====== 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 {{:services:cluster:tesla-m2070-board-specification.pdf|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 [[interactivesessions|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 {{:services:cluster:run.mdp|}} -c waterbox.gro -p {{:services:cluster: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 ;)