebGL. It has responsive control, allowing changes of view by rotating, moving and zooming. Users can also play against artificial intelligence. 1
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GPU Accelerated Adversarial Search
Brehovský, Martin ; Bošanský, Branislav (advisor) ; Bída, Michal (referee)
General purpose graphical processing units were proven to be useful for accelerating computationally intensive algorithms. Their capability to perform massive parallel computing significantly improve performance of many algorithms. This thesis focuses on using graphical processors (GPUs) to accelerate algorithms based on adversarial search. We investigate whether or not the adversarial algorithms are suitable for single instruction multiple data (SIMD) type of parallelism, which GPU provides. Therefore, parallel versions of selected algorithms accelerated by GPU were implemented and compared with the algorithms running on CPU. Obtained results show significant speed improvement and proof the applicability of GPU technology in the domain of adversarial search algorithms.
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