https://hpc.oit.uci.edu/gpu

HPC GPU Node:

Peak double PRecision floating point performance	665 Gigaflops
Peak single precision floating point performance	1331 Gigaflops
Memory bandwidth (ECC off)	177 GBytes/sec
Memory size (GDDR5)	6 GigaBytes
CUDA cores	512

The GPU node ( compute-1-14 ) has dual Intel Xeon DP E5645 2.4GHz 12MB cache (24 cores) CPUs with 96GB DDR3 1333Mhz of main memory.  There are a total of 2,048 CUDA cores with the 4 Tesla M2090 NVIDIA cards.When requesting GPU resources, please try requesting 6 Intel cores per each gpu card you request.  Since the node has 24 Intel cores, the division comes out to 6 Intel cores per each GPU card.    There are no fixed numbers when requesting cores verses GPU cards, it all depends on the running program.  If  you can run with 2 Intel cores and 2 GPU cards, then use those numbers.Consider the following CUDA script file is available at: ~demo/hello-cuda.sh    $ cat  ~demo/hello-cuda.sh

#$ -q gpu	Requesting the GPU queue.
#$ -l gpu=1	Requesting 1 gpu card out of 4 avilable gpu cards.
#$ -pe gpu-node-cores 6	Run with the Parallel Enviroment "gpu-node-core" requesting 6 node cores.

Let's run a cuda hello world example:$ mkdir cuda$ cd cuda$ cp ~demo/hello-cuda.sh  .$ qsub hello-cuda.sh$ qstatCheck the directory for the output "out" file and other files the script created.How many GPU's are available now?As mentioned above, the GPU compute-1-14 node has 4 GPU cards.    To see how many gpus are currently avaialble use:$ qhost -F gpu -h compute-1-14HOSTNAME           NCPU NSOC NCOR NTHR  LOAD  MEMTOT  MEMUSE  SWAPTO  SWAPUS--------------------------------------------------------------------------------compute-1-14        24    2   12   24  0.69   94.6G    1.8G   94.4G     0.0    Host Resource(s):      hc:gpu=4.000000GPU compute node compute-1-14 has 4 gpu's available.CUDA-CompilersCUDA compiler, debugger and libs are available with:    module load  nvidia-cuda/5.0CUDA Documentation:On the HPC cluster, you can get additional help files at /data/apps/cuda/doc  or by clicking on this link.The SDK CUDA Toolkit has been installed in /data/apps/cuda/NVIDIA_GPU_Computing_SDKCUDA SDK Toolkit Documentation is also available from this link.NVIDIA-SMITo display the GPU information, you can use the qrsh command as follows:$ qrsh -q gpu nvidia-smi  Fri Apr 19 10:10:01 2012       +------------------------------------------------------+                       | NVIDIA-SMI 3.295.41   Driver Version: 295.41         |                       |-------------------------------+----------------------+----------------------+| Nb.  Name                     | Bus Id        Disp.  | Volatile ECC SB / DB || Fan   Temp   Power Usage /Cap | Memory Usage         | GPU Util. Compute M. ||===============================+======================+======================|| 0.  Tesla M2090               | 0000:04:00.0  Off    |         0          0 ||  N/A    N/A  P0    77W / 225W |   6%  330MB / 5375MB |   31%     Default    ||-------------------------------+----------------------+----------------------|| 1.  Tesla M2090               | 0000:05:00.0  Off    |         0          0 ||  N/A    N/A  P12   29W / 225W |   0%   10MB / 5375MB |    0%     Default    ||-------------------------------+----------------------+----------------------|| 2.  Tesla M2090               | 0000:83:00.0  Off    |         0          0 ||  N/A    N/A  P12   27W / 225W |   0%   10MB / 5375MB |    0%     Default    ||-------------------------------+----------------------+----------------------|| 3.  Tesla M2090               | 0000:84:00.0  Off    |         0          0 ||  N/A    N/A  P12   28W / 225W |   0%   10MB / 5375MB |    0%     Default    ||-------------------------------+----------------------+----------------------|| Compute processes:                                               GPU Memory ||  GPU  PID     Process name                                       Usage      ||=============================================================================||  0.  13951    ...namd/NAMD_2.9b3_linux-x86_64-multicore-CUDA/namd2   317MB  |+-----------------------------------------------------------------------------+In the display above, Tesla #0 is active and has a load of 31%.   All other Tesla cards are idle ( 0% utilization ).You can get additional help for nvidia-smi on compute-1-14 with:nvidia-smi -hman nvidia-smi

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If you are familiar with using GPU and like to contributing to help others learn how to use the GPU node, please let me know and I will post in on the HPC How To list.