5 ms·

[Disclaimer: How do I know this stuff? I'm a core developer of GROMACS, one of the major molecular dynamics simulation FOSS community codes. Molecular dynamic i

[Disclaimer: How do I know this stuff? I'm a core developer of GROMACS, one of the major molecular dynamics simulation FOSS community codes. Molecular dynamic is the algorithm/method behind the F@H simulations. I work as a researcher in high performance computing and have (co-)developed many of the parallel algorithms and GPU acceleration in GROMACS.] 20-200x is simply not true. Typically, such numbers are a result of comparing unoptimized CPU code to moderately or well-optimized GPU code which is often misleading. (Such differences are however perfectly reasonable when comparing hardware-accelerated workloads like ML/DL). If you compare actually well-optimized codes, you'll see more like ~4-5x difference in performance for FLOP/instrction-bound code as it is the case for well-optimized molecular dynamics. Case in point, I recently pointed out the huge difference in CPU performance of two of the top molecular simulation codes, one of which is 8-10x faster on CPUs than the other, solving the same problem [2]. F@H relies on GROMACS as a CPU engine [1] which happens to be the same code as I quoted above as the fast one. The trouble is that F@H has not updated their CPU engine for many years and distribute CPU binaries which lack crucial SIMD optimizations to allow making use of AVX2/AVX512 on modern x86 CPUs as well as the years of algorithmic improvement and code optimization we made. These two factors combined lead to _significantly_ lower F@H CPU performance compared to what they had we're they using a recent GROMACS engine. Consequently, due to the combination of an inherent performance advantage of GPUs and the severely outdated CPU engine, it is indeed not worth wasting energy with running F@H on CPUs. [1] https://en.wikipedia.org/wiki/List_of_Folding@home_cores#GROMACS https://en.wikipedia.org/wiki/List_of_Folding@home_cores#GRO... [2] https://twitter.com/twilard/status/1235142089156984832?s=20 https://twitter.com/twilard/status/1235142089156984832?s=20 Edit 1: adjusted wording to reflect that the performance difference between running outdated GROMACS version and subotimal SIMD optimizations on modern hw can have a range of performance difference, depending on hardware and inputs. Edit 2: fixed typo + formatting.

swinglock 7y ago
It seems irresponsible to waste donated cycles like that. Does anyone else do it better?
- slizard 7y ago
  Can you please elaborate? Are you referring to the F@H CPU "cores" being outdated and inefficient?
  - swinglock 7y ago
    Yes, if it's so. When donating money or compute time, I want my donation to be used efficiently and not be wasted by high overheads.
    - slizard 7y ago
      > When donating money or compute time, I want my donation to be used efficiently and not be wasted by high overheads. Fair point. I think this is something you should bring up with the authors of Folding@Home. I do not work on that project. My personal view on this is that there is always a cost/benefit balance that one has to strike which is often tricky especially given the considerably constrained resources as it is typically the case in academic computational/simulation tool development. It is however, as you point out, a great responsibility of the researchers and developers of codes to make sure that choices made and action taken (or not) do not lead to disproportionate waste of resources donated by volunteers or awarded through a grant by research funding agencies. I do not know the detailed reasons why F@H chose to not update CPU "core" since FahCore_a7 (AFAIK based one GROMACS code from 2014), but it is likely related to the aforementioned cost/benefit analysis done in their team. One of the motivations could have been (just hypothesizing) that the software engineering efforts estimated to be required to update FahCore for CPUs (which generate a very small fraction of the "points") would have taken away resources from the GPU FahCore.
- slizard 7y ago
  > Does anyone else do it better? To that question, assuming by "anyone" here you are asking about other donate-your-cycles-distributed-computing-projects: I am not too familiar with how well-optimized the codes of different @home projects are. Taking a few steps back, perhaps the efficiency of these codes is the lesser issue and to be honest, in some (many?) cases other forms of donation/contribution may further more scientific progress than simply crunching numbers on one's home PC.

swinglock 7y ago

It seems irresponsible to waste donated cycles like that. Does anyone else do it better?

slizard 7y ago
Can you please elaborate? Are you referring to the F@H CPU "cores" being outdated and inefficient?
- swinglock 7y ago
  Yes, if it's so. When donating money or compute time, I want my donation to be used efficiently and not be wasted by high overheads.
  - slizard 7y ago
    > When donating money or compute time, I want my donation to be used efficiently and not be wasted by high overheads. Fair point. I think this is something you should bring up with the authors of Folding@Home. I do not work on that project. My personal view on this is that there is always a cost/benefit balance that one has to strike which is often tricky especially given the considerably constrained resources as it is typically the case in academic computational/simulation tool development. It is however, as you point out, a great responsibility of the researchers and developers of codes to make sure that choices made and action taken (or not) do not lead to disproportionate waste of resources donated by volunteers or awarded through a grant by research funding agencies. I do not know the detailed reasons why F@H chose to not update CPU "core" since FahCore_a7 (AFAIK based one GROMACS code from 2014), but it is likely related to the aforementioned cost/benefit analysis done in their team. One of the motivations could have been (just hypothesizing) that the software engineering efforts estimated to be required to update FahCore for CPUs (which generate a very small fraction of the "points") would have taken away resources from the GPU FahCore.
slizard 7y ago
> Does anyone else do it better? To that question, assuming by "anyone" here you are asking about other donate-your-cycles-distributed-computing-projects: I am not too familiar with how well-optimized the codes of different @home projects are. Taking a few steps back, perhaps the efficiency of these codes is the lesser issue and to be honest, in some (many?) cases other forms of donation/contribution may further more scientific progress than simply crunching numbers on one's home PC.