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This page summarizes which UFAL servers have some GPU card, and suggests basic diagnostic commands, paths to installed tools, etc., simply everything necessary at the very beginning of using GPUs for experiments.

TODO: IN 2022 MOVING FROM SGE TO SLURM (see slurm guidelines) – commands like qsub and qstat will stop working!

Servers with GPU units

GPU cluster gpu-ms.q at Malá Strana:

machine GPU type GPU driver version cc GPU cnt GPU RAM (GB) machine RAM (GB)
dll1 Quadro RTX 5000 455.23.05 7.5 8 16 366.2
dll3 NVIDIA RTX A4000 510.73.08 8.6 10 16 248.8
dll4 GeForce GTX 1080 Ti 455.23.05 6.1 10 11 248.8
dll5 GeForce GTX 1080 Ti 455.23.05 6.1 10 11 248.8
dll6 NVIDIA RTX A4000 510.73.08 8.6 8 16 248.8
dll7 NVIDIA RTX A4000 510.73.08 8.6 8 16 248.8
dll8 Quadro RTX 5000 455.23.05 7.5 8 16 366.2
dll9 GeForce RTX 3090 455.23.05 8.6 4 25 183.0
dll10 GeForce RTX 3090 455.23.05 8.6 4 25 183.0

GPU cluster gpu-troja.q at Troja:

machine GPU type GPU driver version cc GPU cnt GPU RAM (GB) machine RAM (GB)
tdll1 Quadro P5000 455.23.05 6.1 8 16 245.0
tdll2 Quadro P5000 455.23.05 6.1 8 16 245.0
tdll3 Quadro P5000 455.23.05 6.1 8 16 245.0
tdll4 Quadro P5000 455.23.05 6.1 8 16 245.0
tdll5 Quadro P5000 455.23.05 6.1 8 16 245.0

Desktop machines:

machine GPU type cc GPUs GPU RAM Comment
victoria; arc GeForce GT 630 cc3.0 1 2 GB desktop machine
athena GeForce GTX 1080 cc6.1 1 8 GB Tom's desktop machine

Multiple versions of CUDA library are accessible on each machine together with cudnn. Theano and TensorFlow is supported.

GPU usage rolling graphs


How to use cluster

Set-up CUDA and CUDNN

Multiple versions of cuda can be accessed in /opt/cuda.

You need to set library path from your ~/.bashrc:

if [ -d "$CUDA_DIR_OPT" ] ; then
  export THEANO_FLAGS="cuda.root=$CUDA_HOME,device=gpu,floatX=float32"
  export PATH=$PATH:$CUDA_DIR/bin
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_DIR/cudnn/$CUDNN_version/lib64:$CUDA_DIR/lib64
  export CPATH=$CUDA_DIR/cudnn/$CUDNN_version/include:$CPATH

TensorFlow Environment

Many people at UFAL use TensorFlow. To start using it it is recommended to create a Python virtual environment (or use Anaconda for it). Into the environment you must place TensorFlow. The TF is either in CPU or GPU version.

pip install tensorflow
pip install tensorflow-gpu

You can use prepared environment by adding into your ~/.bashrc

export PATH=/a/merkur3/kocmanek/ANACONDA/bin:$PATH

And then you can activate your environment:

source activate tf18
source activate tf18cpu

This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey.

PyTorch Environment

Install PyTorch following the instructions on https://pytorch.org.

At the time of writing, the recommended setup for CUDA 11.1 (supported by all GPU nodes) is:

 pip3 install torch==1.10.0+cu111 torchvision==0.11.1+cu111 torchaudio==0.10.0+cu111 -f https://download.pytorch.org/whl/cu111/torch_stable.html

Using cluster

As an alternative to qsub, you can use /home/bojar/tools/shell/qsubmit

qsubmit --gpumem=2G --queue="gpu-ms.q" WHAT_SHOULD_BE_RUN

It is recommended to use priority lower than the default -100 if you are not rushing for the results and don't need to leap over your colleagues jobs. Please, do not use priority between -99 to 0 for jobs taking longer than a few hours, unless it is absolutely necessary for your work.

