diff options
Diffstat (limited to 'intern/cycles/kernel/kernel_random.h')
| -rw-r--r-- | intern/cycles/kernel/kernel_random.h | 238 |
1 files changed, 173 insertions, 65 deletions
diff --git a/intern/cycles/kernel/kernel_random.h b/intern/cycles/kernel/kernel_random.h index d4f0caff5de..e8a912ccc0b 100644 --- a/intern/cycles/kernel/kernel_random.h +++ b/intern/cycles/kernel/kernel_random.h @@ -20,14 +20,15 @@ CCL_NAMESPACE_BEGIN #ifdef __SOBOL__ -/* skip initial numbers that are not as well distributed, especially the +/* Skip initial numbers that are not as well distributed, especially the * first sequence is just 0 everywhere, which can be problematic for e.g. - * path termination */ + * path termination. + */ #define SOBOL_SKIP 64 -/* High Dimensional Sobol */ +/* High Dimensional Sobol. */ -/* van der corput radical inverse */ +/* Van der Corput radical inverse. */ ccl_device uint van_der_corput(uint bits) { bits = (bits << 16) | (bits >> 16); @@ -38,58 +39,63 @@ ccl_device uint van_der_corput(uint bits) return bits; } -/* sobol radical inverse */ +/* Sobol radical inverse. */ ccl_device uint sobol(uint i) { uint r = 0; - - for(uint v = 1U << 31; i; i >>= 1, v ^= v >> 1) - if(i & 1) + for(uint v = 1U << 31; i; i >>= 1, v ^= v >> 1) { + if(i & 1) { r ^= v; - + } + } return r; } -/* inverse of sobol radical inverse */ +/* Inverse of sobol radical inverse. */ ccl_device uint sobol_inverse(uint i) { const uint msb = 1U << 31; uint r = 0; - - for(uint v = 1; i; i <<= 1, v ^= v << 1) - if(i & msb) + for(uint v = 1; i; i <<= 1, v ^= v << 1) { + if(i & msb) { r ^= v; - + } + } return r; } -/* multidimensional sobol with generator matrices - * dimension 0 and 1 are equal to van_der_corput() and sobol() respectively */ +/* Multidimensional sobol with generator matrices + * dimension 0 and 1 are equal to van_der_corput() and sobol() respectively. + */ ccl_device uint sobol_dimension(KernelGlobals *kg, int index, int dimension) { uint result = 0; uint i = index; - - for(uint j = 0; i; i >>= 1, j++) - if(i & 1) + for(uint j = 0; i; i >>= 1, j++) { + if(i & 1) { result ^= kernel_tex_fetch(__sobol_directions, 32*dimension + j); - + } + } return result; } -/* lookup index and x/y coordinate, assumes m is a power of two */ -ccl_device uint sobol_lookup(const uint m, const uint frame, const uint ex, const uint ey, uint *x, uint *y) +/* Lookup index and x/y coordinate, assumes m is a power of two. */ +ccl_device uint sobol_lookup(const uint m, + const uint frame, + const uint ex, + const uint ey, + uint *x, uint *y) { - /* shift is constant per frame */ + /* Shift is constant per frame. */ const uint shift = frame << (m << 1); const uint sobol_shift = sobol(shift); - /* van der Corput is its own inverse */ + /* Van der Corput is its own inverse. */ const uint lower = van_der_corput(ex << (32 - m)); - /* need to compensate for ey difference and shift */ + /* Need to compensate for ey difference and shift. */ const uint sobol_lower = sobol(lower); - const uint mask = ~-(1 << m) << (32 - m); /* only m upper bits */ + const uint mask = ~-(1 << m) << (32 - m); /* Only m upper bits. */ const uint delta = ((ey << (32 - m)) ^ sobol_lower ^ sobol_shift) & mask; - /* only use m upper bits for the index (m is a power of two) */ + /* Only use m upper bits for the index (m is a power of two). */ const uint sobol_result = delta | (delta >> m); const uint upper = sobol_inverse(sobol_result); const uint index = shift | upper | lower; @@ -98,11 +104,14 @@ ccl_device uint sobol_lookup(const uint m, const uint frame, const uint ex, cons return index; } -ccl_device_forceinline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension) +ccl_device_forceinline float path_rng_1D(KernelGlobals *kg, + RNG *rng, + int sample, int num_samples, + int dimension) { #ifdef __CMJ__ if(kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_CMJ) { - /* correlated multi-jittered */ + /* Correlated multi-jitter. */ int p = *rng + dimension; return cmj_sample_1D(sample, num_samples, p); } @@ -113,7 +122,7 @@ ccl_device_forceinline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample float r = (float)result * (1.0f/(float)0xFFFFFFFF); return r; #else - /* compute sobol sequence value using direction vectors */ + /* Compute sobol sequence value using direction vectors. */ uint result = sobol_dimension(kg, sample + SOBOL_SKIP, dimension); float r = (float)result * (1.0f/(float)0xFFFFFFFF); @@ -130,24 +139,33 @@ ccl_device_forceinline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample #endif } -ccl_device_forceinline void path_rng_2D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension, float *fx, float *fy) +ccl_device_forceinline void path_rng_2D(KernelGlobals *kg, + RNG *rng, + int sample, int num_samples, + int dimension, + float *fx, float *fy) { #ifdef __CMJ__ if(kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_CMJ) { - /* correlated multi-jittered */ + /* Correlated multi-jitter. */ int p = *rng + dimension; cmj_sample_2D(sample, num_samples, p, fx, fy); } else #endif { - /* sobol */ + /* Sobol. */ *fx = path_rng_1D(kg, rng, sample, num_samples, dimension); *fy = path_rng_1D(kg, rng, sample, num_samples, dimension + 1); } } -ccl_device_inline void path_rng_init(KernelGlobals *kg, ccl_global uint *rng_state, int sample, int num_samples, RNG *rng, int x, int y, float *fx, float *fy) +ccl_device_inline void path_rng_init(KernelGlobals *kg, + ccl_global uint *rng_state, + int sample, int num_samples, + RNG *rng, + int x, int y, + float *fx, float *fy) { #ifdef __SOBOL_FULL_SCREEN__ uint px, py; @@ -182,29 +200,43 @@ ccl_device_inline void path_rng_init(KernelGlobals *kg, ccl_global uint *rng_sta #endif } -ccl_device void path_rng_end(KernelGlobals *kg, ccl_global uint *rng_state, RNG rng) +ccl_device void path_rng_end(KernelGlobals *kg, + ccl_global uint *rng_state, + RNG rng) { /* nothing to do */ } -#else +#else /* __SOBOL__ */ /* Linear Congruential Generator */ -ccl_device_forceinline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension) +ccl_device_forceinline float path_rng_1D(KernelGlobals *kg, + RNG *rng, + int sample, int num_samples, + int dimension) { /* implicit mod 2^32 */ *rng = (1103515245*(*rng) + 12345); return (float)*rng * (1.0f/(float)0xFFFFFFFF); } -ccl_device_inline void path_rng_2D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension, float *fx, float *fy) +ccl_device_inline void path_rng_2D(KernelGlobals *kg, + RNG *rng, + int sample, int num_samples, + int dimension, + float *fx, float *fy) { *fx = path_rng_1D(kg, rng, sample, num_samples, dimension); *fy = path_rng_1D(kg, rng, sample, num_samples, dimension + 1); } -ccl_device void path_rng_init(KernelGlobals *kg, ccl_global uint *rng_state, int sample, int num_samples, RNG *rng, int x, int y, float *fx, float *fy) +ccl_device void path_rng_init(KernelGlobals *kg, + ccl_global uint *rng_state, + int sample, int num_samples, + RNG *rng, + int x, int y, + float *fx, float *fy) { /* load state */ *rng = *rng_state; @@ -220,13 +252,15 @@ ccl_device void path_rng_init(KernelGlobals *kg, ccl_global uint *rng_state, int } } -ccl_device void path_rng_end(KernelGlobals *kg, ccl_global uint *rng_state, RNG rng) +ccl_device void path_rng_end(KernelGlobals *kg, + ccl_global uint *rng_state, + RNG rng) { /* store state for next sample */ *rng_state = rng; } -#endif +#endif /* __SOBOL__ */ /* Linear Congruential Generator */ @@ -257,49 +291,108 @@ ccl_device uint lcg_init(uint seed) * dimension to avoid using the same sequence twice. * * For branches in the path we must be careful not to reuse the same number - * in a sequence and offset accordingly. */ + * in a sequence and offset accordingly. + */ -ccl_device_inline float path_state_rng_1D(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int dimension) +ccl_device_inline float path_state_rng_1D(KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int dimension) { - return path_rng_1D(kg, rng, state->sample, state->num_samples, state->rng_offset + dimension); + return path_rng_1D(kg, + rng, + state->sample, state->num_samples, + state->rng_offset + dimension); } -ccl_device_inline float path_state_rng_1D_for_decision(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int dimension) +ccl_device_inline float path_state_rng_1D_for_decision( + KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int dimension) { - /* the rng_offset is not increased for transparent bounces. if we do then + /* The rng_offset is not increased for transparent bounces. if we do then * fully transparent objects can become subtly visible by the different * sampling patterns used where the transparent object is. * * however for some random numbers that will determine if we next bounce * is transparent we do need to increase the offset to avoid always making - * the same decision */ - int rng_offset = state->rng_offset + state->transparent_bounce*PRNG_BOUNCE_NUM; - return path_rng_1D(kg, rng, state->sample, state->num_samples, rng_offset + dimension); + * the same decision. */ + const int rng_offset = state->rng_offset + state->transparent_bounce * PRNG_BOUNCE_NUM; + return path_rng_1D(kg, + rng, + state->sample, state->num_samples, + rng_offset + dimension); } -ccl_device_inline void path_state_rng_2D(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int