Power8 GPU Nodes
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Revision as of 14:14, 21 November 2019 by Cacwikiadmin (Talk | contribs) (Created page with "== IBM Power 8 GPU Cluster == == Hardware: == 5 - S822LC IBM Power 8 servers each with 4 - Nvidia P100 16GB GPUs 2 - 8 core sockets, 3 threads per core 512 GB memory...")
IBM Power 8 GPU Cluster
Hardware:
5 - S822LC IBM Power 8 servers each with 4 - Nvidia P100 16GB GPUs 2 - 8 core sockets, 3 threads per core 512 GB memory
Software:
Operating System: Ubuntu 18.04
CUDA/10.1 PGI/19.1
Lmod
Runs a local Lmod system not connected to the Compute Canada Lmod.
To find a software module use 'module spider <software-name>'
e.g. hpc1006@cac155: module spider cuda --------------------------------------------------------------------------------------- cuda: cuda/10.1 --------------------------------------------------------------------------------------- Description: CUDA (formerly Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce. CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. hpc1006@cac155: module load cpu/10.1 hpc1006@cac155: module list Currently Loaded Modules: 1) cuda/10.1
Miniconda:
It's recommended for users to install their choice of miniconda in their home directory.
e.g. Choose version here, https://repo.continuum.io/miniconda/ Note: it must be a ppcle version
>wget https://repo.continuum.io/miniconda/Miniconda2-4.7.12.1-Linux-ppc64le.sh >bash Miniconda3-4.7.12.1-Linux-x86_64.sh - Answer 'yes' to accept agreement- - Choose install directory, default is $HOME/miniconda3 but you may choose to install it in a custom directory for power pc to avoid conflicting with any X86 installs. e.g. mkdir $HOME/ppc and use $HOME/ppc/miniconda3 as your install directory - Answer 'yes' to running conda init if you want the miniconda/bin directory in your $PATH