I’m co-organiser of this slightly-different in October 2023 at the Forschungszentrum Jülich, Germany. Rather than following the traditional format of 3-4 day populated by talks with the odd poster session, this is an extended workshop made up of six mini-workshops. Since it is focussed on python-based tools for biomolecular simulations, of which there are an increasing number, the first mini-workshop will be a bootcamp that I will be lead instructor on (helped by David Dotson from ASU). I’m also leading the next mini-workshop on analysing biomolecular simulation data.
I first used an Apple Mac when I was eight. Apart from a brief period in the 1990s when I had a PC laptop I’ve used them ever since.
Until last year I had an old MacPro which had four PCI slots so you could add a GPU-capable NVIDIA card, although you were limited by the power supply. A GPU can accelerate the molecular dynamics code I use, , by up to 2-3 times.
Unfortunately, when Apple designed the new MacPro, they put in so although it is a lovely machine, you can’t run CUDA applications.
But this morning I saw that the next release candidate of GROMACS 5.1 supported . Although OpenCL applications are usually a bit slower than applications, this would, in theory, allow me to accelerate GROMACS on my MacPro.
So I downloaded the code, compiled it with the appropriate OpenCL flag and it just works! I benchmarked the code on an atomistic and a coarse-grained benchmark that I use. Running on a single core, using a single AMD FirePro D300 accelerated GROMACS by 2.0 and 2.5x for the atomistic and coarse-grained benchmarks, respectively.