performance

I was at the 2024 Open Science Ambassodors Meeting in Berlin and gave a presentation on reproducibility in scientific and high performance computing. The presentation slides are at https://mpievolbio-scicomp.pages.gwdg.de/presentations/osa2024.pdf, the permalink is https://dx.doi.org/10.5281/zenodo.14051129. I also advertised our FAQ collection on good scientific practice in research software engineering, now online at https://mpg-rse.pages.gwdg.de/gwp-rse. At the workshop, we collected many more questions and also some answers. Thanks to the participants for your contributions! [Read More]

Introduction The python library numba provides (among others) just-in-time (jit) compilation for python. Python code can gain tremendous speed-ups without the need to rewrite a single line of code. Simply adding a decorator to a function such as illustrated in this example: import numba @njit def accelerate_me(): ... can lead to run times comparable to C or Fortran code. Let's make a concrete example, where we add random numbers in a for loop (not advised, but used here to demonstrate numba). [Read More]