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Re: Mpi and cuda
Re: Mpi and cuda
Tue, 22 Sep 2020 16:05:59 +0200
all the GPU stuff runs from the head node. The other nodes probably still load
the driver, so that's why you see them in the profile.
The GPU work overlaps with the CPU work in time, but some extra communication
is needed to gather the full system on the head node and send it to the GPU.
Before using LB GPU with MPI parallel simulation, it might be worthwhile to put
timings around the integration
tock = time.time()
print("Time per step (s):",(tock-tick)/steps)
On Tue, Sep 22, 2020 at 03:31:33PM +0200, Martin Kaiser wrote:
> Hello everybody,
> I have a technical question about using the open MPI and CUDA implementations
> at the same time.
> If I start my GPU accelerated espresso script in MPI, with the standard
> command like this:
> mpirun -n 4 espresso script.py;
> then 4 instances of the same job are started on my GPU, of which only one is
> actually doing some work on the GPU. If I monitor the usage with
> "nvidia-smi”, I get something like this:
> GPU GI CI PID Type Process name GPU Memory
> 1 N/A N/A 26365 C /usr/bin/python3 207MiB
> 1 N/A N/A 26366 C /usr/bin/python3 129MiB
> 1 N/A N/A 26367 C /usr/bin/python3 129MiB
> 1 N/A N/A 26368 C /usr/bin/python3 129MiB
> Additionally, if I kill this job, not all of the instances on the GPU are
> aborted, meaning that it is not freeing the memory on the card.
> Is there something I am doing wrong with how I compile or call Espresso? Or
> is it that the MPI implementation is not “aware of cuda” and instancing
> copies of the same job on the GPU.
> Thanks for the help,
Dr. Rudolf Weeber
Institute for Computational Physics
- Mpi and cuda, Martin Kaiser, 2020/09/22
- Re: Mpi and cuda,
Rudolf Weeber <=