首先，我装的系统是Ubuntu 14.04.1。
1. 预检查
按照参考链接1中所示，检查系统。
执行命令：
:~$ lspci | grep -i nvidia
03:00.0 3D controller: NVIDIA Corporation GK110GL [Tesla K20c] (rev a1)
04:00.0 VGA compatible controller: NVIDIA Corporation GK106GL [Quadro K4000] (rev a1)
04:00.1 Audio device: NVIDIA Corporation GK106 HDMI Audio Controller (rev a1)
发现有K20和K4000两块GPU，还有一块Audio的应该是声卡。
然后，执行命令检查系统版本：
~$ uname -m && cat /etc/*release
x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=14.04
DISTRIB_CODENAME=trusty
DISTRIB_DESCRIPTION="Ubuntu 14.04.1 LTS"
NAME="Ubuntu"
VERSION="14.04.1 LTS, Trusty Tahr"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 14.04.1 LTS"
VERSION_ID="14.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
可以看到，机器是ubuntu14.04的版本。
然后，使用gcc --version检查gcc版本是否符合链接1中的要求：
~$ gcc --version
gcc (Ubuntu 4.8.2-19ubuntu1) 4.8.2
Copyright (C) 2013 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
检查完毕，就去nvidia的官网（参考链接3）上下载驱动，为下载的是ubuntu14.04的deb包。
2. 安装
Deb包安装较为简单，但是安装过程中提示不稳定，不过用着也没啥出错的地方。
先按照参考链接2安装必要的库。
sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev
还是按照官网上的流程来。
$ sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb
$ sudo apt-get update
$ sudo apt-get install cuda
可能需要下载较长时间，但是没关系，放在那等着就是。
没啥问题就算安装好了。
安装过程中提示：
*** Please reboot your computer and verify that the nvidia graphics driver is loaded. ***
*** If the driver fails to load, please use the NVIDIA graphics driver .run installer ***
*** to get into a stable state.
我没管，提示使用.run安装比较稳定，但我现在用着没问题。
3. 配置环境
我的系统是64位的，因此配置环境时在.bashrc中加入
$ export PATH=/usr/local/cuda-6.5/bin:$PATH
$ export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64:$LD_LIBRARY_PATH
配置完环境后，执行命令
~$ source .bashrc
使其立刻生效。
4. 安装sample
配置好环境后，可以执行如下命令：
$ cuda-install-samples-6.5.sh <dir>
这样，就将cuda的sample拷贝到dir文件夹下了。该命令只是一个拷贝操作。
然后进入该文件夹，执行make命令进行编译，编译时间较长，需要等待。
5. 验证安装是否成功
5.1. 驱动验证
首先，验证nvidia的驱动是否安装成功。
~$ cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module 340.29 Thu Jul 31 20:23:19 PDT 2014
GCC version: gcc version 4.8.2 (Ubuntu 4.8.2-19ubuntu1)
5.2. Toolkit验证
验证cuda toolkit是否成功。
~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2014 NVIDIA Corporation
Built on Thu_Jul_17_21:41:27_CDT_2014
Cuda compilation tools, release 6.5, V6.5.12
5.3. 设备识别
使用cuda sample已经编译好的deviceQuery来验证。deviceQuery在<cuda_sample_install_path>/bin/x_86_64/linux/release目录下。我的结果如下，检测出了两块GPU来。
~/install/NVIDIA_CUDA-6.5_Samples/bin/x86_64/linux/release$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 2 CUDA Capable device(s)
Device 0: "Tesla K20c"
CUDA Driver Version / Runtime Version 6.5 / 6.5
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 4800 MBytes (5032706048 bytes)
(13) Multiprocessors, (192) CUDA Cores/MP: 2496 CUDA Cores
GPU Clock rate: 706 MHz (0.71 GHz)
Memory Clock rate: 2600 Mhz
Memory Bus Width: 320-bit
L2 Cache Size: 1310720 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 3 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 1: "Quadro K4000"
CUDA Driver Version / Runtime Version 6.5 / 6.5
CUDA Capability Major/Minor version number: 3.0
Total amount of global memory: 3071 MBytes (3220504576 bytes)
( 4) Multiprocessors, (192) CUDA Cores/MP: 768 CUDA Cores
GPU Clock rate: 811 MHz (0.81 GHz)
Memory Clock rate: 2808 Mhz
Memory Bus Width: 192-bit
L2 Cache Size: 393216 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 4 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
> Peer access from Tesla K20c (GPU0) -> Quadro K4000 (GPU1) : No
> Peer access from Quadro K4000 (GPU1) -> Tesla K20c (GPU0) : No
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 2, Device0 = Tesla K20c, Device1 = Quadro K4000
Result = PASS
这样，cuda就安装成功了。
参考链接
1．http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux/index.html
3．https://developer.nvidia.com/cuda-downloads