Julia分形

参考：http://www.matrix67.com/blog/archives/292
c=(0.285,0)的图不知道为什么弄不出来QAQ

用cuda写了一下Julia分形
直接生成bmp (下次写个带交互的能放大缩小的

#include "cuda_runtime.h"
#include "device_launch_parameters.h"

#include 
#include 
#include 
#include 
#include 
#include 
using namespace std;
struct BMP {
    template<class T>
    void wri(ofstream &os, T data) {
        //小端输出
        int len = sizeof(T);
        char *p = new char[len];
        for (int i = 0; i < len; ++i) {
            p[i] = data % 256;
            data >>= 8;
        }
        os.write(p, len);
    }
    BITMAPFILEHEADER p1;
    BITMAPINFOHEADER p2;
    unsigned char *src;
    int wid, hei;
    BMP(unsigned char *source, int width, int height):
        src(source), wid(width), hei(height) {
        p1.bfType = 0x4d42;//"MB" 小端输出变成"BM"
        p1.bfSize = 54 + width * height * 4;

        p1.bfReserved1 = p1.bfReserved2 = 0;
        p1.bfOffBits = 54;

        p2.biSize = 40;//40 (in windows)
        p2.biWidth = width;
        p2.biHeight = height;
        p2.biPlanes = 1;
        p2.biBitCount = 32;
        p2.biCompression = 0;
        p2.biSizeImage = width * height * 4;

        p2.biXPelsPerMeter = width;
        p2.biYPelsPerMeter = height;
        p2.biClrUsed = 0;
        p2.biClrImportant = 0;
    }
    void write(string path) {
        ofstream out(path, ios_base::out | ios_base::binary);
        wri(out, p1.bfType);
        wri(out, p1.bfSize);
        wri(out, p1.bfReserved1);
        wri(out, p1.bfReserved2);
        wri(out, p1.bfOffBits);

        wri(out, p2.biSize);
        wri(out, p2.biWidth);
        wri(out, p2.biHeight);
        wri(out, p2.biPlanes);
        wri(out, p2.biBitCount);
        wri(out, p2.biCompression);
        wri(out, p2.biSizeImage);
        wri(out, p2.biXPelsPerMeter);
        wri(out, p2.biYPelsPerMeter);
        wri(out, p2.biClrUsed);
        wri(out, p2.biClrImportant);

        out.write((char*)src, wid*hei*4);
        out.close();
    }
};
struct c2 {
    float x, y;
    __device__ c2(float _x = 0, float _y = 0) : x(_x), y(_y) {}
    __device__ c2 operator + (const c2 &t) {
        return c2(x+t.x, y+t.y);
    }
    __device__ c2 operator - (const c2 &t) {
        return c2(x - t.x, y - t.y);
    }
    __device__ c2 operator * (const c2 &t) {
        return c2(x*t.x - y*t.y, x*t.y + y*t.x);
    }
    __device__ c2 operator / (const int &t) {
        return c2(x / t, y / t);
    }
    __device__ float square() {
        return x*x + y*y;
    }
};

void HANDLE_ERROR(cudaError_t status);
void cudaCalc(unsigned char *res, int width, int height);
const unsigned int WIDTH = 8192, HEIGHT = 8192;
int main()
{
    HANDLE_ERROR(cudaSetDevice(0));
    unsigned char *p = new unsigned char[WIDTH * HEIGHT * 4];
    cudaCalc(p, WIDTH, HEIGHT);
    HANDLE_ERROR(cudaDeviceReset());
    BMP s(p, WIDTH, HEIGHT);
    s.write(".//a.bmp");
    return 0;
}
void HANDLE_ERROR(cudaError_t status) {
    if (status != cudaSuccess) {
        fprintf(stderr, "Error~\n");
        exit(0);
    }
}
__global__ void kernel(unsigned char *p, int width, int height) {
    float x1 = -1.6, y1 = -1.6, x2 = 1.6, y2 = 1.6;
    c2 c(-0.8, 0.156);
    int x = blockIdx.x * blockDim.x + threadIdx.x;
    int y = blockIdx.y * blockDim.y + threadIdx.y;
    int cnt = (y*width+x)*4;
    if (x < width && y < height) {
        c2 sp(x1 + (x2-x1) * x / width, y1 + (y2-y1) * y / height);
        int i;
        for (i = 0; i < 100; ++i) {
            sp = sp * sp + c;
            if (sp.square() >= 1000)
                break;
        }
        sp = sp / sqrt(sp.square());
        float gi = 1.0f * i / 100;
        p[cnt] = 255 * gi;
        p[cnt + 1] = (1.0-sp.x) * 255 * gi;
        p[cnt + 2] = (1.0-sp.y) * 255 * gi;
        p[cnt + 3] = 0;
    }
    return;
}
void cudaCalc(unsigned char *res, int width, int height) {
    unsigned char *p = 0;
    HANDLE_ERROR(cudaMalloc((void**)&p, width*height*4));

    dim3 blockDim(32, 32);
    dim3 gridDim((width + 31) / 32, (height + 31) / 32);
    kernel << > > (p, width, height);
    cudaError_t status;
    status = cudaGetLastError();
    if (status != cudaSuccess) {
        fprintf(stderr, "Build kernel failed.\n");
        goto Error;
    }
    status = cudaDeviceSynchronize();
    if (status != cudaSuccess) {
        fprintf(stderr, "kernel run failed.\n");
        goto Error;
    }
    status = cudaMemcpy(res, p, width*height*4, cudaMemcpyDeviceToHost);
    if (status != cudaSuccess) {
        fprintf(stderr, "cudaMemCpy Error!\n");
        goto Error;
    }
Error:
    cudaFree(p);
    return;
}

样图：
c=(-0.8,0.156)

c=(0.285,0.01)

c=(0.285,-0.01)

后来又发现了一个奇怪的问题

__global__ void kernel(unsigned char *p, int width, int height) {
    float x1 = -1.6f, y1 = -1.6f, x2 = 1.6f, y2 = 1.6f;
    c2 c(0.285f, 0.01f);
    int x = blockIdx.x * blockDim.x + threadIdx.x;
    int y = blockIdx.y * blockDim.y + threadIdx.y;
    int cnt = (y*width + x) * 4;
    if (x < width && y < height) {
        c2 sp(x1 + (x2 - x1) * x / width, y1 + (y2 - y1) * y / height);
        int i;
        for (i = 0; i < 100; ++i) {
            sp = sp * sp + c;
            if (sp.square() >= 1000)
                break;
        }
        sp = sp / sqrt(sp.square());
        float gi = 1.0 * i / 100;//******
        p[cnt] = 255 * gi;
        p[cnt + 1] = (1.0f - sp.x) * 255 * gi;
        p[cnt + 2] = (1.0f - sp.y) * 255 * gi;
        p[cnt + 3] = 0;
    }
    return;
}

float gi = 1.0 * i / 100;
float gi = 1.0f * i / 100;
第一种写法的kernel运行时间比第二种写法多出近50%
原因推测：第一种写法做了double除法
更深层次的原因未知