ubuntu19.10安装cuda-10.1
1.安装N卡驱动：
打开ubuntu的软件和更新，设置N卡驱动
2.查看ubuntu显卡驱动
nvidia-smi
显示：
Sun Feb 23 06:41:41 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.50 Driver Version: 430.50 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce 940M Off | 00000000:01:00.0 Off | N/A |
| N/A 44C P0 N/A / N/A | 306MiB / 2004MiB | 28% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 982 G /usr/lib/xorg/Xorg 23MiB |
| 0 1390 G /usr/lib/xorg/Xorg 102MiB |
| 0 1613 G /usr/bin/gnome-shell 95MiB |
| 0 2036 G ...AAAAAAAAAAAAAAgAAAAAAAAA --shared-files 34MiB |
+-----------------------------------------------------------------------------+
可以看到
CUDA Version: 10.1
3.我们下载CUDA Version: 10.1
ubuntu系统下cuda的下载推荐XDM下载器
下载后，在安装包目录打开终端输入：
sudo dpkg -i cuda-repo-ubuntu1810-10-1-local-10.1.105-418.39_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-10-1-local-10.1.105-418.39/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda
4.至此安装包安装完成，接下来将cuda加入环境变量：
export PATH = /usr/local/cuda-10.1/bin:/usr/local/cuda-10.1/NsightCompute-2019.1${PATH:+:${PATH}}
5.打开安装目录/ usr / local / cuda- 10.1
输入nvcc -V,显示cuda版本:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Fri_Feb__8_19:08:17_PST_2019
Cuda compilation tools, release 10.1, V10.1.105
6.在编译样例目录/usr/local/cuda-10.1/samples输入make发现出错，出错显示：
make[1]: 进入目录“/usr/local/cuda-10.1/samples/0_Simple/simpleSeparateCompilation”
/usr/local/cuda-10.1/bin/nvcc -ccbin g++ -I../../common/inc -m64 -dc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o simpleDeviceLibrary.o -c simpleDeviceLibrary.cu
In file included from /usr/local/cuda-10.1/bin/../targets/x86_64-linux/include/cuda_runtime.h:83,
from <command-line>:
/usr/local/cuda-10.1/bin/../targets/x86_64-linux/include/crt/host_config.h:129:2: error: #error -- unsupported GNU version! gcc versions later than 8 are not supported!
129 | #error -- unsupported GNU version! gcc versions later than 8 are not supported!
| ^~~~~
make[1]: *** [Makefile:290：simpleDeviceLibrary.o] 错误 1
make[1]: 离开目录“/usr/local/cuda-10.1/samples/0_Simple/simpleSeparateCompilation”
make: *** [Makefile:51：0_Simple/simpleSeparateCompilation/Makefile.ph_build] 错误 2
重点在：
error -- unsupported GNU version! gcc versions later than 8 are not supported!
7.原因是ubuntu 19.10 gcc版本过高，所以我们需要使用低于8.0的gcc g++
为了解决这个问题，我们可以下载gcc-7 g+±7来结合 update-alternatives 对样例进行编译
首先安装gcc-7 g+±7
sudo apt-get install gcc-7 g++-7
安装完成后我们开始使用update-alternatives进行版本控制，语法如下：
update-alternatives --install <链接> <名称> <路径> <优先级>
对于gcc有，命令行末尾的数字表优先级，数字越大优先级越高
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 10
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 20
版本设置：
sudo update-alternatives --config gcc
显示：
有 2 个候选项可用于替换 gcc (提供 /usr/bin/gcc)。
选择 路径 优先级 状态
------------------------------------------------------------
* 0 /usr/bin/gcc-9 20 自动模式
1 /usr/bin/gcc-7 10 手动模式
2 /usr/bin/gcc-9 20 手动模式
要维持当前值[*]请按<回车键>，或者键入选择的编号：1（我选的1）
update-alternatives: 使用 /usr/bin/gcc-7 来在手动模式中提供 /usr/bin/gcc (gcc)
对于g++
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 10
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-9 20
版本设置：
sudo update-alternatives --config g++
显示：
有 2 个候选项可用于替换 g++ (提供 /usr/bin/g++)。
选择 路径 优先级 状态
------------------------------------------------------------
* 0 /usr/bin/g++-9 20 自动模式
1 /usr/bin/g++-7 10 手动模式
2 /usr/bin/g++-9 20 手动模式
要维持当前值[*]请按<回车键>，或者键入选择的编号：1（对的我摁的1,就是我）
update-alternatives: 使用 /usr/bin/g++-7 来在手动模式中提供 /usr/bin/g++ (g++)
8.将gcc与g++都切换成gcc-7 g+±7后就可以在样例目录下输入make编译啦
编译完成后，生成的bin文件夹里找到deviceQuery 那一级目录，输入
./deviceQuery
看到类似的即为成功安装：
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce 940M"
CUDA Driver Version / Runtime Version 10.1 / 10.1
CUDA Capability Major/Minor version number: 5.0
Total amount of global memory: 2004 MBytes (2101870592 bytes)
( 3) Multiprocessors, (128) CUDA Cores/MP: 384 CUDA Cores
GPU Max Clock rate: 1176 MHz (1.18 GHz)
Memory Clock rate: 1001 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 1048576 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 supports Compute Preemption: No
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.1, CUDA Runtime Version = 10.1, NumDevs = 1
Result = PASS
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