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Parallel computing solutions to the Balitsky-Kovchegov equation
The JIMWLK (Jalilian-Marian, Iancu, McLerran,Weigert, Leonodiv and Kovner; pronounced "gym walk") equation describes the energy evolution of observables in the colour glass condensate (CGC) state of matter, which is particularly relevant to collider physics. Currently there are many implementations...
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| Language: | English |
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Department of Physics
2016
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| _version_ | 1867614506051436544 |
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| access_status_str | Open Access |
| author | Hillebrand-Viljoen, Charlotte Stephanie |
| author2 | Weigert, Weigert, Heribert |
| author_browse | Hillebrand-Viljoen, Charlotte Stephanie Weigert, Weigert, Heribert |
| author_facet | Weigert, Weigert, Heribert Hillebrand-Viljoen, Charlotte Stephanie |
| author_sort | Hillebrand-Viljoen, Charlotte Stephanie |
| collection | Thesis |
| description | The JIMWLK (Jalilian-Marian, Iancu, McLerran,Weigert, Leonodiv and Kovner; pronounced "gym walk") equation describes the energy evolution of observables in the colour glass condensate (CGC) state of matter, which is particularly relevant to collider physics. Currently there are many implementations of JIMWLK evolution in the spirit of the factorised Balitsky-Kovchegov (BK) equation for the total cross section, including a number of efforts to consistently implement evolution at next-to-leading-order[1-5]. Aside from NLO there is a growing interest in studying new, more exclusive, observables, such as single transverse spin asymmetries and transverse momentum distributions[6-8]. These require the inclusion of new degrees of freedom, which can be done systematically by extending the Gaussian truncation of the JIMWLK equation[9][10]. This necessarily increases the computational demands, both in terms of floating point operations and of storage requirements. After a discussion of the theoretical context, we address the first computational step and introduce new, parallelised methods in code that evolves the BK equation. Parallelisation of BK evolution using NVIDIA CUDA with implementation on a commercially available graphical processing unit (GPU) results in performance improvements of roughly an order of magnitude over comparable serial programmes. This also allows us to implement test cases which are often neglected. The code presented here covers only the total cross section case, but it is written with extension to more interesting cases in mind and we discuss some such potential applications. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/20656 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:53:07.369Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Department of Physics |
| publisherStr | Department of Physics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/20656 Parallel computing solutions to the Balitsky-Kovchegov equation Hillebrand-Viljoen, Charlotte Stephanie Weigert, Weigert, Heribert Physics The JIMWLK (Jalilian-Marian, Iancu, McLerran,Weigert, Leonodiv and Kovner; pronounced "gym walk") equation describes the energy evolution of observables in the colour glass condensate (CGC) state of matter, which is particularly relevant to collider physics. Currently there are many implementations of JIMWLK evolution in the spirit of the factorised Balitsky-Kovchegov (BK) equation for the total cross section, including a number of efforts to consistently implement evolution at next-to-leading-order[1-5]. Aside from NLO there is a growing interest in studying new, more exclusive, observables, such as single transverse spin asymmetries and transverse momentum distributions[6-8]. These require the inclusion of new degrees of freedom, which can be done systematically by extending the Gaussian truncation of the JIMWLK equation[9][10]. This necessarily increases the computational demands, both in terms of floating point operations and of storage requirements. After a discussion of the theoretical context, we address the first computational step and introduce new, parallelised methods in code that evolves the BK equation. Parallelisation of BK evolution using NVIDIA CUDA with implementation on a commercially available graphical processing unit (GPU) results in performance improvements of roughly an order of magnitude over comparable serial programmes. This also allows us to implement test cases which are often neglected. The code presented here covers only the total cross section case, but it is written with extension to more interesting cases in mind and we discuss some such potential applications. 2016-07-25T07:14:55Z 2016-07-25T07:14:55Z 2016 Master Thesis Masters MSc http://hdl.handle.net/11427/20656 eng application/pdf Department of Physics Faculty of Science University of Cape Town |
| spellingShingle | Physics Hillebrand-Viljoen, Charlotte Stephanie Parallel computing solutions to the Balitsky-Kovchegov equation |
| thesis_degree_str | Master's |
| title | Parallel computing solutions to the Balitsky-Kovchegov equation |
| title_full | Parallel computing solutions to the Balitsky-Kovchegov equation |
| title_fullStr | Parallel computing solutions to the Balitsky-Kovchegov equation |
| title_full_unstemmed | Parallel computing solutions to the Balitsky-Kovchegov equation |
| title_short | Parallel computing solutions to the Balitsky-Kovchegov equation |
| title_sort | parallel computing solutions to the balitsky kovchegov equation |
| topic | Physics |
| url | http://hdl.handle.net/11427/20656 |
| work_keys_str_mv | AT hillebrandviljoencharlottestephanie parallelcomputingsolutionstothebalitskykovchegovequation |