Hi, On 24/06/15 14:20, CatDog wrote: > I am sure the PyFR is running when I use nvidia-smi command. But GT620 > does not support displaying GPU usage. How can I know how many cores am > I using?
The term 'CUDA cores' is not particularly meaningful. It is likely that during a time step cuBLAS will employ all of the SMs on the GPU -- however this says nothing about how efficiently they're being used. > another problem is when I use more CPU cores for OpenMP backend, it runs > slower > | > $ export OMP_NUM_THREADS=32 > $ pyfr run -b OPENMP -p euler_vortex_2d.pyfrm euler_vortex_2d.ini > 100.0% > [=================================================================================>] > 100.00/100.00 ela: 00:01:52 rem: 00:00:00 > $ export OMP_NUM_THREADS=64 > $ pyfr run -b OPENMP -p euler_vortex_2d.pyfrm euler_vortex_2d.ini > 100.0% > [=================================================================================>] > 100.00/100.00 ela: 00:02:12 rem: 00:00:00 > | You should aim to use one MPI rank per NUMA zone. There are some good postings on the mailing list outlining how to best to go about this. However, the Euler vortex test case is far too small to be useful for any sort of benchmarking. It will struggle to saturate a single CPU core. > And I am confused with the relationship between mpirun and openmp. It > seems that mpirun -n N and OMP_NUM_THREADS=M means M*N CPU cores are > used. Am I right? That is correct. Regards, Freddie. -- You received this message because you are subscribed to the Google Groups "PyFR Mailing List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send an email to [email protected]. Visit this group at http://groups.google.com/group/pyfrmailinglist. For more options, visit https://groups.google.com/d/optout.
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