Dear Fabrizio, thanks a lot for the help. Now my compilation works. Best regards, Mauro Sgroi.
_______________________ Dr. *Mauro Francesco Sgroi* Department of Chemistry University of Turin Via Quarello 15a I-10135 TORINO (Italy) Tel. +39 011-670-8372 +39 011-670-7364 e-mail: [email protected] Web: www.met.unito.it www.chimica.unito.it Orcid: https://orcid.org/0000-0002-0914-4217 Webex: https://unito.webex.com/webappng/sites/unito/dashboard/pmr/maurofrancesco.sgroi Il giorno ven 2 ago 2024 alle ore 15:13 Fabrizio Ferrari Ruffino < [email protected]> ha scritto: > The GPU executable can be launched in the same way as the CPU one, but > considering this: > > - the number of mpi per node must be the same as the number of GPUs (2 > mpi per node in your case). In principle you can try to use more mpi > processes per GPU, but it is not recommended; > - you can enable openMP together with GPU (add --enable-openmp to > ./configure) in order to exploit CPU threading in the few places where GPU > porting is not present (no more than 8 thread per node, generally doesn't > make much difference though) > > > I don't know which scheduler is in use in your system, here is an example > of a batch job in slurm launching on 2 nodes with 2 GPUs: > > ------------------------------------------------------------------------------------------------------------ > #!/bin/bash > #SBATCH --nodes=2 > #SBATCH --ntasks-per-node=2 > #SBATCH --cpus-per-task=1 > #SBATCH --gres=gpu:2 > #SBATCH --time=00:20:00 > > module purge > module load hpcsdk/24.3 > > export* OMP_NUM_THREADS=1* > > mpirun -np 4 /home/q-e/bin/pw.x -nk 1 -nb 1 -input scf.in > scf.out > > --------------------------------------------------------------------------------------------------------------- > > Hope it helps > Cheers, > > Fabrizio > > ------------------------------ > *From:* Mauro Francesco Sgroi <[email protected]> > *Sent:* Friday, August 2, 2024 2:35 PM > *To:* Fabrizio Ferrari Ruffino <[email protected]> > *Cc:* Quantum ESPRESSO users Forum <[email protected]> > *Subject:* Re: [QE-users] Help for compilation with Nvidia HPC SDK > > Dear Fabrizio, > thanks a lot for the explanation. > I was unsure about how to proceed and worried not to get the proper > performance on the GPU. > > May I ask for help regarding the way of running the code? Where can I find > instructions on how to launch the executable? > > For example, how can I control the number of GPUs used and the parallel > processes? > > I have 2 GPUs for each node. > > Thanks a lot and best regards, > Mauro Sgroi. > > _______________________ > > Dr. *Mauro Francesco Sgroi* > > Department of Chemistry > > University of Turin > > Via Quarello 15a > > I-10135 TORINO (Italy) > > Tel. > > +39 011-670-8372 > > +39 011-670-7364 > > e-mail: [email protected] > > Web: > > www.met.unito.it > > www.chimica.unito.it > > Orcid: https://orcid.org/0000-0002-0914-4217 > > Webex: > https://unito.webex.com/webappng/sites/unito/dashboard/pmr/maurofrancesco.sgroi > > > > Il giorno ven 2 ago 2024 alle ore 14:11 Fabrizio Ferrari Ruffino < > [email protected]> ha scritto: > > Hi, > there are a few minor FFTXlib calls somewhere in QE which are still on > CPU, therefore it is better to have a CPU fft backend enabled too. Whether > to use the internal one or FFTW3 should not make much difference, since all > the main stuff runs on gpu (therefore calling cuFFT). > In a CPU run the FFTW3 backend is faster than the internal one, but, as I > said, in a GPU run it should be quite irrelevant. > Cheers, > > Fabrizio > CNR IOM > ------------------------------ > *From:* users <[email protected]> on behalf of > Mauro Francesco Sgroi via users <[email protected]> > *Sent:* Friday, August 2, 2024 12:13 PM > *To:* Quantum ESPRESSO users Forum <[email protected]> > *Subject:* [QE-users] Help for compilation with Nvidia HPC SDK > > Dear all, > I am trying to compile the 7.3.1 version of Quantum Espresso using the > last Nvidia HPC SDK (24.7) on Ubuntu 24.04. > > I am configuring as follows: > > export BLAS_LIBS='-L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/math_libs/lib64 > -lcublas -lcublasLt -L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/compilers/lib > -lblas -L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/cuda/lib64 -lcudart' > > export > LAPACK_LIBS='-L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/math_libs/lib64 > -lcusolver -lcurand -lcublas -lcublasLt -lcusparse > -L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/compilers/lib -llapack > -L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/cuda/lib64 -lcudart' > > export > SCALAPACK_LIBS='-/opt/nvidia/hpc_sdk/Linux_x86_64/2024/comm_libs/12.5/openmpi4/openmpi-4.1.5/lib > -lscalapack > -L/opt/nvidia/hpc_sdk/Linux_x86_64/2024/comm_libs/12.5/openmpi4/latest/lib > -lmpi -lopen-pal' > > ./configure --with-cuda=/opt/nvidia/hpc_sdk/Linux_x86_64/2024/cuda/12.5 > --with-cuda-cc=75 --with-cuda-runtime=12.5 --with-cuda-mpi=yes > > In this way, the internal FFTW library is selected. Should I use the FFTW3 > library together with cufft? > > Can the two libraries work together? Is it normal that the internal FFTW > library is used? Or should the cufft library be sufficient? > > Or is it better to use the cufftw library supplied by NVIDIA? > > Can I have some guidance on these aspects? > > Thanks a lot and best regards, > Mauro Sgroi. > > _______________________ > > Dr. *Mauro Francesco Sgroi* > > Department of Chemistry > > University of Turin > > Via Quarello 15a > > I-10135 TORINO (Italy) > > Tel. > > +39 011-670-8372 > > +39 011-670-7364 > > e-mail: [email protected] > > Web: > > www.met.unito.it > > www.chimica.unito.it > > Orcid: https://orcid.org/0000-0002-0914-4217 > > Webex: > https://unito.webex.com/webappng/sites/unito/dashboard/pmr/maurofrancesco.sgroi > > >
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