15

As other answers have mentioned, it depends a lot on the workload and the availability of GPU-acceleration for the codes you use (or write). In principal, there are multiple platforms, but in practical use right now, Nvidia CUDA has the best performance and largest use. Nvidia offers a GPU Application directory - listing existing codes with CUDA ...


14

ns/day refers to the number of nanoseconds (ns) of simulation (referring to the variable time in the simulation) that you can do in a day of computation (elapsed real time or wall time). It is a useful quantity to schedule one's work or to get a sense of what is achievable in a given period of time. There's no standard ns/day as it depends on the type of MD (...


12

I am providing only a basic overview of this process and so to get a complete idea of what is happening, please refer to published literature. During MD simulations, one is generally interested in how the system evolves with time (for example, the random motion of atoms cause the system temperature). Therefore, one must have an idea of how time varies for ...


12

It depends a lot on your application. All problems can be solved on a CPU, some problems can be solved much faster on a GPU. One thing to consider is the effort required to write your code. If you're writing your own code from scratch, adapting it for the GPU can be a lot of work, and potentially for little to no reward. On the other hand, if you're using ...


12

I can't say much about the popularity of GPUs in practical calculations. From a development point of view the speedups that can be expected from GPUs in plane-wave DFT are only moderate, probably around 2 to 3, maybe 7 if you are optimistic. See for example the paper describing the VASP implementation [1] or this stackoverflow question illustrating ...


10

While I can't comment on concrete speed-ups as I'm not very familiar with GPU programming itself, I would like to point out that most of the computing time is spent in FFT and GEMM (matrix matrix multipicaltions) calls. A friend of mine tested this for the GPAW code where those two thing accounted for > 70 % of the CPU time. I imagine it is similar for other ...


10

Generally, CPUs give you the best bang for buck. While GPUs are promising, support for them is lacking in many codes. Moreover, even if some codes do support GPUs, the speedups are embarassingly small. Good GPUs are quite expensive: e.g. NVidia cards cost several thousands of dollars, and to get the best efficiency one might need several of them. For this ...


9

I experienced[1] about 5-10 times speedup with GPU accelerated Quantum Espresso on Tesla V100 32 Gb compared with Intel Core i7 9700K processor with 8 cores and 32 Gb RAM. The above mentioned system's volume was about 125 cubic Angstroms, it had about 19 atoms, 5 k-points, ecutwfc = 80 Ry, ecutrho = 320 Ry. 24 SCF iterations took 54 seconds with GPU, and ...


7

ns/day measures the number of nanoseconds of the system's dynamics that can be simulated in 1 day of computing. hours/ns measures the number of hours of computing required to simulate 1 nanosecond of the system's dynamics. Both of these are extremely hardware-dependent and even more importantly, depend on the size of the system being simulated. In the link ...


7

Have you already tried the Pw_forum? I do not use QE and so cannot directly answer your question. However, I'd say that this is one of those things where you are better off finding either a tutorial (in places such as YouTube) or someone you know with experience in compiling software for linux (just to make sure that your compilation is successful). That ...


6

This sounds like a very standard problem for multiphysics applications. The oven is comprised of a heating element, insulation, and argon gas inside it. You will have both radiative and convective transfer of heat inside the oven, and you'll need to model both to find out how the temperature distribution develops. Here is a demonstration of COMSOL for a ...


5

I recently installed VASP GPU version provided by NVIDIA (here's the installation tutorial) on my machine that has an RTX 2080 Ti GPU. GPU version was announced for vasp-5.4.1 and works fairly well for vasp-5.4.4 too. However, I have only managed to observe around 1.5x - 2x speedup when running VASP on the GPU compared to my Intel(R) Core(TM) i7-8700 CPU ...


5

I'm not a Quantum Espresso expert, but I'll try to answer this as best I can. There are two aspects to this answer: 1) whether you have a GPU-enabled version of Quantum Espresso; and 2) whether you should use your GPUs anyway. GPU port of Quantum Espresso Using GPUs effectively requires substantial changes to the source code (e.g. OpenACC, OpenMP5, CUDA-...


2

This was answered on the matsci LAMMPS forum by Axel Kohlmeyer. To summarize, there is a known bug (still unresolved as of 7/28/2021) that leads to a corrupted neighbor list when using GPUs in combination with a hybrid pair style. Axel suggests using the command lmp -sf gpu -pk gpu 1 neigh no -in in as a workaround.


1

So long as your calculations are parallelizable, using GPU's should provide significant speedup over just using CPU's alone. Nvidia last week at their GTC conference announced their new A100 GPU, which provides up to 20X speedup over its predecessor, the Volta V100. Nvidia debuts the A100, its most powerful graphics processor yet. Each A100 can deliver up to ...


Only top voted, non community-wiki answers of a minimum length are eligible