当前位置： 首页 > news >正文

Ubuntu22.04安装CUDA深度学习环境cuda principle

news 来源：原创 2024/5/10 16:40:08

environment:

neofetch && uname -a|lolcat

install nvidia GPU driver:

sudo add-apt-repository ppa:graphics-drivers/ppa  # 加入官方ppa源
sudo apt update  # 检查软件包更新列表
apt list --upgradable  # 查看可更新的软件包列表
sudo apt upgrade  # 更新所有可更新的软件包
ubuntu-drivers devices    # ubuntu检测n卡的可选驱动
sudo apt install nvidia-driver-510  # 根据自己的n卡可选驱动下载显卡驱动

ubuntu-drivers devices    # ubuntu检测n卡的可选驱动
sudo apt install nvidia-driver-510  # 根据自己的n卡可选驱动下载显卡驱动

disable the nouveau

disable the nouveau by add the nouveau to the black list.

 /etc/modprobe.d/blacklist.conf

最后一行加上: blacklist nouveau

and execute:

$ sudo update-initramfs -u
$ reboot

 reboot the system and execute the nvidia-smi:

the output of cuda does not mean the cuda environment already been installed, it just meas the corrspoinding versions of cuda that this driver supports.

sudo nvidia-settings  # 更改Nvidia驱动设置
nvidia-smi  # 查看显卡基本信息

install cuda:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda

nvcc:

add environment in bash shell

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.7/lib64
export PATH=$PATH:/usr/local/cuda-11.7/bin
export CUDA_HOME=$CUDA_HOME:/usr/local/cuda-11.7

test,printf on device:

#include <cuda_runtime.h>
#include <stdio.h>
 
__device__ void add(void)
{
	printf("kernel %s line %d, i am in kernel thread in block %d.\n", __func__, __LINE__,blockIdx.x);
}

__global__ void myfirstkernel(void)
{
	printf("kernel %s line %d, i am in kernel thread in block %d.\n", __func__, __LINE__,blockIdx.x);
	add();
}
 
int main(void)
{
	myfirstkernel <<<16,1>>>();

	cudaDeviceSynchronize();

	printf("exit.\n");
 
	return 0;
}

algo cuda sample:

#include <cuda_runtime.h>
#include <stdio.h>
 
__device__ void add(int a, int b, int *c)
{
	*c = a + b;
	printf("kernel %s line %d, i am in kernel thread in block %d. *c = %d.\n", __func__, __LINE__,blockIdx.x, *c);
}

__global__ void myfirstkernel(int a, int b, int *c)
{
	printf("kernel %s line %d, i am in kernel thread in block %d.\n", __func__, __LINE__,blockIdx.x);
	add(a, b, c);
}
 
int main(void)
{
	int c;
	int *gpu_c;

	cudaMalloc((void **)&gpu_c, sizeof(int));
 
	myfirstkernel <<<16,1>>>(3, 6, gpu_c);

	cudaMemcpy(&c, gpu_c, sizeof(int), cudaMemcpyDeviceToHost);

	cudaFree(gpu_c);

	cudaDeviceSynchronize();

	printf("exit.c = %d.\n", c);
 
	return 0;
}

change thread and block

#include <cuda_runtime.h>
#include <stdio.h>
 
__device__ void add(int a, int b, int *c)
{
	*c = a + b;
	printf("kernel %s line %d, i am in kernel thread %d in block %d. *c = %d.\n", __func__, __LINE__, threadIdx.x, blockIdx.x, *c);
}

__global__ void myfirstkernel(int a, int b, int *c)
{
	printf("kernel %s line %d, i am in kernel thread %d in block %d.\n", __func__, __LINE__,threadIdx.x, blockIdx.x);
	add(a, b, c);
}
 
int main(void)
{
	int c;
	int *gpu_c;

	cudaMalloc((void **)&gpu_c, sizeof(int));
 
	myfirstkernel <<<1,16>>>(3, 6, gpu_c);

	cudaMemcpy(&c, gpu_c, sizeof(int), cudaMemcpyDeviceToHost);

	cudaFree(gpu_c);

	cudaDeviceSynchronize();

	printf("exit.c = %d.\n", c);
 
	return 0;
}

#include <cuda_runtime.h>
#include <stdio.h>
 
__device__ void add(int a, int b, int *c)
{
	*c = a + b;
	printf("kernel %s line %d, i am in kernel thread %d in block %d. *c = %d.\n", __func__, __LINE__, threadIdx.x, blockIdx.x, *c);
}

