Cycles: OpenCL kernel split

This commit contains all the work related on the AMD megakernel split work
which was mainly done by Varun Sundar, George Kyriazis and Lenny Wang, plus
some help from Sergey Sharybin, Martijn Berger, Thomas Dinges and likely
someone else which we're forgetting to mention.

Currently only AMD cards are enabled for the new split kernel, but it is
possible to force split opencl kernel to be used by setting the following
environment variable: CYCLES_OPENCL_SPLIT_KERNEL_TEST=1.

Not all the features are supported yet, and that being said no motion blur,
camera blur, SSS and volumetrics for now. Also transparent shadows are
disabled on AMD device because of some compiler bug.

This kernel is also only implements regular path tracing and supporting
branched one will take a bit. Branched path tracing is exposed to the
interface still, which is a bit misleading and will be hidden there soon.

More feature will be enabled once they're ported to the split kernel and
tested.

Neither regular CPU nor CUDA has any difference, they're generating the
same exact code, which means no regressions/improvements there.

Based on the research paper:

  https://research.nvidia.com/sites/default/files/publications/laine2013hpg_paper.pdf

Here's the documentation:

  https://docs.google.com/document/d/1LuXW-CV-sVJkQaEGZlMJ86jZ8FmoPfecaMdR-oiWbUY/edit

Design discussion of the patch:

  https://developer.blender.org/T44197

Differential Revision: https://developer.blender.org/D1200

1 files changed, 9 insertions, 9 deletions

diff --git a/intern/cycles/kernel/kernel_random.h b/intern/cycles/kernel/kernel_random.h
index 40767bac013..631a2cb75de 100644
--- a/intern/cycles/kernel/kernel_random.h
+++ b/intern/cycles/kernel/kernel_random.h
@@ -98,7 +98,7 @@ ccl_device uint sobol_lookup(const uint m, const uint frame, const uint ex, cons
 	return index;
 }
 
-ccl_device_inline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension)
+ccl_device_inline float path_rng_1D(KernelGlobals *kg, ccl_addr_space RNG *rng, int sample, int num_samples, int dimension)
 {
 #ifdef __CMJ__
 	if(kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_CMJ) {
@@ -132,7 +132,7 @@ ccl_device_inline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample, int
 #endif
 }
 
-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, ccl_addr_space RNG *rng, int sample, int num_samples, int dimension, float *fx, float *fy)
 {
 #ifdef __CMJ__
 	if(kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_CMJ) {
@@ -149,7 +149,7 @@ ccl_device_inline void path_rng_2D(KernelGlobals *kg, RNG *rng, int sample, int
 	}
 }
 
-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, ccl_addr_space RNG *rng, int x, int y, float *fx, float *fy)
 {
 #ifdef __SOBOL_FULL_SCREEN__
 	uint px, py;
@@ -261,12 +261,12 @@ ccl_device uint lcg_init(uint seed)
  * For branches in the path we must be careful not to reuse the same number
  * in a sequence and offset accordingly. */
 
-ccl_device_inline float path_state_rng_1D(KernelGlobals *kg, RNG *rng, const PathState *state, int dimension)
+ccl_device_inline float path_state_rng_1D(KernelGlobals *kg, ccl_addr_space 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);
 }
 
-ccl_device_inline float path_state_rng_1D_for_decision(KernelGlobals *kg, RNG *rng, const PathState *state, int dimension)
+ccl_device_inline float path_state_rng_1D_for_decision(KernelGlobals *kg, ccl_addr_space RNG *rng, const ccl_addr_space PathState *state, int dimension)
 {
 	/* the rng_offset is not increased for transparent bounces. if we do then
 	 * fully transparent objects can become subtly visible by the different
@@ -279,23 +279,23 @@ ccl_device_inline float path_state_rng_1D_for_decision(KernelGlobals *kg, RNG *r
 	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 PathState *state, int dimension, float *fx, float *fy)
+ccl_device_inline void path_state_rng_2D(KernelGlobals *kg, ccl_addr_space 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);
 }
 
-ccl_device_inline float path_branched_rng_1D(KernelGlobals *kg, RNG *rng, const PathState *state, int branch, int num_branches, int dimension)
+ccl_device_inline float path_branched_rng_1D(KernelGlobals *kg, ccl_addr_space RNG *rng, const 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);
 }
 
-ccl_device_inline float path_branched_rng_1D_for_decision(KernelGlobals *kg, RNG *rng, const PathState *state, int branch, int num_branches, int dimension)
+ccl_device_inline float path_branched_rng_1D_for_decision(KernelGlobals *kg, ccl_addr_space RNG *rng, const 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);
 }
 
-ccl_device_inline void path_branched_rng_2D(KernelGlobals *kg, RNG *rng, const PathState *state, int branch, int num_branches, int dimension, float *fx, float *fy)
+ccl_device_inline void path_branched_rng_2D(KernelGlobals *kg, ccl_addr_space RNG *rng, const 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);
 }