Cycles: Cleanup, always use parenthesis
Easier to read/follow, and more robust for the further changes.
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908bb8bd82
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@ -37,59 +37,69 @@ CCL_NAMESPACE_BEGIN
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ccl_device_inline void math_vector_zero(float *v, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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v[i] = 0.0f;
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}
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}
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ccl_device_inline void math_matrix_zero(float *A, int n)
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{
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for(int row = 0; row < n; row++)
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for(int col = 0; col <= row; col++)
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for(int row = 0; row < n; row++) {
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for(int col = 0; col <= row; col++) {
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MAT(A, n, row, col) = 0.0f;
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}
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}
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}
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/* Elementary vector operations. */
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ccl_device_inline void math_vector_add(float *a, const float *ccl_restrict b, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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a[i] += b[i];
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}
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}
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ccl_device_inline void math_vector_mul(float *a, const float *ccl_restrict b, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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a[i] *= b[i];
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}
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}
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ccl_device_inline void math_vector_mul_strided(ccl_global float *a, const float *ccl_restrict b, int astride, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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a[i*astride] *= b[i];
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}
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}
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ccl_device_inline void math_vector_scale(float *a, float b, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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a[i] *= b;
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}
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}
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ccl_device_inline void math_vector_max(float *a, const float *ccl_restrict b, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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a[i] = max(a[i], b[i]);
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}
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}
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ccl_device_inline void math_vec3_add(float3 *v, int n, float *x, float3 w)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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v[i] += w*x[i];
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}
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}
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ccl_device_inline void math_vec3_add_strided(ccl_global float3 *v, int n, float *x, float3 w, int stride)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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v[i*stride] += w*x[i];
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}
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}
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/* Elementary matrix operations.
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@ -97,8 +107,9 @@ ccl_device_inline void math_vec3_add_strided(ccl_global float3 *v, int n, float
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ccl_device_inline void math_trimatrix_add_diagonal(ccl_global float *A, int n, float val, int stride)
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{
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for(int row = 0; row < n; row++)
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for(int row = 0; row < n; row++) {
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MATHS(A, row, row, stride) += val;
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}
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}
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/* Add Gramian matrix of v to A.
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@ -108,9 +119,11 @@ ccl_device_inline void math_matrix_add_gramian(float *A,
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const float *ccl_restrict v,
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float weight)
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{
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for(int row = 0; row < n; row++)
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for(int col = 0; col <= row; col++)
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for(int row = 0; row < n; row++) {
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for(int col = 0; col <= row; col++) {
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MAT(A, n, row, col) += v[row]*v[col]*weight;
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}
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}
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}
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/* Add Gramian matrix of v to A.
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@ -121,9 +134,11 @@ ccl_device_inline void math_trimatrix_add_gramian_strided(ccl_global float *A,
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float weight,
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int stride)
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{
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for(int row = 0; row < n; row++)
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for(int col = 0; col <= row; col++)
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for(int row = 0; row < n; row++) {
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for(int col = 0; col <= row; col++) {
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MATHS(A, row, col, stride) += v[row]*v[col]*weight;
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}
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}
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}
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/* Transpose matrix A inplace. */
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@ -138,9 +153,6 @@ ccl_device_inline void math_matrix_transpose(ccl_global float *A, int n, int str
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}
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}
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/* Solvers for matrix problems */
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/* In-place Cholesky-Banachiewicz decomposition of the square, positive-definite matrix A
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@ -199,10 +211,6 @@ ccl_device_inline void math_trimatrix_vec3_solve(ccl_global float *A, ccl_global
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}
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}
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/* Perform the Jacobi Eigenvalue Methon on matrix A.
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* A is assumed to be a symmetrical matrix, therefore only the lower-triangular part is ever accessed.
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* The algorithm overwrites the contents of A.
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@ -215,15 +223,19 @@ ccl_device void math_matrix_jacobi_eigendecomposition(float *A, ccl_global float
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{
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const float singular_epsilon = 1e-9f;
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for (int row = 0; row < n; row++)
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for (int col = 0; col < n; col++)
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for (int row = 0; row < n; row++) {
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for (int col = 0; col < n; col++) {
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MATS(V, n, row, col, v_stride) = (col == row) ? 1.0f : 0.0f;
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}
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}
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for (int sweep = 0; sweep < 8; sweep++) {
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float off_diagonal = 0.0f;
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for (int row = 1; row < n; row++)
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for (int col = 0; col < row; col++)
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for (int row = 1; row < n; row++) {
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for (int col = 0; col < row; col++) {
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off_diagonal += fabsf(MAT(A, n, row, col));
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}
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}
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if (off_diagonal < 1e-7f) {
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/* The matrix has nearly reached diagonal form.
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* Since the eigenvalues are only used to determine truncation, their exact values aren't required - a relative error of a few ULPs won't matter at all. */
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@ -327,51 +339,61 @@ ccl_device void math_matrix_jacobi_eigendecomposition(float *A, ccl_global float
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}
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#ifdef __KERNEL_SSE3__
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ccl_device_inline void math_vector_zero_sse(__m128 *A, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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A[i] = _mm_setzero_ps();
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}
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}
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ccl_device_inline void math_matrix_zero_sse(__m128 *A, int n)
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{
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for(int row = 0; row < n; row++)
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for(int col = 0; col <= row; col++)
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for(int row = 0; row < n; row++) {
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for(int col = 0; col <= row; col++) {
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MAT(A, n, row, col) = _mm_setzero_ps();
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}
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}
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}
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/* Add Gramian matrix of v to A.
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* The Gramian matrix of v is v^T*v, so element (i,j) is v[i]*v[j]. */
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ccl_device_inline void math_matrix_add_gramian_sse(__m128 *A, int n, const __m128 *ccl_restrict v, __m128 weight)
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{
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for(int row = 0; row < n; row++)
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for(int col = 0; col <= row; col++)
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for(int row = 0; row < n; row++) {
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for(int col = 0; col <= row; col++) {
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MAT(A, n, row, col) = _mm_add_ps(MAT(A, n, row, col), _mm_mul_ps(_mm_mul_ps(v[row], v[col]), weight));
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}
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}
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}
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ccl_device_inline void math_vector_add_sse(__m128 *V, int n, const __m128 *ccl_restrict a)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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V[i] = _mm_add_ps(V[i], a[i]);
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}
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}
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ccl_device_inline void math_vector_mul_sse(__m128 *V, int n, const __m128 *ccl_restrict a)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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V[i] = _mm_mul_ps(V[i], a[i]);
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}
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}
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ccl_device_inline void math_vector_max_sse(__m128 *a, const __m128 *ccl_restrict b, int n)
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{
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for(int i = 0; i < n; i++)
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for(int i = 0; i < n; i++) {
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a[i] = _mm_max_ps(a[i], b[i]);
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}
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}
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ccl_device_inline void math_matrix_hsum(float *A, int n, const __m128 *ccl_restrict B)
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{
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for(int row = 0; row < n; row++)
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for(int col = 0; col <= row; col++)
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for(int row = 0; row < n; row++) {
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for(int col = 0; col <= row; col++) {
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MAT(A, n, row, col) = _mm_hsum_ss(MAT(B, n, row, col));
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}
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}
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}
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#endif
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