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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.

Servers with GPU units

machine GPU; Capability [cc] cores GPU RAM Comment
titan GeForce GTX 1080 Ti; cc6.1 1 12 GB
titan-gpu GeForce GTX Titan Z; cc3.5 2 6 GB each core
twister1; twister2; kronos Tesla K40c; cc3.5 1 12 GB
iridium Quadro K2000; cc3.0 1 2 GB
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
dll1; dll2 GeForce GTX 1080; cc6.1 8 8 GB each core

not used at the moment: GeForce GTX 570 (from twister2)
All machines have CUDA8.0 and should support both Theano and TensorFlow.

Summary of future plans:

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:

How to use cluster

In this section will be explained how to use cluster properly.

TensorFlow Environment

Majority people at UFAL use TensorFlow. To start using it you need to create python virtual environment (virtualenv 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 tf1
source activate tf1cpu

This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey.

Using cluster

Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments.

For testing and using the cluster interactively you can use qrsh (this should not be used for long running experiments since the console is not closed on the end of the experiment). Following command will assign you a GPU and creates interactive console.

qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash

For running experiments you must use qsub command:

qsub -q gpu.q -l gpu=1,gpu_cc_min3.5=1,gpu_ram=2G WHAT_SHOULD_BE_RUN

Cleaner way to use cluster is with /home/bojar/tools/shell/qsubmit

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

It is recommended to use priority -100 if you are not rushing for the results and don't need to leap over your colleagues jobs.

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!)
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.

Select GPU device

Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use (GPU queue do this for you):


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.

machine Setup; CPU/GPU; Capability [cc] Walltime Note
athena GeForce GTX 1080; cc6.1 9:55:58 Tom's desktop
dll2 (2 GPU) GeForce GTX 1080; cc6.1 10:19:40 with CUDA_VISIBLE_DEVICES=0
titan GeForce GTX 1080 Ti 11:41:08
dll1 (2 GPU) GeForce GTX 1080; cc6.1 12:34:34 Probably only one GPU was used
dll2 (2 GPU) 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
victoria GeForce GT 630; cc3.0 never run, same GPU as Arc

GPU specs for those GPUs we have:

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