[CP2K-user] CP2K performance on GPUs

[Alfio Lazzaro]

You may take a look at this issue on 
github: https://github.com/cp2k/cp2k/issues/73

In your particular case, your setup of 8 V100 is pretty extreme and it 
would require a large computation. Which test are you using for 
benchmarking?

Then, your setup of 8 ranks + 5 threads should be OK. CP2K attaches ranks 
to GPU in a round-robin manner, therefore in your case there is a rank 
talking to each GPU.
We don't have a large experience of multi-gpu nodes, hence I would suggest 
to do some scalability test by running 1 rank, 2 ranks, ... 8 ranks (always 
5 threads) to check how the performance scales. BTW, make sure CP2K is able 
to recognize 8 GPUs by checking the following output at the beginning:

 DBCSR| ACC: Number of devices/node                                        
    1

Eventually, you might consider reoptimizing the kernels for the V100, but 
this is not a priority...

Alfio



Il giorno sabato 3 novembre 2018 07:55:09 UTC+1, for... at gmail.com ha 
scritto:
>
> HI,
>
> How is the CP2K performance on GPUs in general?
>
> I'm getting very low performance on GPUs(Nvidia V100 SXM2). It is a single 
> node benchmark with 8 GPUs and Intel Skylake Gold 6148 dual processors. 
>
> The CP2K time on 8 GPUs (CP2K-6.1 psmp version, ifort-2017, CUDA-9.2, 8mpi 
> ranks + 5 threads per rank) is still slower than CP2K time of CPU only 
> benchmark.
>
> For CPU runs, the CP2K-6.1 is built with LIBXSMM-1.8.3.
>
> For GPU runs, have tried both with and without LIBXSMM. There is no 
> performance difference. But both's performance is still slower than CPU 
> only benchmark even after using all the 8 GPUs & all 40 cores of CPU. Can 
> some one please share their experience on CP2K performance with GPUs.
>
> The CUDA specific DFLAGS used are: -D__ACC -D__DBCSR_ACC -D__PW_CUDA.
>
>
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