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Diffstat (limited to 'intern/cycles/kernel/kernel_random.h')
| -rw-r--r-- | intern/cycles/kernel/kernel_random.h | 216 |
1 files changed, 0 insertions, 216 deletions
diff --git a/intern/cycles/kernel/kernel_random.h b/intern/cycles/kernel/kernel_random.h deleted file mode 100644 index e5e87453611..00000000000 --- a/intern/cycles/kernel/kernel_random.h +++ /dev/null @@ -1,216 +0,0 @@ -/* - * Copyright 2011-2013 Blender Foundation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#pragma once - -#include "kernel/kernel_jitter.h" -#include "util/util_hash.h" - -CCL_NAMESPACE_BEGIN - -/* Pseudo random numbers, uncomment this for debugging correlations. Only run - * this single threaded on a CPU for repeatable results. */ -//#define __DEBUG_CORRELATION__ - -/* High Dimensional Sobol. - * - * Multidimensional sobol with generator matrices. Dimension 0 and 1 are equal - * to classic Van der Corput and Sobol sequences. */ - -#ifdef __SOBOL__ - -/* Skip initial numbers that for some dimensions have clear patterns that - * don't cover the entire sample space. Ideally we would have a better - * progressive pattern that doesn't suffer from this problem, because even - * with this offset some dimensions are quite poor. - */ -# define SOBOL_SKIP 64 - -ccl_device uint sobol_dimension(KernelGlobals kg, int index, int dimension) -{ - uint result = 0; - uint i = index + SOBOL_SKIP; - for (int j = 0, x; (x = find_first_set(i)); i >>= x) { - j += x; - result ^= __float_as_uint(kernel_tex_fetch(__sample_pattern_lut, 32 * dimension + j - 1)); - } - return result; -} - -#endif /* __SOBOL__ */ - -ccl_device_forceinline float path_rng_1D(KernelGlobals kg, - uint rng_hash, - int sample, - int dimension) -{ -#ifdef __DEBUG_CORRELATION__ - return (float)drand48(); -#endif - -#ifdef __SOBOL__ - if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_PMJ) -#endif - { - return pmj_sample_1D(kg, sample, rng_hash, dimension); - } - -#ifdef __SOBOL__ - /* Sobol sequence value using direction vectors. */ - uint result = sobol_dimension(kg, sample, dimension); - float r = (float)result * (1.0f / (float)0xFFFFFFFF); - - /* Cranly-Patterson rotation using rng seed */ - float shift; - - /* Hash rng with dimension to solve correlation issues. - * See T38710, T50116. - */ - uint tmp_rng = cmj_hash_simple(dimension, rng_hash); - shift = tmp_rng * (1.0f / (float)0xFFFFFFFF); - - return r + shift - floorf(r + shift); -#endif -} - -ccl_device_forceinline void path_rng_2D(KernelGlobals kg, - uint rng_hash, - int sample, - int dimension, - ccl_private float *fx, - ccl_private float *fy) -{ -#ifdef __DEBUG_CORRELATION__ - *fx = (float)drand48(); - *fy = (float)drand48(); - return; -#endif - -#ifdef __SOBOL__ - if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_PMJ) -#endif - { - pmj_sample_2D(kg, sample, rng_hash, dimension, fx, fy); - - return; - } - -#ifdef __SOBOL__ - /* Sobol. */ - *fx = path_rng_1D(kg, rng_hash, sample, dimension); - *fy = path_rng_1D(kg, rng_hash, sample, dimension + 1); -#endif -} - -/** - * 1D hash recommended from "Hash Functions for GPU Rendering" JCGT Vol. 9, No. 3, 2020 - * See https://www.shadertoy.com/view/4tXyWN and https://www.shadertoy.com/view/XlGcRh - * http://www.jcgt.org/published/0009/03/02/paper.pdf - */ -ccl_device_inline uint hash_iqint1(uint n) -{ - n = (n << 13U) ^ n; - n = n * (n * n * 15731U + 789221U) + 1376312589U; - - return n; -} - -/** - * 2D hash recommended from "Hash Functions for GPU Rendering" JCGT Vol. 9, No. 3, 2020 - * See https://www.shadertoy.com/view/4tXyWN and https://www.shadertoy.com/view/XlGcRh - * http://www.jcgt.org/published/0009/03/02/paper.pdf - */ -ccl_device_inline uint hash_iqnt2d(const uint x, const uint y) -{ - const uint qx = 1103515245U * ((x >> 1U) ^ (y)); - const uint qy = 1103515245U * ((y >> 1U) ^ (x)); - const uint n = 1103515245U * ((qx) ^ (qy >> 3U)); - - return n; -} - -ccl_device_inline uint path_rng_hash_init(KernelGlobals kg, - const int sample, - const int x, - const int y) -{ - const uint rng_hash = hash_iqnt2d(x, y) ^ kernel_data.integrator.seed; - -#ifdef __DEBUG_CORRELATION__ - srand48(rng_hash + sample); -#else - (void)sample; -#endif - - return rng_hash; -} - -/* Linear Congruential Generator */ - -ccl_device uint lcg_step_uint(uint *rng) -{ - /* implicit mod 2^32 */ - *rng = (1103515245 * (*rng) + 12345); - return *rng; -} - -ccl_device float lcg_step_float(uint *rng) -{ - /* implicit mod 2^32 */ - *rng = (1103515245 * (*rng) + 12345); - return (float)*rng * (1.0f / (float)0xFFFFFFFF); -} - -ccl_device uint lcg_init(uint seed) -{ - uint rng = seed; - lcg_step_uint(&rng); - return rng; -} - -ccl_device_inline uint lcg_state_init(const uint rng_hash, - const uint rng_offset, - const uint sample, - const uint scramble) -{ - return lcg_init(rng_hash + rng_offset + sample * scramble); -} - -ccl_device_inline bool sample_is_even(int pattern, int sample) -{ - if (pattern == SAMPLING_PATTERN_PMJ) { - /* See Section 10.2.1, "Progressive Multi-Jittered Sample Sequences", Christensen et al. - * We can use this to get divide sample sequence into two classes for easier variance - * estimation. */ -#if defined(__GNUC__) && !defined(__KERNEL_GPU__) - return __builtin_popcount(sample & 0xaaaaaaaa) & 1; -#elif defined(__NVCC__) - return __popc(sample & 0xaaaaaaaa) & 1; -#else - /* TODO(Stefan): pop-count intrinsic for Windows with fallback for older CPUs. */ - int i = sample & 0xaaaaaaaa; - i = i - ((i >> 1) & 0x55555555); - i = (i & 0x33333333) + ((i >> 2) & 0x33333333); - i = (((i + (i >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24; - return i & 1; -#endif - } - else { - /* TODO(Stefan): Are there reliable ways of dividing CMJ and Sobol into two classes? */ - return sample & 0x1; - } -} - -CCL_NAMESPACE_END |