cuda 并行计算 | GPU 编程模型

udacity上的课程，有nvidia的工程师上课，比较基础也比较易懂。

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CUDA程序的特点

相比于CPU的单线程串行计算，CUDA程序的多线程对速度提升有很大的作用。
这就是优化时间与优化吞吐量的区别。

1. 程序编译后分别在CPU和GPU上运行；
2. CPU是主机（host），GPU是从机（device）；
3. 各自有各自的存储位置，不能相互访问。
4. GPU不能发起运算，只能相应运算

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CUDA程序的执行步骤

1. CPU在GPU上申请空间
   cudaMalloc(起始地址,大小)
2. CPU将数据从内存拷贝到显存
   cudaMemcpy(源,目标,大小,方向)
3. CPU启动GPU上的内核进行计算
   kernel_name <<>>(函数参数)
4. CPU将处理结果从显存拷贝到内存
   cudaMemcpy(源,目标,大小,方向)

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kernel 函数

_global_ void square(float *d_in, float *d_out)
{
    int idx = threadIdx.x;
    float f = d_in[idx];
    d_out[idx] = f * f;
}

对于每个kernel，其计算流程类似于串行计算。

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Block 与 Thread

在CPU启动GPU上的内核进行计算时，使用的是kernel_name <<>>，定义了blocks，threads的大小。这两者可以是1、2或是3D的结构，代表使用多少块，每块线程数目。
数据结构为dim(x,y,z)，对于一维w等价于dim3(w)也等价于dim3(w,1,1)。
总的线程数为二者的乘积。
对于每个线程，其索引号是比较重要的，访问方法有：

• threadIdx
• blockDim
• blockIdx

等。

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映射

是一种数据与方法的关系（其实感觉就是函数）。
Map(Elements, Function)
元素为待处理的数据集合，Function为对每个元素处理的方法。

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彩图转灰度图

这是lesson 1的homework，不是很难。

// Homework 1
// Color to Greyscale Conversion

//A common way to represent color images is known as RGBA - the color
//is specified by how much Red, Green, and Blue is in it.
//The 'A' stands for Alpha and is used for transparency; it will be
//ignored in this homework.

//Each channel Red, Blue, Green, and Alpha is represented by one byte.
//Since we are using one byte for each color there are 256 different
//possible values for each color.  This means we use 4 bytes per pixel.

//Greyscale images are represented by a single intensity value per pixel
//which is one byte in size.

//To convert an image from color to grayscale one simple method is to
//set the intensity to the average of the RGB channels.  But we will
//use a more sophisticated method that takes into account how the eye 
//perceives color and weights the channels unequally.

//The eye responds most strongly to green followed by red and then blue.
//The NTSC (National Television System Committee) recommends the following
//formula for color to greyscale conversion:

//I = .299f * R + .587f * G + .114f * B

//Notice the trailing f's on the numbers which indicate that they are 
//single precision floating point constants and not double precision
//constants.

//You should fill in the kernel as well as set the block and grid sizes
//so that the entire image is processed.

#include "reference_calc.cpp"
#include "utils.h"
#include 

__global__
void rgba_to_greyscale(const uchar4* const rgbaImage,
                       unsigned char* const greyImage,
                       int numRows, int numCols)
{
  //TODO
  //Fill in the kernel to convert from color to greyscale
  //the mapping from components of a uchar4 to RGBA is:
  // .x -> R ; .y -> G ; .z -> B ; .w -> A
  //
  //The output (greyImage) at each pixel should be the result of
  //applying the formula: output = .299f * R + .587f * G + .114f * B;
  //Note: We will be ignoring the alpha channel for this conversion
  int ind_x = blockIdx.x;
  int ind_y = blockIdx.y;
  uchar4 pixel_in = rgbaImage[ind_x * numCols +ind_y];
  unsigned char R = pixel_in.x;
  unsigned char G = pixel_in.y;
  unsigned char B = pixel_in.z;
  unsigned char output = .299f * R + .587f * G + .114f * B;
  greyImage[ind_x * numCols +ind_y] = output;
  //First create a mapping from the 2D block and grid locations
  //to an absolute 2D location in the image, then use that to
  //calculate a 1D offset
}

void your_rgba_to_greyscale(const uchar4 * const h_rgbaImage, uchar4 * const d_rgbaImage,
                            unsigned char* const d_greyImage, size_t numRows, size_t numCols)
{
  //You must fill in the correct sizes for the blockSize and gridSize
  //currently only one block with one thread is being launched
  const dim3 blockSize(1, 1, 1);  //TODO
  const dim3 gridSize( numRows, numCols, 1);  //TODO
  rgba_to_greyscale<<>>(d_rgbaImage, d_greyImage, numRows, numCols);

  cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
}

Intro to Parallel Programming