0803119725
This includes much improved GPU rendering performance, viewport interactivity, new shadow catcher, revamped sampling settings, subsurface scattering anisotropy, new GPU volume sampling, improved PMJ sampling pattern, and more. Some features have also been removed or changed, breaking backwards compatibility. Including the removal of the OpenCL backend, for which alternatives are under development. Release notes and code docs: https://wiki.blender.org/wiki/Reference/Release_Notes/3.0/Cycles https://wiki.blender.org/wiki/Source/Render/Cycles Credits: * Sergey Sharybin * Brecht Van Lommel * Patrick Mours (OptiX backend) * Christophe Hery (subsurface scattering anisotropy) * William Leeson (PMJ sampling pattern) * Alaska (various fixes and tweaks) * Thomas Dinges (various fixes) For the full commit history, see the cycles-x branch. This squashes together all the changes since intermediate changes would often fail building or tests. Ref T87839, T87837, T87836 Fixes T90734, T89353, T80267, T80267, T77185, T69800
84 lines
2.3 KiB
C++
84 lines
2.3 KiB
C++
/*
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* Copyright 2021 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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#pragma once
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CCL_NAMESPACE_BEGIN
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/* Parallel sum of array input_data with size n into output_sum.
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*
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* Adapted from "Optimizing Parallel Reduction in GPU", Mark Harris.
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*
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* This version adds multiple elements per thread sequentially. This reduces
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* the overall cost of the algorithm while keeping the work complexity O(n) and
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* the step complexity O(log n). (Brent's Theorem optimization) */
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#define GPU_PARALLEL_SUM_DEFAULT_BLOCK_SIZE 512
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template<uint blocksize, typename InputT, typename OutputT, typename ConvertOp>
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__device__ void gpu_parallel_sum(
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const InputT *input_data, const uint n, OutputT *output_sum, OutputT zero, ConvertOp convert)
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{
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extern ccl_gpu_shared OutputT shared_data[];
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const uint tid = ccl_gpu_thread_idx_x;
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const uint gridsize = blocksize * ccl_gpu_grid_dim_x();
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OutputT sum = zero;
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for (uint i = ccl_gpu_block_idx_x * blocksize + tid; i < n; i += gridsize) {
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sum += convert(input_data[i]);
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}
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shared_data[tid] = sum;
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ccl_gpu_syncthreads();
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if (blocksize >= 512 && tid < 256) {
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shared_data[tid] = sum = sum + shared_data[tid + 256];
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}
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ccl_gpu_syncthreads();
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if (blocksize >= 256 && tid < 128) {
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shared_data[tid] = sum = sum + shared_data[tid + 128];
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}
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ccl_gpu_syncthreads();
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if (blocksize >= 128 && tid < 64) {
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shared_data[tid] = sum = sum + shared_data[tid + 64];
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}
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ccl_gpu_syncthreads();
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if (blocksize >= 64 && tid < 32) {
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shared_data[tid] = sum = sum + shared_data[tid + 32];
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}
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ccl_gpu_syncthreads();
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if (tid < 32) {
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for (int offset = ccl_gpu_warp_size / 2; offset > 0; offset /= 2) {
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sum += ccl_shfl_down_sync(0xFFFFFFFF, sum, offset);
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}
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}
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if (tid == 0) {
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output_sum[ccl_gpu_block_idx_x] = sum;
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}
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}
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CCL_NAMESPACE_END
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