GPU Use

Resources

Two (2) NVIDIA Quadro K2200 (2014)

Access

Access can be requested through GLPI.

WARNING: the following instructions are no longer up to date

Basic use

Login

Then, the login is performed with a command

ssh lpnws5232.in2p3.fr

Environment configuration

WARNING: the rest of these instructions is no longer up to date To access to CUDA

. /usr/local/bin/cuda-setup.sh         # sh, bash

source /usr/local/bin/cuda-setup.csh   # csh, tcsh

Please note that GPU boards are also available on the Mesu-Beta of ICS.

How to begin

CUDA samples

To install CUDA samples :

cuda-install-samples-8.0.sh $destDir
cd $destDir
cuda-fix-samples.sh # to make them compatible with SL
cd NVIDIA_CUDA-8.0_Samples
make -j   # to compile everything

Then, one should find compiled codes in each subdir, organized by item :

SOMEWHERE/cuda-samples/NVIDIA_CUDA-8.0_Samples $ ls
0_Simple     2_Graphics  4_Finance      6_Advanced       bin     EULA.txt
1_Utilities  3_Imaging   5_Simulations  7_CUDALibraries  common  Makefile

Binaries are directly available at SOMEWHERE/cuda-samples/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/

Local examples

CUDA example

Based on the pi estimation :

basic code : pi_gpu.cu
better implemented code : pi_gpu_v2.cu
Multi-GPU version : pi_gpu_multiGPU.cu
Using thrust : pi_gpu_thrust.cu

Python examples

In order to use pythlon examples, one should install some python modules locally. The simpliest way is to use anaconda, which gives a local python environment, and install needed modules which does not require root privileges. The tool is available on the GPU machine. To proceed :

export ANACONDADIR="/usr/local/anaconda3-2.5.0"
export PATH="$ANACONDADIR/bin:$PATH"

Then, for samples, one need ~~to install~~ to use (a priori it's already available for all users — to be confirmed) the numba module which allows automatic optimization of the python code and uses the NVIDIA CUDA library for GPU programming :

conda install numba
conda install accelerate

Other informations

Available GPU boards

On the machine with GPU, there are two available boards, numbered 0 et 1. By default, if one use only one board, the 0th is used; it's an issue if all users are using the 0th board and none on the 1st.

Thus, the CUDA_VISIBLE_DEVICES variable is randomly set (actually not, it's done according to the session parity) to 0 or 1. If you wish to use 2 boards :

export CUDA_VISIBLE_DEVICES=0,1

unset CUDA_VISIBLE_DEVICES

Profiling CUDA code

It's done with the use of the Nvidia Visual Profiler (nvvp) tool.

Monitoring

In order to have a sort of top for GPUs :

nvidia-smi --loop=2

For more informations :

nvidia-smi -q -d MEMORY,UTILIZATION,TEMPERATURE,POWER,CLOCK,COMPUTE,PIDS,PERFORMANCE -l

Using OpenCL

OpenCL is a programming standard of GPU architectures based on C++ extensions. To use OpenCL :

include files are located at /usr/local/cuda/include/CL; one should use -I/usr/local/cuda/include option for compiling ;
the OpenCL library is located at /usr/lib64; then just add the -lOpenCL option for linking.

Some references on the web (mainly in french)

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