HPC GPU Node:
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https://hpc.oit.uci.edu/gpu
HPC GPU Node:
NVIDIA Corporation has graciously donated four (4) of their top high-end Tesla M2090 GPU cards to the HPC Cluster at UCI for your research needs.
Each NVIDIA Tesla M2090 card has the following attributes:
Each NVIDIA Tesla M2090 card has the following attributes:
Peak double precision floating point performance665 GigaflopsPeak single precision floating point performance1331 GigaflopsMemory bandwidth (ECC off)177 GBytes/secMemory size (GDDR5)6 GigaBytesCUDA cores512
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
$ qstat
$ cd cuda
$ cp ~demo/hello-cuda.sh .
$ qsub hello-cuda.sh
$ qstat
Check 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-1-14
HOSTNAME 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=4.000000
GPU compute node compute-1-14 has 4 gpu's available.HOSTNAME 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=4.000000
CUDA-Compilers
CUDA compiler, debugger and libs are available with:
module load nvidia-cuda/5.0
module load nvidia-cuda/5.0
CUDA 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_SDK
CUDA SDK Toolkit Documentation is also available from this link.
The SDK CUDA Toolkit has been installed in /data/apps/cuda/NVIDIA_GPU_Computing_SDK
CUDA SDK Toolkit Documentation is also available from this link.
NVIDIA-SMI
To 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 |
+-----------------------------------------------------------------------------+
+------------------------------------------------------+
| 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:
You can get additional help for nvidia-smi on compute-1-14 with:
- nvidia-smi -h
- man nvidia-smi
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.
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