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Fix errors and optimize kernel.

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castano
2007-07-08 09:46:18 +00:00
parent 25f977318f
commit 50a71f78b5
2 changed files with 178 additions and 89 deletions
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155
src/nvimage/nvtt/cuda/CudaMath.h
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@ -122,92 +122,99 @@ inline __device__ __host__ float3 normalize(float3 v)
// http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html
inline __device__ __host__ float3 firstEigenVector( float matrix[6] )
{
// 8 iterations seems to be more than enough.
// 8 iterations seems to be more than enough.
float3 v = make_float3(1.0f, 1.0f, 1.0f);
for(int i = 0; i < 8; i++) {
float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
float m = max(max(x, y), z);
float iv = 1.0f / m;
#if __DEVICE_EMULATION__
if (m == 0.0f) iv = 0.0f;
#endif
v = make_float3(x*iv, y*iv, z*iv);
}
float3 v = make_float3(1.0f, 1.0f, 1.0f);
for(int i = 0; i < 8; i++) {
float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
float m = max(max(x, y), z);
float iv = 1.0f / m;
#if __DEVICE_EMULATION__
if (m == 0.0f) iv = 0.0f;
#endif
v = make_float3(x*iv, y*iv, z*iv);
}
return v;
return v;
}
inline __device__ float3 bestFitLine(const float3 * colors)
inline __device__ void colorSums(const float3 * colors, float3 * sums)
{
#if __DEVICE_EMULATION__
float3 color_sum = make_float3(0.0f, 0.0f, 0.0f);
for (int i = 0; i < 16; i++)
{
color_sum += colors[i];
}
// Compute covariance matrix of the given colors.
float3 center = make_float3(0.0f, 0.0f, 0.0f);
for (int i = 0; i < 16; i++)
{
center += colors[i];
}
center /= 16.0f;
float covariance[6] = {0, 0, 0, 0, 0, 0};
for (int i = 0; i < 16; i++)
{
float3 a = colors[i] - center;
covariance[0] += a.x * a.x;
covariance[1] += a.x * a.y;
covariance[2] += a.x * a.z;
covariance[3] += a.y * a.y;
covariance[4] += a.y * a.z;
covariance[5] += a.z * a.z;
}
for (int i = 0; i < 16; i++)
{
sums[i] = color_sum;
}
#else
const int idx = threadIdx.x;
const int idx = threadIdx.x;
__shared__ float3 colorSum[16];
colorSum[idx] = colors[idx];
// Unrolled parallel reduction.
if (idx < 8) {
colorSum[idx] += colorSum[idx + 8];
colorSum[idx] += colorSum[idx + 4];
colorSum[idx] += colorSum[idx + 2];
colorSum[idx] += colorSum[idx + 1];
}
// @@ Eliminate two-way bank conflicts here.
// @@ It seems that doing that and unrolling the reduction doesn't help...
__shared__ float covariance[16*6];
colorSum[idx] = colors[idx] - colorSum[0] / 16.0f;
covariance[6 * idx + 0] = colorSum[idx].x * colorSum[idx].x; // 0, 6, 12, 2, 8, 14, 4, 10, 0
covariance[6 * idx + 1] = colorSum[idx].x * colorSum[idx].y;
covariance[6 * idx + 2] = colorSum[idx].x * colorSum[idx].z;
covariance[6 * idx + 3] = colorSum[idx].y * colorSum[idx].y;
covariance[6 * idx + 4] = colorSum[idx].y * colorSum[idx].z;
covariance[6 * idx + 5] = colorSum[idx].z * colorSum[idx].z;
for(int d = 8; d > 0; d >>= 1)
{
if (idx < d)
{
covariance[6 * idx + 0] += covariance[6 * (idx+d) + 0];
covariance[6 * idx + 1] += covariance[6 * (idx+d) + 1];
covariance[6 * idx + 2] += covariance[6 * (idx+d) + 2];
covariance[6 * idx + 3] += covariance[6 * (idx+d) + 3];
covariance[6 * idx + 4] += covariance[6 * (idx+d) + 4];
covariance[6 * idx + 5] += covariance[6 * (idx+d) + 5];
}
}
sums[idx] = colors[idx];
sums[idx] += sums[idx^8];
sums[idx] += sums[idx^4];
sums[idx] += sums[idx^2];
sums[idx] += sums[idx^1];
#endif
// Compute first eigen vector.
return firstEigenVector(covariance);
}
inline __device__ float3 bestFitLine(const float3 * colors, float3 color_sum)
{
// Compute covariance matrix of the given colors.
#if __DEVICE_EMULATION__
float covariance[6] = {0, 0, 0, 0, 0, 0};
for (int i = 0; i < 16; i++)
{
float3 a = colors[i] - color_sum * (1.0f / 16.0f);
covariance[0] += a.x * a.x;
covariance[1] += a.x * a.y;
covariance[2] += a.x * a.z;
covariance[3] += a.y * a.y;
covariance[4] += a.y * a.z;
covariance[5] += a.z * a.z;
}
#else
const int idx = threadIdx.x;
float3 diff = colors[idx] - color_sum * (1.0f / 16.0f);
// @@ Eliminate two-way bank conflicts here.
// @@ It seems that doing that and unrolling the reduction doesn't help...
__shared__ float covariance[16*6];
covariance[6 * idx + 0] = diff.x * diff.x; // 0, 6, 12, 2, 8, 14, 4, 10, 0
covariance[6 * idx + 1] = diff.x * diff.y;
covariance[6 * idx + 2] = diff.x * diff.z;
covariance[6 * idx + 3] = diff.y * diff.y;
covariance[6 * idx + 4] = diff.y * diff.z;
covariance[6 * idx + 5] = diff.z * diff.z;
for(int d = 8; d > 0; d >>= 1)
{
if (idx < d)
{
covariance[6 * idx + 0] += covariance[6 * (idx+d) + 0];
covariance[6 * idx + 1] += covariance[6 * (idx+d) + 1];
covariance[6 * idx + 2] += covariance[6 * (idx+d) + 2];
covariance[6 * idx + 3] += covariance[6 * (idx+d) + 3];
covariance[6 * idx + 4] += covariance[6 * (idx+d) + 4];
covariance[6 * idx + 5] += covariance[6 * (idx+d) + 5];
}
}
#endif
// Compute first eigen vector.
return firstEigenVector(covariance);
}