__global__ void myfirstkernel(int a, int b, int *c)
{
	printf("kernel %s line %d, i am in kernel thread %d in block %d.\n", __func__, __LINE__,threadIdx.x, blockIdx.x);
	printf("block.x = %d, block.y = %d,block.z = %d\n", blockDim.x, blockDim.y,blockDim.z);
	printf("thread.x = %d, thread.y = %d,thread.z = %d\n", threadIdx.x, threadIdx.y,threadIdx.z);
	add(a, b, c);
}
 
int main(void)
{
	int c;
	int *gpu_c;

	cudaMalloc((void **)&gpu_c, sizeof(int));
 
	myfirstkernel <<<3,16>>>(3, 6, gpu_c);

	cudaMemcpy(&c, gpu_c, sizeof(int), cudaMemcpyDeviceToHost);

	cudaFree(gpu_c);

	cudaDeviceSynchronize();

	printf("exit.c = %d.\n", c);
 
	return 0;
}
czl@czl-RedmiBook-14:~/workspace/work$ ./a.out 
kernel myfirstkernel line 12, i am in kernel thread 0 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 1 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 2 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 3 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 4 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 5 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 6 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 7 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 8 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 9 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 10 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 11 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 12 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 13 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 14 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 15 in block 0.
kernel myfirstkernel line 12, i am in kernel thread 0 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 1 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 2 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 3 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 4 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 5 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 6 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 7 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 8 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 9 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 10 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 11 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 12 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 13 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 14 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 15 in block 1.
kernel myfirstkernel line 12, i am in kernel thread 0 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 1 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 2 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 3 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 4 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 5 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 6 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 7 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 8 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 9 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 10 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 11 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 12 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 13 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 14 in block 2.
kernel myfirstkernel line 12, i am in kernel thread 15 in block 2.
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
block.x = 16, block.y = 1,block.z = 1
thread.x = 0, thread.y = 0,thread.z = 0
thread.x = 1, thread.y = 0,thread.z = 0
thread.x = 2, thread.y = 0,thread.z = 0
thread.x = 3, thread.y = 0,thread.z = 0
thread.x = 4, thread.y = 0,thread.z = 0
thread.x = 5, thread.y = 0,thread.z = 0
thread.x = 6, thread.y = 0,thread.z = 0
thread.x = 7, thread.y = 0,thread.z = 0
thread.x = 8, thread.y = 0,thread.z = 0
thread.x = 9, thread.y = 0,thread.z = 0
thread.x = 10, thread.y = 0,thread.z = 0
thread.x = 11, thread.y = 0,thread.z = 0
thread.x = 12, thread.y = 0,thread.z = 0
thread.x = 13, thread.y = 0,thread.z = 0
thread.x = 14, thread.y = 0,thread.z = 0
thread.x = 15, thread.y = 0,thread.z = 0
thread.x = 0, thread.y = 0,thread.z = 0
thread.x = 1, thread.y = 0,thread.z = 0
thread.x = 2, thread.y = 0,thread.z = 0
thread.x = 3, thread.y = 0,thread.z = 0
thread.x = 4, thread.y = 0,thread.z = 0
thread.x = 5, thread.y = 0,thread.z = 0
thread.x = 6, thread.y = 0,thread.z = 0
thread.x = 7, thread.y = 0,thread.z = 0
thread.x = 8, thread.y = 0,thread.z = 0
thread.x = 9, thread.y = 0,thread.z = 0
thread.x = 10, thread.y = 0,thread.z = 0
thread.x = 11, thread.y = 0,thread.z = 0
thread.x = 12, thread.y = 0,thread.z = 0
thread.x = 13, thread.y = 0,thread.z = 0
thread.x = 14, thread.y = 0,thread.z = 0
thread.x = 15, thread.y = 0,thread.z = 0
thread.x = 0, thread.y = 0,thread.z = 0
thread.x = 1, thread.y = 0,thread.z = 0
thread.x = 2, thread.y = 0,thread.z = 0
thread.x = 3, thread.y = 0,thread.z = 0
thread.x = 4, thread.y = 0,thread.z = 0
thread.x = 5, thread.y = 0,thread.z = 0
thread.x = 6, thread.y = 0,thread.z = 0
thread.x = 7, thread.y = 0,thread.z = 0
thread.x = 8, thread.y = 0,thread.z = 0
thread.x = 9, thread.y = 0,thread.z = 0
thread.x = 10, thread.y = 0,thread.z = 0
thread.x = 11, thread.y = 0,thread.z = 0
thread.x = 12, thread.y = 0,thread.z = 0
thread.x = 13, thread.y = 0,thread.z = 0
thread.x = 14, thread.y = 0,thread.z = 0
thread.x = 15, thread.y = 0,thread.z = 0
kernel add line 7, i am in kernel thread 0 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 1 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 2 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 3 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 4 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 5 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 6 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 7 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 8 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 9 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 10 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 11 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 12 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 13 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 14 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 15 in block 0. *c = 9.
kernel add line 7, i am in kernel thread 0 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 1 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 2 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 3 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 4 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 5 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 6 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 7 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 8 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 9 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 10 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 11 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 12 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 13 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 14 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 15 in block 2. *c = 9.
kernel add line 7, i am in kernel thread 0 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 1 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 2 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 3 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 4 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 5 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 6 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 7 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 8 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 9 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 10 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 11 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 12 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 13 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 14 in block 1. *c = 9.
kernel add line 7, i am in kernel thread 15 in block 1. *c = 9.
exit.c = 9.
czl@czl-RedmiBook-14:~/workspace/work$

