====== Introduction to GPU accelerated jobs ======

<note info>This information is severely outdated</note>

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''.

<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 {{: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
</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 ;)