我已经向量化了色彩空间转换算法(RGB到YCbCr)。当我不使用线程(#pragma omp parallel for
)时,一切似乎都很好。但是,当我尝试使用线程时,它无法提高代码的矢量化版本的性能(它同时也将不作改进。)>
线程加速标量代码,自动矢量化代码和OpenMP SIMDized代码(#pragma omp parallel for simd
)
我不知道发生了什么,需要您的帮助。
提前感谢
我使用fedora 31,Intel corei7 6700HQ,12GB RAM,ICC 19.0.3(-Ofast[-no-vec]
-qopenmp -xHOST
代码如下:
标量:
版本://Scalar for basline #include <stdio.h> #define MAX1 512 #define MAX2 MAX1 float __attribute__(( aligned(32))) image_r[MAX1][MAX2], image_g[MAX1][MAX2], image_b[MAX1][MAX2], image_y[MAX1][MAX2], image_cb[MAX1][MAX2], image_cr[MAX1][MAX2]; float coeff_RTY[3][3] = {{0.299, 0.587, 0.114},{-0.169, -0.331, 0.500},{0.500, -0.419, -0.081}}; inline void fill_float(float a[MAX1][MAX1]) { int i,j; for(i=0; i<MAX1; i++){ for(j=0; j<MAX2; j++){ a[i][j] = (i+j+100)%256; } } } int main() { fill_float(image_r); fill_float(image_g); fill_float(image_b); int i, j; long t1,t2,min=100000000000000; do{ t1=_rdtsc(); //#pragma omp parallel for for( i=0; i<MAX1; i++){ for( j=0; j<MAX2; j++){ image_y[i][j] = coeff_RTY[0][0]*image_r[i][j] + coeff_RTY[0][1]*image_g[i][j] + coeff_RTY[0][2]*image_b[i][j]; image_cb[i][j] = coeff_RTY[1][0]*image_r[i][j] + coeff_RTY[1][1]*image_g[i][j] + coeff_RTY[1][2]*image_b[i][j] + 128; image_cr[i][j] = coeff_RTY[2][0]*image_r[i][j] + coeff_RTY[2][1]*image_g[i][j] + coeff_RTY[2][2]*image_b[i][j] + 128; } } t2=_rdtsc(); if((t2-t1)<min){ min=t2-t1; printf("\n%li", t2-t1); } }while(1); printf("%f", image_y[MAX1/2][MAX2/2]); printf("%f", image_cb[MAX1/2][MAX2/2]); printf("%f", image_cr[MAX1/2][MAX2/2]); return 0; }
以及使用AVX(浮点数)的vectorized
//AVX #include <stdio.h> #include <x86intrin.h> #define MAX1 512 #define MAX2 MAX1 float __attribute__(( aligned(32))) image_r[MAX1][MAX2], image_g[MAX1][MAX2], image_b[MAX1][MAX2], image_y[MAX1][MAX2], image_cb[MAX1][MAX2], image_cr[MAX1][MAX2]; float coeff_RTY[3][3] = {{0.299, 0.587, 0.114},{-0.169, -0.331, 0.500},{0.500, -0.419, -0.081}}; inline void fill_float(float a[MAX1][MAX1]) { int i,j; for(i=0; i<MAX1; i++){ for(j=0; j<MAX2; j++){ a[i][j] = (i+j+100)%256; } } } int main() { //program variables: //calculate filter coeff or use an existing one __m256 vec_c[3][3], vec_128; __m256 vec_r, vec_g, vec_b, vec_y, vec_cb, vec_cr; __m256 vec_t[3][3], vec_sum; vec_c[0][0] = _mm256_set1_ps(coeff_RTY[0][0]); vec_c[0][1] = _mm256_set1_ps(coeff_RTY[0][1]); vec_c[0][2] = _mm256_set1_ps(coeff_RTY[0][2]); vec_c[1][0] = _mm256_set1_ps(coeff_RTY[1][0]); vec_c[1][1] = _mm256_set1_ps(coeff_RTY[1][1]); vec_c[1][2] = _mm256_set1_ps(coeff_RTY[1][2]); vec_c[2][0] = _mm256_set1_ps(coeff_RTY[2][0]); vec_c[2][1] = _mm256_set1_ps(coeff_RTY[2][1]); vec_c[2][2] = _mm256_set1_ps(coeff_RTY[2][2]); vec_128 = _mm256_set1_ps(128); //iorder to avoid optimization for zero values fill_float(image_r); fill_float(image_g); fill_float(image_b); int i, j=0; long t1,t2,min=100000000000000; do{ t1=_rdtsc(); //#pragma omp parallel for for( i=0; i<MAX1; i++){ for( j=0; j<MAX2; j+=8){ //_mm_prefetch(&image_r[i][j+8],_MM_HINT_T0); //_mm_prefetch(&image_g[i][j+8],_MM_HINT_T0); //_mm_prefetch(&image_b[i][j+8],_MM_HINT_T0); vec_r = _mm256_load_ps(&image_r[i][j]); vec_g = _mm256_load_ps(&image_g[i][j]); vec_b = _mm256_load_ps(&image_b[i][j]); vec_t[0][0] = _mm256_mul_ps(vec_r, vec_c[0][0]); vec_t[0][1] = _mm256_mul_ps(vec_g, vec_c[0][1]); vec_t[0][2] = _mm256_mul_ps(vec_b, vec_c[0][2]); vec_t[1][0] = _mm256_mul_ps(vec_r, vec_c[1][0]); vec_t[1][1] = _mm256_mul_ps(vec_g, vec_c[1][1]); vec_t[1][2] = _mm256_mul_ps(vec_b, vec_c[1][2]); vec_t[2][0] = _mm256_mul_ps(vec_r, vec_c[2][0]); vec_t[2][1] = _mm256_mul_ps(vec_g, vec_c[2][1]); vec_t[2][2] = _mm256_mul_ps(vec_b, vec_c[2][2]); //vec_y = vec_t[0][0] + vec_t[0][1] + vec_t[0][2] vec_sum = _mm256_add_ps(vec_t[0][0], vec_t[0][1]); vec_y = _mm256_add_ps(vec_t[0][2], vec_sum); //vec_cb = vec_t[1][0] + vec_t[1][1] + vec_t[1][2] +128 vec_sum = _mm256_add_ps(vec_t[1][0], vec_t[1][1]); vec_sum = _mm256_add_ps(vec_t[1][2], vec_sum); vec_cb = _mm256_add_ps(vec_128, vec_sum); //vec_cr = vec_t[2][0] + vec_t[2][1] + vec_t[2][2] +128 vec_sum = _mm256_add_ps(vec_t[2][0], vec_t[2][1]); vec_sum = _mm256_add_ps(vec_t[2][2], vec_sum); vec_cr = _mm256_add_ps(vec_128, vec_sum); _mm256_stream_ps(&image_y[i][j], vec_y); _mm256_stream_ps(&image_cb[i][j], vec_cb); _mm256_stream_ps(&image_cr[i][j], vec_cr); } } t2=_rdtsc(); if((t2-t1)<min){ min=t2-t1; printf("\n%li", t2-t1); } }while(1); //inorder to avoid optimization for non used values printf("%f", image_y[MAX1/2][MAX2/2]); printf("%f", image_cb[MAX1/2][MAX2/2]); printf("%f", image_cr[MAX1/2][MAX2/2]); return 0; }
UPDATE:
[最佳记录周期为128x128图像尺寸如下:
单核:
Scalar code: 88k Auto-vectorized: 59k Vectorized using intrinsics: **21k** vectorized by #pragma omp simd: 59k
多核:
Scalar code: 25k Auto-vectorized: 13k Vectorized using intrinsics: **226k** vectorized by #pragma omp .. simd: 22k
对于1024x1024的图像尺寸如下:
单核:
Scalar code: 7M Auto-vectorized: 3M Vectorized using intrinsics: **3M** vectorized by #pragma omp simd: 3M
多核:
Scalar code: 6M
Auto-vectorized: 6M
Vectorized using intrinsics: **15M**
vectorized by #pragma omp parallel for simd: 8M
我已经向量化了色彩空间转换算法(RGB到YCbCr)。当我不使用线程(#pragma omp parallel)时,一切似乎都很好。但是当我尝试使用线程时,它无法改善...
[尝试了不同的想法之后,通过在#pragma omp parallel for
之前添加以下OpenMP语句行解决了该问题