This online workshop is meant to give an overview of working with research data in Python using general libraries for storing, processing, analysing and sharing data. The focus is on improving performance. After covering tools for performant processing (netcdf, numpy, pandas, scipy) on single workstations the focus shifts to parallel, distributed and GPU computing (snakemake, numba, dask, multiprocessing, mpi4py).
We recently achieved two important objectives in our collaboration with VeloxChem: We helped port the code to compile […]
The message passing interface (MPI) is the go-to technology for the development of distributed parallel programs. In this […]