Information
- Publication Type: Bachelor Thesis
- Workgroup(s)/Project(s):
- Date: September 2015
- First Supervisor: Michael Wimmer
Abstract
With modern processing hardware converging on the physical barrier in terms of transistor size and speed per single core, hardware manufacturers have shifted their focus to improve performance from raw clock power towards parallelization. Solutions to utilize the computation power of GPUs are published and supported by graphics card manufacturers. While there exist solutions for arbitrary precision integer arithmetics on the CPU there has been little adoption of these libraries to the GPU. This thesis presents an approach to map arbitrary precision integer operations to single threads on the GPU. This novel computation mapping technique is benchmarked and compared to a library that runs these computations on the CPU. Furthermore the novel parallelization technique is compared to an alternative mapping scheme proposed by Langer et al [Lan15]. It is shown that mapping computations to single threads outperforms both the CPU and the approach by Langer. This thesis also explored the feasibility of rational number operations on the GPU and shows that this is in fact practically usable by providing benchmarks.Additional Files and Images
Weblinks
No further information available.BibTeX
@bachelorsthesis{Gusenbauer_Matthias_2015_ANM, title = "A Novel Mapping of Arbitrary Precision Integer Operations to the GPU", author = "Matthias Gusenbauer", year = "2015", abstract = "With modern processing hardware converging on the physical barrier in terms of transistor size and speed per single core, hardware manufacturers have shifted their focus to improve performance from raw clock power towards parallelization. Solutions to utilize the computation power of GPUs are published and supported by graphics card manufacturers. While there exist solutions for arbitrary precision integer arithmetics on the CPU there has been little adoption of these libraries to the GPU. This thesis presents an approach to map arbitrary precision integer operations to single threads on the GPU. This novel computation mapping technique is benchmarked and compared to a library that runs these computations on the CPU. Furthermore the novel parallelization technique is compared to an alternative mapping scheme proposed by Langer et al [Lan15]. It is shown that mapping computations to single threads outperforms both the CPU and the approach by Langer. This thesis also explored the feasibility of rational number operations on the GPU and shows that this is in fact practically usable by providing benchmarks.", month = sep, address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", URL = "https://www.cg.tuwien.ac.at/research/publications/2015/Gusenbauer_Matthias_2015_ANM/", }