Table of Contents
Introduction to GPU accelerated jobs
Currently we have 31 nodes in the yoshi cluster (ygpu01-ygpu31) equipped with GPU boards. The exact hardware config is:
- 2x NVidia Tesla M2070
- 2x Xeon X5570
- 24GB RAM
- QDR Infiniband between all GPU nodes
In oder to use the GPU cards, you need to allocate them through the queuing system using the –gres=gpu:2 option. You could also just use one card if you submit with –gres=gpu:1. You also have to explicitly state the partition to run in using –partition=gpu-main (or gpu-test for the GPU test queue).
GROMACS example using GPU acceleration
Here I give a simple example using GROMACS. First I'll use an interactive session to explore the GPU feature, in the end I'll supply a complete batch script for use with sbatch.
dreger@yoshi:~/gpu> sinfo | grep gpu gpu-test up 2:00:00 1 idle ygpu01 gpu-main up infinite 30 idle ygpu[02-31]
The test partition gpu-test which consists of the single node ygpu01 will most likely be free, since it has a timelimit of 2 hours. So we'll use that for testing:
dreger@yoshi:~/gpu> srun –time=02:00:00 –nodes=1 –tasks=8 –gres=gpu:2 –partition=gpu-test –mem=1G –pty /bin/bash
dreger@ygpu01:~/gpu> env | grep CUDA
CUDA_VISIBLE_DEVICES=0,1
dreger@ygpu01:~/gpu> nvidia-smi
Thu Jun 18 14:16:19 2015
+------------------------------------------------------+
| NVIDIA-SMI 340.65 Driver Version: 340.65 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla M2070 Off | 0000:14:00.0 Off | 0 |
| N/A N/A P0 N/A / N/A | 9MiB / 5375MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla M2070 Off | 0000:15:00.0 Off | 0 |
| N/A N/A P0 N/A / N/A | 9MiB / 5375MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Compute processes: GPU Memory |
| GPU PID Process name Usage |
|=============================================================================|
| No running compute processes found |
+-----------------------------------------------------------------------------+
The nvidia-smi command gives some information on the GPUs. Currently no process is running on the GPUs. We'll start a simple GROMACS computation:
dreger@ygpu01:~/gpu> module load gromacs/non-mpi/4.6.7-cuda dreger@ygpu01:~/gpu> genbox -box 9 9 9 -p -cs spc216 -o waterbox.gro dreger@ygpu01:~/gpu> grompp -f run.mdp -c waterbox.gro -p topol.top dreger@ygpu01:~/gpu> mdrun [...] Using 2 MPI threads Using 4 OpenMP threads per tMPI thread 2 GPUs detected: #0: NVIDIA Tesla M2070, compute cap.: 2.0, ECC: yes, stat: compatible #1: NVIDIA Tesla M2070, compute cap.: 2.0, ECC: yes, stat: compatible 2 GPUs auto-selected for this run. Mapping of GPUs to the 2 PP ranks in this node: #0, #1 [...] Core t (s) Wall t (s) (%) Time: 262.880 34.401 764.2 (ns/day) (hour/ns) Performance: 25.121 0.955
While your jobs run you can log in to the node and call nvidia-smi to see if the GPUs are used at all:
dreger@ygpu01:~> nvidia-smi
Thu Jun 18 14:25:21 2015
+------------------------------------------------------+
| NVIDIA-SMI 340.65 Driver Version: 340.65 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla M2070 Off | 0000:14:00.0 Off | 0 |
| N/A N/A P0 N/A / N/A | 67MiB / 5375MiB | 76% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla M2070 Off | 0000:15:00.0 Off | 0 |
| N/A N/A P0 N/A / N/A | 67MiB / 5375MiB | 77% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Compute processes: GPU Memory |
| GPU PID Process name Usage |
|=============================================================================|
| 0 11481 mdrun 55MiB |
| 1 11481 mdrun 55MiB |
+-----------------------------------------------------------------------------+
Please check your job logfiles to see if your program has some problems using the GPUs. In case of GROMACS this might look like:
NOTE: GPU(s) found, but the current simulation can not use GPUs
To use a GPU, set the mdp option: cutoff-scheme = Verlet
(for quick performance testing you can use the -testverlet option)
Using 8 MPI threads
2 GPUs detected:
#0: NVIDIA Tesla M2070, compute cap.: 2.0, ECC: yes, stat: compatible
#1: NVIDIA Tesla M2070, compute cap.: 2.0, ECC: yes, stat: compatible
2 compatible GPUs detected in the system, but none will be used.
Consider trying GPU acceleration with the Verlet scheme!
In this case a cutoff-scheme was specified that can not be used with GPU acceleration.
Compare the timings with a test run on the same node, that does not use the GPUs. In some cases the GPUs will not help at all, even though nvidia-smi shows a high utilization. For this example without GPU (note the missing -cuda in the module load command) we get:
dreger@ygpu01:~/gpu> module load gromacs/non-mpi/4.6.7
dreger@ygpu01:~/gpu> grompp -f run.mdp -c waterbox.gro -p topol.top
dreger@ygpu01:~/gpu> mdrun
Core t (s) Wall t (s) (%)
Time: 844.970 106.315 794.8
(ns/day) (hour/ns)
Performance: 8.128 2.953
So in this case the calculation runs about three times faster with two GPU cards.
Example batch file
A job script for the example given above could look like:
#!/bin/bash
#SBATCH --mail-user=dreger@physik.fu-berlin.de
#SBATCH --mail-type=end
#SBATCH --output=job%j.out
#SBATCH --error=job%j.err
#SBATCH --ntasks=8
#SBATCH --mem-per-cpu=1024
#SBATCH --time=01:00:00
#SBATCH --gres=gpu:2
#SBATCH --nodes=1
#SBATCH --partition=gpu-main
module load gromacs/non-mpi/4.6.7-cuda
TAG="${SLURM_JOB_ID}-$(hostname -s)-cuda"
grompp -f run.mdp -c waterbox.gro -p topol.top -o output-$TAG
mdrun -nt ${SLURM_CPUS_ON_NODE} -testverlet -v -deffnm output-$TAG
Please make sure you change the email if you use this for your own tests ;)