#ThisMonthInFluiddyn - Nov 2024 edition

* #FluidDyn, #FluidSim, #FluidImage now works with #numpy 2 and #mpi4py 4.0.

* #Spack packaging recipes

https://foss.heptapod.net/fluiddyn/fluiddyn/-/tree/branch/default/misc/spack?ref_type=heads

* #Apptainer and #Guix packaging for FluidSim

https://foss.heptapod.net/fluiddyn/fluidsim/-/tree/branch/default/doc/apptainer?ref_type=heads

https://foss.heptapod.net/fluiddyn/fluidsim/-/blob/7e52e7bed240665823e04e9f5f8a1fae7d787d06/doc/examples/clusters/gricad/README.md

* Basic support for #Jax is part of #Transonic with v0.7.x

* #FluidImage v0.5.x has a number of improvements, such as work towards matching #UVmat (the MATLAB #PIV suite), new executors and a command `fluidimage-mean`

Article: 10 times faster, running cases in parallel

In this article, we explore running optimization model cases in parallel. Specifically, we use the Python multiprocessing and mpi4py libraries to fully use the many CPU cores/threads in modern computers.

Our goals are to:
- Illustrate how to apply the multiprocessing and mpi4py libraries to running optimization model cases in parallel.
- Measure the performance of running cases in parallel compared with serially.
- Compare the performance of an old 4 core / 4 thread CPU with a new 20 core / 28 thread CPU, using the HiGHS solver.

https://www.solvermax.com/blog/10-times-faster-running-cases-in-parallel
#Python #pyomo #orms #optimization #modelling #HiGHS #multiprocessing #mpi4py