gridDim.x/gridDim.y/gridDim.z

#include <cuda_runtime.h>
#include <stdio.h>
 
__device__ void add(int a, int b, int *c)
{
	*c = a + b;
	printf("kernel %s line %d, i am in kernel thread %d in blockidx.x %d. blokidx.y %d blockidx.z %d *c = %d.\n", __func__, __LINE__, threadIdx.x, blockIdx.x, blockIdx.y,blockIdx.z,*c);
}

__global__ void myfirstkernel(int a, int b, int *c)
{
	printf("kernel %s line %d, i am in kernel thread %d in block %d.\n", __func__, __LINE__,threadIdx.x, blockIdx.x);
	printf("block.x = %d, block.y = %d,block.z = %d\n", blockDim.x, blockDim.y,blockDim.z);
	printf("thread.x = %d, thread.y = %d,thread.z = %d\n", threadIdx.x, threadIdx.y,threadIdx.z);
	printf("gridDim.x = %d, gridDim.y = %d,gridDim.z = %d\n", gridDim.x, gridDim.y,gridDim.z);
	add(a, b, c);
}
 
int main(void)
{
	int c;
	int *gpu_c;

	cudaMalloc((void **)&gpu_c, sizeof(int));
 
	myfirstkernel <<<2,3>>>(3, 6, gpu_c);

	cudaMemcpy(&c, gpu_c, sizeof(int), cudaMemcpyDeviceToHost);

	cudaFree(gpu_c);

	cudaDeviceSynchronize();

	printf("exit.c = %d.\n", c);
 
	return 0;
}

kernel call convontion:

kernel call invocation convotional is:

dim3 gridSize(3,2,1);

dim3 blockSize(2,2,2);

my_first_kernel<<<gridSize, blockSize>>>(para1,para2,...);

主机函数在声明的时候可以带有限定符__host__,全局函数在声明时必须带有限定符__global__.如果声明的函数没有限定符，则系统将认为其是主机函数，限定符写在返回类型之前。从上面的程序可以观察到，在调用全剧函数时除了函数名和形参表外，还有一个用三个小于号"<"和三个大于号">"包含的部分，这一部分用来指定在并行计算时使用的线程组数和每一个线程组包含的线程数。CUDA中将每一个线程组称为一个BLOCK，每个BLOCK由若干线程组成，而完成一次函数调用的所有BLOCK组成了一个grid.

在使用时，BLOCK和GRID的尺寸都可以用三元向量来表示，这表明BLOCK和GRID都是三维数组，BLOCK的元素是线程，而GRID的数组元素是BLOCK，在当前CUDA的计算能力下，BLOCK和GRID的维数和各维数的尺寸都有限制。

那么执行的线程是如何知道自己在在GRID，BLOCK，THREAD中的位置的呢？一种更简单的方案是让每个线程把自己的X索引（也就是threadIdx.x)记录下来，线程索引是线程在每个BLOCK里的索引，由于BLOCK的尺寸是三维的，因此线程索引也是一个三元常向量，threadIdx,访问方式为：threadIdx.x, threadIdx.y, threadIdx.z.对于一个BLOCK来说，它其中的每个线程的索引是唯一的，但是当一个GRID中有两个以上的BLOCK时，其中就会出现重复的线程索引，相应的，每个GRID里面的BLOCK也有唯一的BLOCK索引，用blockIdx表示，它同样是一个三维喜爱常向量，blockIdx.x, blockIdx.y, blockIdx.z。由于一次函数调用中只有一个GRID，因此不存在GRID索引。

