Difference between revisions of "GPU Guide"
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<code>gpu</code>, <code>gpu_v100</code>, <code>gpu_gtx</code>, and <code>gpu_rtx</code>. | <code>gpu</code>, <code>gpu_v100</code>, <code>gpu_gtx</code>, and <code>gpu_rtx</code>. | ||
Each partition has the type of GPU cards shown | Each partition has the type of GPU cards shown | ||
| − | [[ | + | [[HPC_Getting_Started#GPU_Cluster_Nodes|here]]. |
Compute jobs submitted to these partitions can run for up to twelve | Compute jobs submitted to these partitions can run for up to twelve | ||
| Line 21: | Line 21: | ||
== GPU V100 Nodes == | == GPU V100 Nodes == | ||
| − | A list of our GPU nodes is [[ | + | A list of our GPU nodes is [[HPC_Getting_Started#GPU_Cluster_Nodes|here]]. |
| − | However, our | + | However, our V100 nodes come in a few varieties. |
| − | Here are number of | + | Here are number of V100 cards and the memory on each card for our V100 nodes. |
{| class="wikitable" | {| class="wikitable" | ||
| Line 50: | Line 50: | ||
|} | |} | ||
| + | |||
| + | ==GPU Debug Partition== | ||
| + | We also have a GPU partition for debugging GPU jobs, <code>gpu_debug</code>. This partition is available to everyone and | ||
| + | has a high priority (though not quite as high as the standard Priority partitions) so your | ||
| + | debug jobs can start faster. However, these jobs are limited to one node and a 30 minute run time. | ||
| + | |||
| + | This debug partition applies to all four of our GPU types, so you'll need to use the following | ||
| + | '''--exclude=''' parameter in your SBATCH file to direct your job to the correct GPU family. | ||
| + | See the next section, [[Batch Jobs]], for how to use these parameters. | ||
| + | |||
| + | {| class='wikitable' | ||
| + | |- | ||
| + | ! GPU Type !! Exclude Line | ||
| + | |- | ||
| + | | K40m || --exclude=gpu[03-11],gtx[01-16] | ||
| + | |- | ||
| + | | V100 || --exclude=gpu[01-02],gtx[01-16] | ||
| + | |- | ||
| + | | GTX || --exclude=gpu[01-11],gtx[12-16] | ||
| + | |- | ||
| + | | RTX || --exclude=gpu[01-11],gtx[01-11] | ||
| + | |} | ||
==Batch Jobs== | ==Batch Jobs== | ||
| Line 59: | Line 81: | ||
#!/bin/bash | #!/bin/bash | ||
#SBATCH --partition='''GPU_PARTITION''' | #SBATCH --partition='''GPU_PARTITION''' | ||
| − | #SBATCH --gres=''' | + | #SBATCH --gres=gpu:'''NUM_GPUS''' |
#SBATCH --time='''04:00:00''' | #SBATCH --time='''04:00:00''' | ||
| − | + | ||
{COMMAND} | {COMMAND} | ||
| − | Select the value for | + | Select the value for <code>GPU_PARTITION</code> from |
| − | [[wiki.hpc.uconn.edu/index.php/HPC_Getting_Started#GPU_Cluster_Node|this table]]. | + | [[wiki.hpc.uconn.edu/index.php/HPC_Getting_Started#GPU_Cluster_Node|this table]], or use <code>gpu_debug</code>. |
| − | + | Set <code>NUM_GPUS</code> to the number of GPUs that you want to use, according to the table linked above. | |
| − | + | Also, if you are using the <code>gpu_debug</code> partition, you might want to include one | |
| + | of the '--exclude' commands shown in the previous section. | ||
| − | + | In this example, we set the job's time limit to 4 hours (04:00:00). | |
| − | + | ''Note that for <code>gpu_debug</code>, any time limit greater than 30 minutes (00:30:00) will cause your job submission to fail, | |
| + | since that partition is limited to 30 minutes.'' | ||
| + | Then, submit the script to the job scheduler: | ||
| + | sbatch gpu.sh | ||
==Interactive Jobs== | ==Interactive Jobs== | ||
Assign [[HPC_Getting_Started#Overview_of_cluster_nodes|one of the GPU nodes]] using <code>fisbatch</code>: | Assign [[HPC_Getting_Started#Overview_of_cluster_nodes|one of the GPU nodes]] using <code>fisbatch</code>: | ||
module load cuda/8.0 | module load cuda/8.0 | ||
| − | fisbatch --partition=gpu -c <numprocs> | + | fisbatch --partition=gpu -c <numprocs> --gres=gpu:'''NUM_GPUS''' |
| − | Where <code> | + | Where <code>NUM_GPUS</code> needs to be replaced by the number of CPU processors you need, and <code>NUM_GPUS</code> needs to be replaced by the number of GPUs you need. |
Then run the <code>deviceQuery</code> sample program to get useful information about the GPU information like memory, processor cores, etc: | Then run the <code>deviceQuery</code> sample program to get useful information about the GPU information like memory, processor cores, etc: | ||
Latest revision as of 12:04, 7 April 2020
Our GPU nodes are available to all users. They are separated into four different partitions:
gpu, gpu_v100, gpu_gtx, and gpu_rtx.
