Difference between revisions of "SOSCIP GPU"
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#SBATCH --ntasks=20 # MPI tasks (needed for srun) | #SBATCH --ntasks=20 # MPI tasks (needed for srun) | ||
#SBATCH --time=00:10:00 # H:M:S | #SBATCH --time=00:10:00 # H:M:S | ||
| − | + | #SBATCH --gres=gpu:4 # Ask for 4 GPUs per node | |
cd $SLURM_SUBMIT_DIR | cd $SLURM_SUBMIT_DIR | ||
Revision as of 14:35, 7 September 2017
| SOSCIP GPU | |
|---|---|
| Installed | September 2017 |
| Operating System | Ubuntu 16.04 le |
| Number of Nodes | 14x Power 8 with 4x NVIDIA P100 |
| Interconnect | Infiniband EDR |
| Ram/Node | 512 GB |
| Cores/Node | 2 x 10core (20 physical, 160 SMT) |
| Login/Devel Node | sgc01 |
| Vendor Compilers | xlc/xlf, nvcc |
SOSCIP
The SOSCIP GPU Cluster is a Southern Ontario Smart Computing Innovation Platform (SOSCIP) resource located at theUniversity of Toronto's SciNet HPC facility. The SOSCIP multi-university/industry consortium is funded by the Ontario Government and the Federal Economic Development Agency for Southern Ontario [1].
Specifications
The SOSCIP GPU Cluster consists of of 14 IBM Power 822LC "Minksy" Servers each with 2x10core 3.25GHz Power8 CPUs and 512GB Ram. Similar to Power 7, the Power 8 utilizes Simultaneous MultiThreading (SMT), but extends the design to 8 threads per core allowing the 20 physical cores to support up to 160 threads. Each node has 4x NVIDIA Tesla P100 GPUs each with 16GB of RAM with CUDA Capability 6.0 (Pascal) connected using NVlink.
Compile/Devel/Test
Access is provided through the BGQ login node, bgqdev.scinet.utoronto.ca using ssh, and from there you can proceed to the GPU development node sgc01.
Filesystem
The filesystem is shared with the BGQ system. See here for details.
Job Submission
The SOSCIP GPU cluster uses SLURM as a job scheduler and jobs are scheduled by node, ie 20 cores and 4 GPUs each. Jobs are submitted from the development node sgc01.
$ sbatch myjob.script
Where myjob.script is
#!/bin/bash #SBATCH --nodes=1 #SBATCH --ntasks=20 # MPI tasks (needed for srun) #SBATCH --time=00:10:00 # H:M:S #SBATCH --gres=gpu:4 # Ask for 4 GPUs per node cd $SLURM_SUBMIT_DIR hostname nvidia-smi
You can queury job information using
squeue
To cancel a job use
scancel $JOBID
Software
GNU Compilers
To load the newer advance toolchain version use:
module load gcc/6.3.1
IBM Compilers
To load the native IBM xlc/xlc++ compilers
module load xlc/13.1.5 module load xlf/15.1.5
Driver Version
The current NVIDIA driver version is 384.66
CUDA
The current installed CUDA Tookit is 8.0
module load cuda/8.0
The CUDA driver is installed locally, however the CUDA Toolkit is installed in:
/usr/local/cuda-8.0
OpenMPI
Currently OpenMPI has been setup on the 14 nodes connected over EDR Infiniband.
$ module load openmpi/2.1.1-gcc-5.4.0 $ module load openmpi/2.1.1-XL-13_15.1.5
OpenAI
The IBM powerAI framework is installed.