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AABB transformation
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| //-O0: SSE is ~8.2x faster | |
| //-O1: SSE is ~11.6x faster | |
| //-O2: SSE is ~23.3x faster | |
| //-O3: SSE is ~5x faster | |
| //-Ofast: SSE is ~1.1x faster | |
| //Compiled with GCC 5.3.1 | |
| //Ran on a Intel(R) Core(TM) i7-3770K CPU | |
| //These results are probably wrong. | |
| //Matrices are in column-major layout (or OpenGL or non-Direct3D layout). | |
| #include <xmmintrin.h> | |
| #include <math.h> | |
| void transformAABB_SSE(const float matrix_[4][4], float min[4], float max[4]) { | |
| min[3] = max[3] = NAN; | |
| __m128 tmin __attribute__((aligned(16))) = _mm_loadu_ps(min); | |
| __m128 tmax __attribute__((aligned(16))) = _mm_loadu_ps(max); | |
| __m128 rmin __attribute__((aligned(16))) = _mm_loadu_ps(((const float*)&matrix_)+12); | |
| __m128 rmax __attribute__((aligned(16))) = rmin; | |
| __m128 matrix[4] __attribute__((aligned(16))); | |
| matrix[0] = _mm_loadu_ps((const float*)&matrix_); | |
| matrix[1] = _mm_loadu_ps(((const float*)&matrix_)+4); | |
| matrix[2] = _mm_loadu_ps(((const float*)&matrix_)+8); | |
| matrix[3] = _mm_loadu_ps(((const float*)&matrix_)+12); | |
| _MM_TRANSPOSE4_PS(matrix[0], matrix[1], matrix[2], matrix[3]); | |
| __m128 x __attribute__((aligned(16))) = _mm_mul_ps(matrix[0], tmin); | |
| __m128 y __attribute__((aligned(16))) = _mm_mul_ps(matrix[0], tmax); | |
| rmin = _mm_add_ps(rmin, _mm_min_ps(x, y)); | |
| rmax = _mm_add_ps(rmax, _mm_max_ps(x, y)); | |
| x = _mm_mul_ps(matrix[1], tmin); | |
| y = _mm_mul_ps(matrix[1], tmax); | |
| rmin = _mm_add_ps(rmin, _mm_min_ps(x, y)); | |
| rmax = _mm_add_ps(rmax, _mm_max_ps(x, y)); | |
| x = _mm_mul_ps(matrix[2], tmin); | |
| y = _mm_mul_ps(matrix[2], tmax); | |
| rmin = _mm_add_ps(rmin, _mm_min_ps(x, y)); | |
| rmax = _mm_add_ps(rmax, _mm_max_ps(x, y)); | |
| _mm_store_ps(min, rmin); | |
| _mm_store_ps(max, rmax); | |
| } | |
| void transformAABB(const float matrix[4][4], float min[4], float max[4]) { | |
| float rmin[3] = {matrix[3][0], matrix[3][1], matrix[3][2]}; | |
| float rmax[3] = {matrix[3][0], matrix[3][1], matrix[3][2]}; | |
| #define ITER(i, j) {\ | |
| float x = min[j] * matrix[j][i];\ | |
| float y = max[j] * matrix[j][i];\ | |
| rmin[i] += fmin(x, y);\ | |
| rmax[i] += fmax(x, y);\ | |
| } | |
| ITER(0, 0) | |
| ITER(0, 1) | |
| ITER(0, 2) | |
| ITER(1, 0) | |
| ITER(1, 1) | |
| ITER(1, 2) | |
| ITER(2, 0) | |
| ITER(2, 1) | |
| ITER(2, 2) | |
| #undef ITER | |
| min[0] = rmin[0]; | |
| min[1] = rmin[1]; | |
| min[2] = rmin[2]; | |
| max[0] = rmax[0]; | |
| max[1] = rmax[1]; | |
| max[2] = rmax[2]; | |
| } | |
| int main(int argc) { | |
| const float matrix[4][4]; | |
| float min[4]; | |
| float max[4]; | |
| for (size_t i = 0; i < 10000000; i++) | |
| transformAABB_SSE(matrix, min, max); | |
| //Stop the compiler from optimizing stuff away. | |
| return min[0] + min[1] + min[2] + max[0] + max[1] + max[2]; | |
| } |
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