457 214 9031 6 25325 document 9031 Data Files.zip application/zip 4c5a3647e809f08452ba8616670c3872 MD5 33018387 2018-05-14 12:06:47 https://researchdata.bath.ac.uk/457/2/Data%20Files.zip 457 2 2 application/zip other Dataset for the research paper 'Efficient characterisation of large deviations using population dynamics'. This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath. en public cc_by

Data Files.zip

data 9032 4 25354 document 9032 README.md text/plain 39507c61216e41235f8ac62de8534dd1 MD5 1533 2018-05-15 09:02:42 https://researchdata.bath.ac.uk/457/3/README.md 457 3 3 text/plain other README for the data set including a list of the folders and subfolders within the data set and what type of files each folder and subfolder contain. en public cc_by

README.md

documentation archive 6624 disk0/00/00/04/57 2018-05-16 14:43:59 2024-06-24 10:44:45 2018-05-16 14:43:59 data_collection show Brewer Tobias T.J.M.Brewer@bath.ac.uk University of Bath TRUE Jack Robert rj248@bath.ac.uk University of Bath FALSE Supervisor Clark Stephen S.R.Clark@bath.ac.uk 0000-0002-2072-7499 University of Bath Supervisor Bradford Russell R.J.Bradford@bath.ac.uk 0009-0003-3251-2051 University of Bath GJ0060 GJ0090 dept_compsci dept_physics Large Deviations, Efficient Computation, Parallelisation, Numerical Algorithm, Population Dynamics Each data folder is related to a figure within the 'Efficient characterisation of large deviations using population dynamics' paper. Each folder (including the repository) has a README.md file corresponding to the enclosed data and .m processing files. This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath. We have produced a research paper 'Efficient characterisation of large deviations using population dynamics' investigating the nature of rare events in the SSEP. We study activity distribiutions of this process on a one dimensional lattice with periodic boundary conditions. The process is simulated using a C++ code and parallelisation is used to increase computational efficiency. The two parallelisation methods that we use are OpenMP and MPI and these are stored in a repository in this data set. The codes are designed such that they can be used to study rare events in other processes and study observables other than activity. The dataset includes .txt and .mat files output by the C++ files and by the .m files used for processing. Further .m MATLAB files are used to produce the data and tables within the paper. 2018-05-08 University of Bath public RightsHolder University of Bath ClusterVision ClusterVision HPC scholarship Running simulations with .cpp and .hpp header files. Producing some analytic values with .m MATLAB files. Data processed and plotted with .m Matlab files. To run alternative systems or observables the user is required to define classes in header files. Computational files: .cpp, .hpp and Matlab files are included in this dataset. The OpenMP and serial codes are run with an intel compiler (icpc) and the MPI codes are run with the corresponding intel MPI compiler (mpiicpc). This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath. 2015-10-01 2017-10-01 en 1 10.15125/BATH-00457 https://doi.org/10.1088/1742-5468/aab3ef pub open