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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 Cuda Theano TensorFlow Comment
titan-gpu GeForce GTX Titan Z; cc3.5 2 6 GB each core V8.0 works works
twister1 Tesla K40c; cc3.5 1 12 GB V8.0 ? works
twister2 Tesla K40c; cc3.5 1 12 GB V8.0 ? works
kronos-dev Tesla K40c; cc3.5 1 12 GB V8.0 0.6 0.8 works
iridium Quadro K2000; cc3.0 1 2 GB worked with V6.5.12 worked with 0.6.0 had ImportError: libcudart.so.7.5 (should work though)
victoria GeForce GT 630; cc3.0 1 2 GB V7.5 0.7.0 0.8 works Ondrej Bojar's desktop machine
arc GeForce GT 630; cc3.0 1 2 GB V7.5 0.7.0 0.8 works Ales's desktop machine
athena GeForce GTX 1080; cc6.1 1 8 GB V8.0 ? ? Tom's desktop machine

not used at the moment: GeForce GTX 570 (from twister2)

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. 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:

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/GPUBenchmark

machine GPU; Capability [cc] CUDA Walltime
titan-gpu GeForce GTX Titan Z; cc3.5
twister1 Tesla K40c; cc3.5
twister2 Tesla K40c; cc3.5
kronos-dev Tesla K40c; cc3.5
iridium Quadro K2000; cc3.0
victoria GeForce GT 630; cc3.0
arc GeForce GT 630; cc3.0
athena GeForce GTX 1080; cc6.1

Installed toolkits

This should mention where each interesting toolkit lives (on a particular machine).


This script installs TensorFlow 0.7.1 (and all other dependencies we need for Multimodal Translation) into `tf' and `tf-gpu' virtual environments. The GPU environment can be loaded by calling

source tf-gpu/bin/activate-gpu

OP: I created script which install Tensorflow 0.8 and test it if it uses GPU. TF is installed into `user` or `global` installation either for `python3.4` or `python2.7`

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:


To list available devices, use:

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

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.

GPU specs for those GPUs we have:

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