GPU Devel Nodes

GPU Development Cluster
Installed	June 2010
Operating System	Linux
Interconnect	Infiniband,GigE
Ram/Node	48 Gb
Cores/Node	8
Login/Devel Node	cell-srv01 (from login.scinet)
Vendor Compilers	gcc,nvcc

The Intel nodes have two 2.53GHz 4core Xeon X5550 CPU's with 48GB of RAM per node with 3 containing NVIDIA 9800GT GPUs.

Login

First login via ssh with your scinet account at login.scinet.utoronto.ca, and from there you can proceed to cell-srv01 which is currently the gateway machine.

Compile/Devel/Compute Nodes

Nehalem (x86_64)

You can log into any of 8 nodes cell-srv[01-08] directly however each of the nodes have differing configurations.

• cell-srv01 - login node & nfs server, GigE connected
• cell-srv[02-05] - no GPU, GigE connected
• cell-srv[06-07] - 1x NVIDIA 9800GT GPU, Infiniband connected
• cell-srv08 - 2x NVIDIA 9800GT GPU, GigE connected

Local Disk

This test cluster currently cannot see the global /home and /scratch space so you will have to copy (scp,etc..) your code to a separate local/home dedicated for this cluster. Also initially you will probably want to copy/modify your scinet .bashrc, .bashrc_profile, and .ssh directory onto this system.

Programming Frameworks

Currently there are two programming frameworks to use, NVIDIA's CUDA framework or OpenCL.

CUDA

/usr/local/cuda/

OpenCL

Demos, examples, and build details available in

Compilers

• nvcc -- Nvidia GCC compiler

MPI

Still a work in progress.

Documentation

• CUDA
  • http...

• OpenCL
  • http...

User Codes

Please discuss put any relevant information/problems/best practices you have encountered when using/developing for CUDA and/or OpenCL