本文介绍如何安装CUDA，以CUDA6.0为例介绍。
1、Installing the CUDA Toolkit onto your device for native CUDA development
Download the .deb file for the CUDA Toolkit for L4T either using a web browser on the device, or download on your PC then copy the file to your device using a USB flash stick or across the network. (Make sure you download the Toolkit for L4T and not the Toolkit for Ubuntu since that is for cross-compilation instead of native compilation).
On the device, install the .deb file and the CUDA Toolkit. eg:
cd ~/Downloads# Install the CUDA repo metadata that you downloaded manually for L4Tsudo dpkg -i cuda-repo-l4t-r19.2_6.0-42_armhf.deb# Download & install the actual CUDA Toolkit including the OpenGL toolkit from NVIDIA. (It only downloads around 15MB)sudo apt-get update# Install "cuda-toolkit-6-0" if you downloaded CUDA 6.0, or "cuda-toolkit-6-5" if you downloaded CUDA 6.5, etc.sudo apt-get install cuda-toolkit-6-0# Add yourself to the "video" group to allow access to the GPUsudo usermod -a -G video $USERAdd the 32-bit CUDA paths to your .bashrc login script, and start using it in your current console:
echo "# Add CUDA bin & library paths:" >> ~/.bashrcecho "export PATH=/usr/local/cuda/bin:$PATH" >> ~/.bashrcecho "export LD_LIBRARY_PATH=/usr/local/cuda/lib:$LD_LIBRARY_PATH" >> ~/.bashrcsource ~/.bashrcVerify that the CUDA Toolkit is installed on your device:
nvcc -V
2、Installing & running the CUDA samples (optional)
If you think you will write your own CUDA code or you want to see what CUDA can do, then follow this section to build & run all of the CUDA samples.
Install writeable copies of the CUDA samples to your device's home directory (it will create a "NVIDIA_CUDA-6.0_Samples" folder):
cuda-install-samples-6.0.sh /home/ubuntuBuild the CUDA samples (takes around 15 minutes on Jetson TK1):
cd ~/NVIDIA_CUDA-6.0_SamplesmakeRun some CUDA samples:
1_Utilities/deviceQuery/deviceQuery1_Utilities/bandwidthTest/bandwidthTestcd 0_Simple/matrixMul./matrixMulCUBLAScd ../..cd 0_Simple/simpleTexture./simpleTexturecd ../..cd 3_Imaging/convolutionSeparable./convolutionSeparablecd ../..cd 3_Imaging/convolutionTexture./convolutionTexturecd ../..
3、注意事项（some notes）
Note: Many of the CUDA samples use OpenGL GLX and open graphical windows. If you are running these programs through an SSH remote terminal, you can remotely display the windows on your desktop by typing "export DISPLAY=:0" and then executing the program. (This will only work if you are using a Linux/Unix machine or you run an X server such as the free "Xming" for Windows). eg:
export DISPLAY=:0cd ~/NVIDIA_CUDA-6.0_Samples/2_Graphics/simpleGL./simpleGLcd ~/NVIDIA_CUDA-6.0_Samples/3_Imaging/bicubicTexture./bicubicTexturecd ~/NVIDIA_CUDA-6.0_Samples/3_Imaging/bilateralFilter./bilateralFilterNote: the Optical Flow sample (HSOpticalFlow) and 3D stereo sample (stereoDisparity) take rougly 1 minute each to execute since they compare results with CPU code.
Some of the CUDA samples use other libraries such as OpenMP or MPI or OpenGL.
If you want to compile those samples then you'll need to install these toolkits like this:
(to be added)