dimension, float *fx, float *fy) +ccl_device_inline void path_state_rng_2D(KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int dimension, + float *fx, float *fy) { - path_rng_2D(kg, rng, state->sample, state->num_samples, state->rng_offset + dimension, fx, fy); + path_rng_2D(kg, + rng, + state->sample, state->num_samples, + state->rng_offset + dimension, + fx, fy); } -ccl_device_inline float path_branched_rng_1D(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int branch, int num_branches, int dimension) +ccl_device_inline float path_branched_rng_1D( + KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int branch, + int num_branches, + int dimension) { - return path_rng_1D(kg, rng, state->sample*num_branches + branch, state->num_samples*num_branches, state->rng_offset + dimension); + return path_rng_1D(kg, + rng, + state->sample * num_branches + branch, + state->num_samples * num_branches, + state->rng_offset + dimension); } -ccl_device_inline float path_branched_rng_1D_for_decision(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int branch, int num_branches, int dimension) +ccl_device_inline float path_branched_rng_1D_for_decision( + KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int branch, + int num_branches, + int dimension) { - int rng_offset = state->rng_offset + state->transparent_bounce*PRNG_BOUNCE_NUM; - return path_rng_1D(kg, rng, state->sample*num_branches + branch, state->num_samples*num_branches, rng_offset + dimension); + const int rng_offset = state->rng_offset + state->transparent_bounce * PRNG_BOUNCE_NUM; + return path_rng_1D(kg, + rng, + state->sample * num_branches + branch, + state->num_samples * num_branches, + rng_offset + dimension); } -ccl_device_inline void path_branched_rng_2D(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int branch, int num_branches, int dimension, float *fx, float *fy) +ccl_device_inline void path_branched_rng_2D( + KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int branch, + int num_branches, + int dimension, + float *fx, float *fy) { - path_rng_2D(kg, rng, state->sample*num_branches + branch, state->num_samples*num_branches, state->rng_offset + dimension, fx, fy); + path_rng_2D(kg, + rng, + state->sample * num_branches + branch, + state->num_samples * num_branches, + state->rng_offset + dimension, + fx, fy); } -/* Utitility functions to get light termination value, since it might not be needed in many cases. */ -ccl_device_inline float path_state_rng_light_termination(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state) +/* Utitility functions to get light termination value, + * since it might not be needed in many cases. + */ +ccl_device_inline float path_state_rng_light_termination( + KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state) { if(kernel_data.integrator.light_inv_rr_threshold > 0.0f) { return path_state_rng_1D_for_decision(kg, rng, state, PRNG_LIGHT_TERMINATE); @@ -307,15 +400,27 @@ ccl_device_inline float path_state_rng_light_termination(KernelGlobals *kg, RNG return 0.0f; } -ccl_device_inline float path_branched_rng_light_termination(KernelGlobals *kg, RNG *rng, const ccl_addr_space PathState *state, int branch, int num_branches) +ccl_device_inline float path_branched_rng_light_termination( + KernelGlobals *kg, + RNG *rng, + const ccl_addr_space PathState *state, + int branch, + int num_branches) { if(kernel_data.integrator.light_inv_rr_threshold > 0.0f) { - return path_branched_rng_1D_for_decision(kg, rng, state, branch, num_branches, PRNG_LIGHT_TERMINATE); + return path_branched_rng_1D_for_decision(kg, + rng, + state, + branch, + num_branches, + PRNG_LIGHT_TERMINATE); } return 0.0f; } -ccl_device_inline void path_state_branch(ccl_addr_space PathState *state, int branch, int num_branches) +ccl_device_inline void path_state_branch(ccl_addr_space PathState *state, + int branch, + int num_branches) { /* path is splitting into a branch, adjust so that each branch * still gets a unique sample from the same sequence */ @@ -324,14 +429,17 @@ ccl_device_inline void path_state_branch(ccl_addr_space PathState *state, int br state->num_samples = state->num_samples*num_branches; } -ccl_device_inline uint lcg_state_init(RNG *rng, int rng_offset, int sample, uint scramble) +ccl_device_inline uint lcg_state_init(RNG *rng, + int rng_offset, + int sample, + uint scramble) { return lcg_init(*rng + rng_offset + sample*scramble); } ccl_device float lcg_step_float_addrspace(ccl_addr_space uint *rng) { - /* implicit mod 2^32 */ + /* Implicit mod 2^32 */ *rng = (1103515245*(*rng) + 12345); return (float)*rng * (1.0f/(float)0xFFFFFFFF); } |