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epyc : computational experiment management in Python
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dc.contributor.author | Dobson, Simon Andrew | |
dc.date.accessioned | 2022-04-22T15:30:41Z | |
dc.date.available | 2022-04-22T15:30:41Z | |
dc.date.issued | 2022-04-18 | |
dc.identifier | 279066498 | |
dc.identifier | eedb4da7-d693-4c55-9da8-a334e1832bc9 | |
dc.identifier.citation | Dobson , S A 2022 , ' epyc : computational experiment management in Python ' , Journal of Open Source Software , vol. 7 , no. 72 , 03764 . https://doi.org/10.21105/joss.03764 | en |
dc.identifier.issn | 2475-9066 | |
dc.identifier.other | ORCID: /0000-0001-9633-2103/work/111972181 | |
dc.identifier.uri | https://hdl.handle.net/10023/25237 | |
dc.description.abstract | epyc is a Python module for designing, executing, storing, and analysing the results of large sets of (possibly long-running) computational experiments, as are often found when writing simulations of complex networks and other domains. It allows the same experimental code to be run on single machines, multicore machines, and computational clusters without modification, and automatically manages the execution of an experiment for different parameter values and for multiple repetitions. | |
dc.format.extent | 3 | |
dc.format.extent | 181153 | |
dc.language.iso | eng | |
dc.relation.ispartof | Journal of Open Source Software | en |
dc.subject | Computational science | en |
dc.subject | High-performance computing | en |
dc.subject | Experimental design | en |
dc.subject | QA76 Computer software | en |
dc.subject | T-NDAS | en |
dc.subject | AC | en |
dc.subject.lcc | QA76 | en |
dc.title | epyc : computational experiment management in Python | en |
dc.type | Journal article | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.identifier.doi | https://doi.org/10.21105/joss.03764 | |
dc.description.status | Peer reviewed | en |
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