We are happy to announce that Folding@Home Core 17 has entered Beta. Externally, you probably won’t notice too much of a difference. Internally, this is a complete overhaul that brings many new features, and sets a strong foundation for the future of GPU core development. In addition, the restructuring brings much tighter integration of the core with the rest of the development within Folding@Home.
We’re also introducing an explicit solvent project (p7661) as part of the Beta. To keep the credit assigned to these projects consistent with previous explicit solvent work units run on CPUs, we are also awarding a quick return bonus with a k-factor of 0.75. This reflects the additional scientific value of these units, and keeps the Folding@Home credit awards consistent across different architectures.
This is a still a very new core, a lot of the features have yet to be fully tested. Thus, as is the beta policy, no official support is given. You must enable the -beta flag on FAHClient, ie. set client-type=beta. If you’re using client 7.2.x or earlier, there are two options:
2. If -gpu-vendor is not specified, the core will automatically guess the platformId.
Otherwise, 7.3.6 lets you specify the particular -gpu-vendor as an option
Supported NVIDIA cards: Fermi or better (Titan does not work atm, as NVIDIA needs to publish new OpenCL drivers)
Supported ATI cards: HD5000 or better
As always, please use the latest WHQL drivers (Win XP is NOT supported due to super-old AMD drivers).
We have deprecated several layers of GROMACs and other wrappers as the old architecture severely limited the types of simulations that can be run. Much of the work on the new core has been to replace existing features. The resulting code is now more streamlined and integrated. We also anticipate that this major re-design will allow us to introduce new features into the Folding@Home much faster.
We have introduced a new serialization mechanism that allows Pande Group researchers to setup significantly more diverse simulations. Pande Group researchers can now easily setup jobs and projects using Python (with a much richer and easier to use set of libraries), while the core maintains its speed by virtue of being written in C++. We achieved this using a serialization technique, whereby all details of a simulation are encapsulated using a standardized format that is then be passed around between language barriers. This also drastically reduces the dependencies needed by the Work Server and other parts of Folding@Home.
A single unified core now runs both NVIDIA and AMD cards
Before we had two development branches for NVIDIA and AMD cards. It was a difficult and cumbersome task to debug and maintain. We couldn’t easily mix runs and gens produced by different GPU types. Now, using OpenCL, a single core supports not only AMD and NVIDIA, but theoretically any OpenCL-capable device.
By reducing the amount of boilerplate code, we’ve also increased the robustness and stability of the core. The log files should also now be much more informative. There are also a lot of useful debugging features built right into the core to help PG developers nail down hard to find bugs.
Extra special thanks to our internal testers:
Jesse_V, k1wi, artoar_11, bollix, ChelseaOilman, bruce, Demonfang, Grandpa_01, EXT64, Flaschie, HayesK, jimerickson, mmonnin_, P5-133XL, Patriot, rhavern, sc0tty8, SodaAnt, SolidSteel144, EvilPenguin, art_l_j_PlanetAMD64, thews, cam51037, Pin, muziqaz, baz657, PantherX, QINSP, Schro, hootis