On Wed, 9 Nov 2016 06:35 pm, John Ladasky wrote: [...] > I work a lot with a package called GROMACS, which does highly iterative > calculations to simulate the motions of atoms in complex molecules. > GROMACS can be built to run on a pure-CPU platform (taking advantage of > multiple cores, if you want), a pure-GPU platform (leaving your CPU cores > free), or a blended platform, where certain parts of the algorithm run on > CPUs and other parts on GPUs. This latter configuration is the most > powerful, because only some parts of the simulation algorithm are optimal > for GPUs. GROMACS only supports NVidia hardware with CUDA 2.0+. > > Because of the iterative nature of these calculations, small discrepancies > in the arithmetic algorithms can rapidly lead to a completely > different-looking result. In order to verify the integrity of GROMACS, > the developers run simulations with all three supported hardware > configurations, and verify that the results are identical. Now, I don't > know that every last function and corner case in the IEEE-754 suite gets > exercised by GROMACS, but that's a strong vote of confidence.
That is really good, and I'm very pleased to learn about it. But I don't think that the average scientist writes code of that quality. (Nor should they: replicating work is the job of the scientific community as a whole, not a single scientist.) Thanks for the update on the state of art for GPU numeric computing. I'll agree that things are better than I feared. -- Steve “Cheer up,” they said, “things could be worse.” So I cheered up, and sure enough, things got worse. -- https://mail.python.org/mailman/listinfo/python-list
