Upgraded: more GPUs added.

The Lengau cluster at the CHPC includes 9 GPU compute nodes with a total of 30 Nvidia V100 GPU devices. There are 6 gpu200n nodes with 3 GPUs in each, and 3 gpu400n nodes with 4 GPUs in each.

Jobs that require 1, 2 or 3 GPUs can be allocated to any node, and will share the node if the job does not use all the GPU devices on that node. Jobs that require 4 GPUs can only be allocated to gpu4* nodes and will not be shared, obviously.

Principle Investigators apply for GPU access for their Research Programme members through the CHPC Helpdesk. RP members may not apply directly. E-mailed applications will not be considered.

Research programme allocations will be depleted by a factor of the wallclock time and the number of GPUs (1, 2, or 4) requested by the job.

gpu_allocation_used = 40 * runtime * ngpus

Some pre-built applications have automated scripts that you can use to launch them:

• GROMACS
• AMBER18
• NAMD

There are four queues available in PBSPro which access the GPU nodes:

Note the ncpus parameters above is the maximum that should be set to match the number of GPU devices you need.

The maximum wall clock time on all GPU queues is 12 hours.

#PBS -l walltime=12:00:00

It is better to specify a shorter walltime if your code executes in less time: this allows the scheduler a better chance of running your job sooner.

A single interactive session may be request on a GPU node by

qsub -I -q gpu_1 -P PRJT1234 -l select=1:ncpus=9:ngpus=1

NB: Replace PRJT1234 with your project number.

The default time for an interactive session is 1 hour.

#!/bin/bash
#PBS -N nameyourjob
#PBS -q gpu_1
#PBS -l select=1:ncpus=4:ngpus=1
#PBS -P PRJT1234
#PBS -l walltime=4:00:00
#PBS -m abe
#PBS -M your.email@address
 
cd /mnt/lustre/users/USERNAME/cuda_test
 
echo
echo `date`: executing CUDA job on host ${HOSTNAME}
echo
echo Available GPU devices: $CUDA_VISIBLE_DEVICES
echo
 
# Run program
./hello_cuda

As usual, replace PRJT1234 with your group's project name, your.email@address with your email address, and USERNAME with your cluster user name.

The Nvidia V100 GPUs are programmed using the CUDA development tools.

To build a CUDA code (library or application) for the GPU nodes requires loading the appropriate CUDA module before compiling. The current CUDA modules are:

chpc/cuda/11.2/PCIe/11.2
chpc/cuda/11.2/SXM2/11.2
chpc/cuda/11.5.1/PCIe/11.5.1
chpc/cuda/11.6/PCIe/11.6
chpc/cuda/11.6/SXM2/11.6
chpc/cuda/12.0/12.0

with version 12.0 the most recent version.

Note that the 11.x version modules are available in two types:

• the PCIe version is for PCIe bus nodes: gpu200x
• the SXM2 version is for the SXM2 bus nodes: gpu400x

• Detailed overview of the V100.
• Nvidia whitepaper on the Volta architecture.
• CUDA Toolkit Release Notes