Basic commands

  # is any such hardware there?
  # more details, incl. running processes on the GPU
  # nvidia-* are typically located in /usr/bin
watch nvidia-smi
  # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)
  # You can also use nvidia-smi -l TIME
nvcc --version
  # this should tell CUDA version
  # nvcc is typically installed in /usr/local/cuda/bin/
  # dela to vubec neco uzitecneho? :-)
  # theano-* are typically located in /usr/local/bin/
  # shows CUDA capability etc.
ssh dll1; ~popel/bin/gpu_allocations
  # who occupies which card on a given machine

Select GPU device

The variable CUDA_VISIBLE_DEVICES constrains tensorflow and other toolkits to compute only on the selected GPUs. Do not set this variable yourself (unless debugging SGE), it is set for you automatically by SGE if you ask for some GPUs (see above).

To list available devices, use:

/opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device

Performance tests

* 980 vs 1080 vs Titan X (not the Titan Z we have)

In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/Projects/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA). The benchmark uses 2GB model of seq2seq machine translation in Neural Monkey (De > EN). If not specified, the benchmark had an access only to one GPU.

machine Setup; CPU/GPU; Capability [cc] Walltime Note
athena GeForce GTX 1080; cc6.1 9:55:58 Tom's desktop
dll2 GeForce GTX 1080; cc6.1 10:19:40
titan GeForce GTX 1080 Ti 10:45:11 (new result with correct CUDA version)
dll1 (2 GPU) GeForce GTX 1080; cc6.1 12:34:34 Probably only one GPU was used
twister2 Quadro P5000 13:19:00
dll2 GeForce GTX 1080; cc6.1 13:01:05 Only one GPU was used
titan-gpu (2 GPU) GeForce GTX Titan Z; cc3.5 16:05:24 Probably only one GPU was used
kronos-dev Tesla K40c; cc3.5 22:41:01
twister2 Tesla K40c; cc3.5 22:43:10
twister1 Tesla K40c; cc3.5 24:19:45
helena1 16x cores CPU 46:33:14
belzebub 16x cores CPU 52:36:56
iridium Quadro K2000; cc3.0 59:47:58
helena7 8x cores CPU 60:39:17
arc GeForce GT 630; cc3.0 103:42:30 (approximated after 66 hours)
lucifer4 8x cores CPU 134:41:22

Second Benchmark

The previous benchmark only compares the speed of processing units within the GPUs and do not take into account the size of memory. Therefore I have conducted another benchmark, this time for each graphic card I have increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark model had batch size 20). This way the results should be more representative of the power for each GPU.

GPU; Cuda capability GPU RAM Walltime Batch size Machine
GeForce GTX 1080 Ti; cc6.1 11 GB 00:55:56 2300 dll5
GeForce GTX 1080; cc6.1 8 GB 01:10:57 1700 dll1
Quadro P5000 16 GB 01:17:00 3400 twister2
GeForce GTX Titan Z; cc3.5 6 GB 02:20:47 1100 titan-gpu
Quadro K2000; cc3.0 2 GB 28:15:26 50 iridium

GPU specs for those GPUs we have:

Individual acquisitions: NVIDIA Academic Hardware Grants

There is an easy way to get one high-end GPU: ask NVIDIA for an Academic Hardware Grant. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards (2017; check with Milan about this issue). If you want to have a look at an application, feel free to ask at hajicj@ufal.mff.cuni.cz :)

Take care, however, to coordinate the grant applications a little, so that not too many arrive from UFAL within a short time: these grants are explicitly not intended to build GPU clusters, they are “seeding” grants meant for researchers to try out GPUs (and fall in love with them, and buy a cluster later). If you are planning to submit the hardware grant, have submitted one, or have already been awarded one, please add yourself here.

Known NVIDIA Academic Hardware Grants:

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