对于BLOCK和GRID的尺寸，也用相应的三维常向量来表示，BLOCK的尺寸保存在常向量blockDim中，GRID的尺寸保存在gridDim中，他们都是CUDA C的内建变量，可以直接在设备代码中使用，在计算中，用户常常要给每个线程一个唯一的标识符，即线程号，以便给每个线程分配不同的任务。在多个BLOCK的情况下，线程号也不能重复。线程号在实际使用中很重要，它关系到被处理的数据在输入数组中的位置，也关系到线程的分配和存储器的使用问题。

当BLOCK或者GRID是多维的时候，该如何计算线程号呢？分别说明：

1D grid && 1d block.

__device__ int get_globalidx_1d_1d(void)
{
	return blockIdx.x * blockDim.x + threadIdx.x;
}

1D grid && 2d block.

__device__ int get_globalidx_1d_2d(void)
{
	return blockIdx.x * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
}

1d grid && 3d block

__device__ int get_globalidx_1d_3d(void)
{
	return blockIdx.x * blockDim.x * blockDim.y * blockDim.z + threadIdx.z * blockDim.y * blockDim.x + threadIdx.y * blockDim.x + threadIdx.x;
}

2d grid && 1d block

__device__ int get_globalidx_2d_1d(void)
{
	int blockid = blockIdx.y * gridDim.x + blockIdx.x;
	int threadid = blockid * blockDim.x + threadIdx.x;

	return threadid;
}

2d grid && 2d block

__device__ int get_globalidx_2d_2d(void)
{
	int blockid = blockIdx.y * gridDim.x + blockIdx.x;
	int threadid = blockid * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

2d grid && 3d block

__device__ int get_globalidx_2d_3d(void)
{
	int blockid = blockIdx.y * gridDim.x + blockIdx.x;
	int threadid = blockid * (blockDim.x * blockDim.y * blockDim.z) + (threadIdx.z * (blockDim.x * blockDim.y)) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

3d grid && 1d block

__device__ int get_globalidx_3d_1d(void)
{
	int blockid = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
	int threadid = blockid * blockDim.x + threadIdx.x;

	return threadid;
}

3d grid && 2d block

__device__ int get_globalidx_3d_2d(void)
{
	int blockid = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
	int threadid = blockid * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

3d grid && 3d block

__device__ int get_globalidx_3d_3d(void)
{
	int blockid = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
	int threadid = blockid * (blockDim.x * blockDim.y * blockDim.z) + (threadIdx.z * (blockDim.x * blockDim.y)) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

code:

#include <cuda_runtime.h>
#include <stdio.h>
 
__device__ void add(int a, int b, int *c)
{
	*c = a + b;
	printf("kernel %s line %d, i am in kernel thread %d in blockidx.x %d. blokidx.y %d blockidx.z %d *c = %d.\n", __func__, __LINE__, threadIdx.x, blockIdx.x, blockIdx.y,blockIdx.z,*c);
}

__global__ void myfirstkernel(int a, int b, int *c)
{
	printf("kernel %s line %d, i am in kernel thread %d in block %d.\n", __func__, __LINE__,threadIdx.x, blockIdx.x);
	printf("block.x = %d, block.y = %d,block.z = %d\n", blockDim.x, blockDim.y,blockDim.z);
	printf("thread.x = %d, thread.y = %d,thread.z = %d\n", threadIdx.x, threadIdx.y,threadIdx.z);
	printf("gridDim.x = %d, gridDim.y = %d,gridDim.z = %d\n", gridDim.x, gridDim.y,gridDim.z);
	add(a, b, c);
}

__device__ int get_globalidx_1d_1d(void)
{
	return blockIdx.x * blockDim.x + threadIdx.x;
}

__device__ int get_globalidx_1d_2d(void)
{
	return blockIdx.x * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
}

__device__ int get_globalidx_1d_3d(void)
{
	return blockIdx.x * blockDim.x * blockDim.y * blockDim.z + threadIdx.z * blockDim.y * blockDim.x + threadIdx.y * blockDim.x + threadIdx.x;
}

__device__ int get_globalidx_2d_1d(void)
{
	int blockid = blockIdx.y * gridDim.x + blockIdx.x;
	int threadid = blockid * blockDim.x + threadIdx.x;

	return threadid;
}

__device__ int get_globalidx_2d_2d(void)
{
	int blockid = blockIdx.y * gridDim.x + blockIdx.x;
	int threadid = blockid * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

