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
.
<xterm> dreger@yoshi:~/gpu> sinfo | grep gpu gpu-test up 2:00:00 1 idle ygpu01 gpu-main up infinite 30 idle ygpu[02-31] </xterm>
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:
<xterm> 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 |
+—————————————————————————–+ </xterm>
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:
<xterm> 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 </xterm>
While your jobs run you can log in to the node and call nvidia-smi
to see if the GPUs are used at all:
<xterm> 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 |
+—————————————————————————–+ </xterm>
Please check your job logfiles to see if your program has some problems using the GPUs. In case of GROMACS this might look like:
<xterm> 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! </xterm>
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:
<xterm> 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 </xterm>
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:
<xterm> #!/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 </xterm>
Please make sure you change the email if you use this for your own tests ;)