Each partition has the type of GPU cards shown
here.
Compute jobs submitted to these partitions can run for up to twelve hours.
Basic Information
For most work on the GPU nodes, you'll first need to load a CUDA module.
$ module load cuda
This will load the default CUDA module cuda/10.2-rhel7.7.
You can obtain the full list of avilable modules as follows:
$ module avail cuda
To compile with CUDA using the NVidia CUDA compiler:
$ nvcc {MYFILE.cu} -o {OUTPUT_FILE}
GPU V100 Nodes
A list of our GPU nodes is here.
However, our V100 nodes come in a few varieties. Here are number of V100 cards and the memory on each card for our V100 nodes.
| NodeName | Number of GPU | Memory On GPU |
|---|---|---|
| gpu03 | 3 | 16GB |
| gpu04 | 3 | 16GB |
| gpu05 | 3 | 16GB |
| gpu06 | 1 | 16GB |
| gpu07 | 1 | 16GB |
| gpu08 | 1 | 16GB |
| gpu09 | 3 | 32GB |
| gpu10 | 3 | 32GB |
| gpu11 | 1 | 16GB |
GPU Debug Partition
We also have a GPU partition for debugging GPU jobs, gpu_debug. This partition is available to everyone and
has a high priority (though not quite as high as the standard Priority partitions) so your
debug jobs can start faster. However, these jobs are limited to one node and a 30 minute run time.
This debug partition applies to all four of our GPU types, so you'll need to use the following --exclude= parameter in your SBATCH file to direct your job to the correct GPU family. See the next section, Batch Jobs, for how to use these parameters.
| GPU Type | Exclude Line |
|---|---|
| K40m | --exclude=gpu[03-11],gtx[01-16] |
| V100 | --exclude=gpu[01-02],gtx[01-16] |
| GTX | --exclude=gpu[01-11],gtx[12-16] |
| RTX | --exclude=gpu[01-11],gtx[01-11] |
Batch Jobs
The script below serves as an example for submitting a job to the scheduler to use a single GPU card, for up to four hours. You should change the variables in bold to meet your needs.
#!/bin/bash #SBATCH --partition=GPU_PARTITION #SBATCH --gres=gpu:NUM_GPUS #SBATCH --time=04:00:00
{COMMAND}
Select the value for GPU_PARTITION from
this table, or use gpu_debug.
Set NUM_GPUS to the number of GPUs that you want to use, according to the table linked above.
Also, if you are using the gpu_debug partition, you might want to include one
of the '--exclude' commands shown in the previous section.
In this example, we set the job's time limit to 4 hours (04:00:00).
Note that for gpu_debug, any time limit greater than 30 minutes (00:30:00) will cause your job submission to fail,
since that partition is limited to 30 minutes.
Then, submit the script to the job scheduler:
sbatch gpu.sh
Interactive Jobs
Assign one of the GPU nodes using fisbatch:
module load cuda/8.0 fisbatch --partition=gpu -c <numprocs> --gres=gpu:NUM_GPUS
Where NUM_GPUS needs to be replaced by the number of CPU processors you need, and NUM_GPUS needs to be replaced by the number of GPUs you need.
Then run the deviceQuery sample program to get useful information about the GPU information like memory, processor cores, etc:
$ /apps2/cuda/8.0/samples/1_Utilities/deviceQuery/deviceQuery
/apps2/cuda/8.0/samples/1_Utilities/deviceQuery/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Tesla K40m"
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 11440 MBytes (11995578368 bytes)
(15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores
GPU Max Clock rate: 745 MHz (0.75 GHz)
Memory Clock rate: 3004 Mhz
Memory Bus Width: 384-bit
L2 Cache Size: 1572864 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 3 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = Tesla K40m
Result = PASS
Then, exist your interactive session.
$ exit [screen is terminating] Connection to gpu01 closed. FISBATCH -- exiting job