__device__ int get_globalidx_2d_3d(void)
{
	int blockid = blockIdx.y * gridDim.x + blockIdx.x;
	int threadid = blockid * (blockDim.x * blockDim.y * blockDim.z) + (threadIdx.z * (blockDim.x * blockDim.y)) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

__device__ int get_globalidx_3d_1d(void)
{
	int blockid = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
	int threadid = blockid * blockDim.x + threadIdx.x;

	return threadid;
}

__device__ int get_globalidx_3d_2d(void)
{
	int blockid = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
	int threadid = blockid * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}

__device__ int get_globalidx_3d_3d(void)
{
	int blockid = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
	int threadid = blockid * (blockDim.x * blockDim.y * blockDim.z) + (threadIdx.z * (blockDim.x * blockDim.y)) + (threadIdx.y * blockDim.x) + threadIdx.x;

	return threadid;
}
 
__host__ int main(void)
{
	int c;
	int *gpu_c;

	cudaMalloc((void **)&gpu_c, sizeof(int));
 
	myfirstkernel <<<2,3>>>(3, 6, gpu_c);

	cudaMemcpy(&c, gpu_c, sizeof(int), cudaMemcpyDeviceToHost);

	cudaFree(gpu_c);

	cudaDeviceSynchronize();

	printf("exit.c = %d.\n", c);
 
	return 0;
}

others blog, opencl:

OpenCL编程初探_papaofdoudou的博客-CSDN博客_opencl 源码

CUDA编程初探_papaofdoudou的博客-CSDN博客_哪些场合必须用cuda编程

OpenCL编程之二_papaofdoudou的博客-CSDN博客

end 

相关文章：

  • asp.net毕业设计项目源码社区保障管理系统
  • linux内核中list的实现
  • ZYNQ入门
  • everything-everything使用技巧，过滤文件语法
  • 两军对垒问题及个人的思考
  • HDFS的启动流程和HA
  • ISP算法----AWB总结及源代码
  • 【Java基础(六)】面向对象与类的基础知识
  • Python爬虫——BeautifulSoup的基本使用
  • Acwing 802. 区间和
  • 传统方式连接数据库的弊端和数据库连接池原理
  • 什么叫 “雪碧图”？
  • 公众号网课搜题题库使用方法
  • Excel中身份证号码相关操作详解
  • 如何用4行 C 代码实现一个跨平台的命令行 mp3 播放器
  • CSS中外联样式表代表的含义
  • Cumulo 的 ClojureScript 模块已经成型
  • C学习-枚举（九）
  • dva中组件的懒加载
  • exif信息对照
  • express.js的介绍及使用
  • Java新版本的开发已正式进入轨道，版本号18.3
  • js对象的深浅拷贝
  • laravel5.5 视图共享数据
  • LeetCode29.两数相除 JavaScript
  • leetcode46 Permutation 排列组合
  • Linux下的乱码问题
  • Python连接Oracle
  • vue-cli3搭建项目
  • 笨办法学C 练习34：动态数组
  • 工程优化暨babel升级小记
  • 使用 5W1H 写出高可读的 Git Commit Message
  • 使用docker-compose进行多节点部署
  • 微信端页面使用-webkit-box和绝对定位时，元素上移的问题
  • 微信小程序设置上一页数据
  • 3月7日云栖精选夜读 | RSA 2019安全大会：企业资产管理成行业新风向标，云上安全占绝对优势 ...
  • 树莓派用上kodexplorer也能玩成私有网盘
  • #android不同版本废弃api，新api。
  • #define
  • #Linux杂记--将Python3的源码编译为.so文件方法与Linux环境下的交叉编译方法
  • （java版）排序算法----【冒泡，选择，插入，希尔，快速排序，归并排序，基数排序】超详细~~
  • （分布式缓存）Redis哨兵
  • （附源码）ssm高校志愿者服务系统 毕业设计 011648
  • (附源码)ssm教师工作量核算统计系统 毕业设计 162307
  • (解决办法)ASP.NET导出Excel，打开时提示“您尝试打开文件'XXX.xls'的格式与文件扩展名指定文件不一致
  • （利用IDEA+Maven）定制属于自己的jar包
  • （转）平衡树
  • **PHP二维数组遍历时同时赋值
  • ./configure、make、make install 命令
  • .gitattributes 文件
  • .net web项目 调用webService
  • .Net 路由处理厉害了
  • .net 中viewstate的原理和使用
  • .NET/C# 中你可以在代码中写多个 Main 函数，然后按需要随时切换
  • .net中的Queue和Stack