CUDA并行编程
教材《GPU高性能编程CUDA实战》第四章 CUDA C并行编程 基于CPU和GPU的Julia集对比
基于CPU的Julia集
#include "book.h"
#include "cpu_bitmap.h"
#define DIM 1000
struct cuComplex {
float r;
float i;
cuComplex( float a, float b ) : r(a), i(b) {}
float magnitude2( void ) { return r * r + i * i; }
cuComplex operator*(const cuComplex& a) {
return cuComplex(r*a.r - i*a.i, i*a.r + r*a.i);
}
cuComplex operator+(const cuComplex& a) {
return cuComplex(r+a.r, i+a.i);
}
};
int julia( int x, int y ) {
const float scale = 1.5;
float jx = scale * (float)(DIM/2 - x)/(DIM/2);
float jy = scale * (float)(DIM/2 - y)/(DIM/2);
cuComplex c(-0.8, 0.156);
cuComplex a(jx, jy);
int i = 0;
for (i=0; i<200; i++) {
a = a * a + c;
if (a.magnitude2() > 1000)
return 0;
}
return 1;
}
void kernel( unsigned char *ptr ){
for (int y=0; y<DIM; y++) {
for (int x=0; x<DIM; x++) {
int offset = x + y * DIM;
int juliaValue = julia( x, y );
ptr[offset*4 + 0] = 255 * juliaValue;
ptr[offset*4 + 1] = 0;
ptr[offset*4 + 2] = 0;
ptr[offset*4 + 3] = 255;
}
}
}
int main( void ) {
CPUBitmap bitmap( DIM, DIM );
unsigned char *ptr = bitmap.get_ptr();
kernel( ptr );
bitmap.display_and_exit();
}
基于GPU的Julia集
#include "cuda_runtime.h"
#include "device_launch_parameters.h"//包含blockIdx.x
#include "book.h"
#include"cpu_bitmap.h"
#define DIM 1000
struct cuComplex {
float r;
float i;
__device__ cuComplex(float a,float b):r(a),i(b){}//前面要加__device__否则会报错
__device__ float magnitude2(void) {
return r*r + i*i;
}
__device__ cuComplex operator*(const cuComplex& a) {
return cuComplex(r*a.r - i*a.i, i*a.r + r*a.i);
}
__device__ cuComplex operator+(const cuComplex& a) {
return cuComplex(r+a.r,i+a.i);
}
};
__device__ int julia(int x, int y) {
const float scale = 1.5;
float jx = scale*(float)(DIM / 2 - x) / (DIM / 2);
float jy = scale*(float)(DIM / 2 - y) / (DIM / 2);
cuComplex c(-0.8, 0.156);
cuComplex a(jx, jy);
int i = 0;
for (i = 0; i < 200; i++) {
a = a*a + c;
if (a.magnitude2() > 1000)
return 0;
}
return 1;
}
__global__ void kernel(unsigned char *ptr) {
//将threadIdx/BlockIdx映射到像素位置
int x = blockIdx.x;
int y = blockIdx.y;
int offset = x + y*gridDim.x;
//现在计算这个位置上的值
int juliaValue = julia(x, y);
ptr[offset * 4 + 0] = 255 * juliaValue;
ptr[offset * 4 + 1] = 0;
ptr[offset * 4 + 2] = 0;
ptr[offset * 4 + 3] = 255;
}
int main() {
CPUBitmap bitmap(DIM, DIM);
unsigned char *dev_bitmap;
HANDLE_ERROR(cudaMalloc((void**)&dev_bitmap, bitmap.image_size()));
dim3 grid(DIM, DIM);
kernel << <grid, 1 >> >(dev_bitmap);
HANDLE_ERROR(cudaMemcpy(bitmap.get_ptr(), dev_bitmap, bitmap.image_size(), cudaMemcpyDeviceToHost));
HANDLE_ERROR(cudaFree(dev_bitmap));
bitmap.display_and_exit();
}
注意:构造函数__device__ cuComplex(float a,float b):r(a),i(b){} 前面要加_device__,否则会报错
从device上调用host的函数是不允许的。