47da8dcbca
This patch puts threads that render the same pixel closer together, as opposed to threads that render the same sample. Thus threads within a warp are more coherent in memory access and control flow, leading to performance improvements. Example benchmarks on a Quadro RTX4000 (WDDM) on Windows 10: Koro: 4:23 -> 3:46 BMW: 1:18 -> 1:25 Barbershop Interior: 17:52 -> 14:55 Classroom: 4:37 -> 3:45 Performance differences on OpenCL/AMD were hit and miss, some scenes became faster, others lost significantly. Therefore, this is kept as CUDA only change for now.
89 lines
3.0 KiB
C
89 lines
3.0 KiB
C
/*
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* Copyright 2011-2015 Blender Foundation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef __KERNEL_WORK_STEALING_H__
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#define __KERNEL_WORK_STEALING_H__
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CCL_NAMESPACE_BEGIN
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/*
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* Utility functions for work stealing
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*/
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#ifdef __KERNEL_OPENCL__
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# pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics : enable
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#endif
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#ifdef __SPLIT_KERNEL__
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/* Returns true if there is work */
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ccl_device bool get_next_work(KernelGlobals *kg,
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ccl_global uint *work_pools,
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uint total_work_size,
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uint ray_index,
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ccl_private uint *global_work_index)
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{
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/* With a small amount of work there may be more threads than work due to
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* rounding up of global size, stop such threads immediately. */
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if(ray_index >= total_work_size) {
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return false;
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}
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/* Increase atomic work index counter in pool. */
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uint pool = ray_index / WORK_POOL_SIZE;
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uint work_index = atomic_fetch_and_inc_uint32(&work_pools[pool]);
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/* Map per-pool work index to a global work index. */
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uint global_size = ccl_global_size(0) * ccl_global_size(1);
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kernel_assert(global_size % WORK_POOL_SIZE == 0);
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kernel_assert(ray_index < global_size);
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*global_work_index = (work_index / WORK_POOL_SIZE) * global_size
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+ (pool * WORK_POOL_SIZE)
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+ (work_index % WORK_POOL_SIZE);
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/* Test if all work for this pool is done. */
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return (*global_work_index < total_work_size);
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}
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#endif
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/* Map global work index to tile, pixel X/Y and sample. */
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ccl_device_inline void get_work_pixel(ccl_global const WorkTile *tile,
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uint global_work_index,
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ccl_private uint *x,
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ccl_private uint *y,
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ccl_private uint *sample)
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{
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#ifdef __KERNEL_CUDA__
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/* Keeping threads for the same pixel together improves performance on CUDA. */
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uint sample_offset = global_work_index % tile->num_samples;
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uint pixel_offset = global_work_index / tile->num_samples;
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#else /* __KERNEL_CUDA__ */
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uint tile_pixels = tile->w * tile->h;
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uint sample_offset = global_work_index / tile_pixels;
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uint pixel_offset = global_work_index - sample_offset * tile_pixels;
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#endif /* __KERNEL_CUDA__ */
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uint y_offset = pixel_offset / tile->w;
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uint x_offset = pixel_offset - y_offset * tile->w;
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*x = tile->x + x_offset;
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*y = tile->y + y_offset;
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*sample = tile->start_sample + sample_offset;
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}
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CCL_NAMESPACE_END
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#endif /* __KERNEL_WORK_STEALING_H__ */
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