CUDA入门01：安装配置测试

本文介绍CUDA的安装与配置。

首先，你要有一块英伟达显卡。

安装

打开CUDA官网选择平台下载新版本的安装包。Windows下是exe格式的，直接双击安装就行了。Linux下下载deb格式的离线包，防止因为网络问题在线安装失败。

打开CUDNN官网，登录账户，进入CUDNN下载工具需要下载指定版本。CUDNN是神经网络库，一般是结果动态链接库复制到CUDA的动态链接库目录就可以了。

Linux

Linux下还可以通过官方软件仓库安装。

ARM

ARM平台一般使用的是Ubuntu，那么操作和Linux下一样。如果不是，连NVIDIA官方的开发板都是Ubuntu for ARM，我也无能为力。

配置

所谓配置就是将可执行程序所在的目录添加到系统环境变量中（Windows平台不需要）。

测试

老版本在软件安装文件夹中有samples文件夹，在1_Utilities/deviceQuery文件夹中有测试代码，不需要理解，主要能编译运行就可以了。

新版本的CUDA示例代码被分开，放在cuda-samples Github上。

暗夜精灵5笔记本，CUDA驱动为10.2，英伟达驱动为440，cudnn为0.7，测试结果为：

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./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1660 Ti"
  CUDA Driver Version / Runtime Version          10.2 / 10.2
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 5942 MBytes (6230245376 bytes)
  (24) Multiprocessors, ( 64) CUDA Cores/MP:     1536 CUDA Cores
  GPU Max Clock rate:                            1590 MHz (1.59 GHz)
  Memory Clock rate:                             6001 Mhz
  Memory Bus Width:                              192-bit
  L2 Cache Size:                                 1572864 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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:  1024
  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 3 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:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  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 = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS