On 22.09.2020, at 16:05, Rudolf Weeber <weeber@icp.uni-stuttgart.de> wrote:
Hi Martin,
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
```python
import time
...
tick =time.time()
system.integrator.run(steps)
tock = time.time()
print("Time per step (s):",(tock-tick)/steps)
```
Regards, Rudolf
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,
Martin
--
Dr. Rudolf Weeber
Institute for Computational Physics
Universität Stuttgart
Allmandring 3
70569 Stuttgart
Germany
Phone: +49(0)711/685-67717
Email: weeber@icp.uni-stuttgart.de
http://www.icp.uni-stuttgart.de/~icp/Rudolf_Weeber