Macro-parallelisation for controlled source electromagnetic applications
© 2015 Elsevier B.V. Many geophysical computational problems can be referred to as "embarrassingly parallel". Parallel computing utilises linked CPU cores to solve computational problems. We create a "macro" parallelisation method that rapidly recovers solutions to large scale el...
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| Format: | Journal Article |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/13335 |
| _version_ | 1848748320327991296 |
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| author | Pethick, Andrew Harris, Brett |
| author_facet | Pethick, Andrew Harris, Brett |
| author_sort | Pethick, Andrew |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 Elsevier B.V. Many geophysical computational problems can be referred to as "embarrassingly parallel". Parallel computing utilises linked CPU cores to solve computational problems. We create a "macro" parallelisation method that rapidly recovers solutions to large scale electromagnetic forward and inverse modelling problems. The method involves software operating above a generic electromagnetic data structure. Two examples are provided. The first example quantifies the reduction in computational time where macro-parallelisation is applied to forward modelling of data generated during synthetic marine controlled source electromagnetic surveys. In the second numerical experiment we apply macro-parallelisation to recover the subsurface conductivity distribution from a large airborne transient electromagnetic survey spanning more than 2000km2. The computation time for inverting 98 thousand soundings with a serial batch approach on an i7 with a single thread was 65h. Computational time from inverting 98 thousand soundings on a single thread of a standard i7 processor was 65h. A 1700 times improvement in computation time was achieved through macro-parallelisation across just 350 cores of a Cray XC30 Supercomputer. Inversion of data for the full AEM survey took just 135s. Parallel computing is rapidly becoming an essential for geophysicists. We provide description, sequence diagrams, pseudo-code and examples to illustrate its implementation. In summary we present applied parallelisation for the masses. |
| first_indexed | 2025-11-14T07:03:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-13335 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:03:10Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-133352017-09-13T14:59:18Z Macro-parallelisation for controlled source electromagnetic applications Pethick, Andrew Harris, Brett © 2015 Elsevier B.V. Many geophysical computational problems can be referred to as "embarrassingly parallel". Parallel computing utilises linked CPU cores to solve computational problems. We create a "macro" parallelisation method that rapidly recovers solutions to large scale electromagnetic forward and inverse modelling problems. The method involves software operating above a generic electromagnetic data structure. Two examples are provided. The first example quantifies the reduction in computational time where macro-parallelisation is applied to forward modelling of data generated during synthetic marine controlled source electromagnetic surveys. In the second numerical experiment we apply macro-parallelisation to recover the subsurface conductivity distribution from a large airborne transient electromagnetic survey spanning more than 2000km2. The computation time for inverting 98 thousand soundings with a serial batch approach on an i7 with a single thread was 65h. Computational time from inverting 98 thousand soundings on a single thread of a standard i7 processor was 65h. A 1700 times improvement in computation time was achieved through macro-parallelisation across just 350 cores of a Cray XC30 Supercomputer. Inversion of data for the full AEM survey took just 135s. Parallel computing is rapidly becoming an essential for geophysicists. We provide description, sequence diagrams, pseudo-code and examples to illustrate its implementation. In summary we present applied parallelisation for the masses. 2016 Journal Article http://hdl.handle.net/20.500.11937/13335 10.1016/j.jappgeo.2015.11.013 restricted |
| spellingShingle | Pethick, Andrew Harris, Brett Macro-parallelisation for controlled source electromagnetic applications |
| title | Macro-parallelisation for controlled source electromagnetic applications |
| title_full | Macro-parallelisation for controlled source electromagnetic applications |
| title_fullStr | Macro-parallelisation for controlled source electromagnetic applications |
| title_full_unstemmed | Macro-parallelisation for controlled source electromagnetic applications |
| title_short | Macro-parallelisation for controlled source electromagnetic applications |
| title_sort | macro-parallelisation for controlled source electromagnetic applications |
| url | http://hdl.handle.net/20.500.11937/13335 |