forked from bartvdbraak/blender
165 lines
4.4 KiB
C
165 lines
4.4 KiB
C
/*
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* Copyright 2011-2013 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_COMPAT_CUDA_H__
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#define __KERNEL_COMPAT_CUDA_H__
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#define __KERNEL_GPU__
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#define __KERNEL_CUDA__
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#define CCL_NAMESPACE_BEGIN
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#define CCL_NAMESPACE_END
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/* Selective nodes compilation. */
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#ifndef __NODES_MAX_GROUP__
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# define __NODES_MAX_GROUP__ NODE_GROUP_LEVEL_MAX
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#endif
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#ifndef __NODES_FEATURES__
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# define __NODES_FEATURES__ NODE_FEATURE_ALL
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#endif
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#include <cuda.h>
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#include <cuda_fp16.h>
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#include <float.h>
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#include <stdint.h>
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/* Qualifier wrappers for different names on different devices */
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#define ccl_device __device__ __inline__
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# define ccl_device_forceinline __device__ __forceinline__
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#if __CUDA_ARCH__ < 500
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# define ccl_device_inline __device__ __forceinline__
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#else
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# define ccl_device_inline __device__ __inline__
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#endif
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#define ccl_device_noinline __device__ __noinline__
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#define ccl_global
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#define ccl_constant
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#define ccl_local __shared__
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#define ccl_local_param
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#define ccl_private
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#define ccl_may_alias
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#define ccl_addr_space
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#define ccl_restrict __restrict__
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#define ccl_align(n) __align__(n)
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/* No assert supported for CUDA */
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#define kernel_assert(cond)
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/* Types */
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#include "util/util_half.h"
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#include "util/util_types.h"
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/* Work item functions */
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ccl_device_inline uint ccl_local_id(uint d)
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{
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switch(d) {
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case 0: return threadIdx.x;
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case 1: return threadIdx.y;
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case 2: return threadIdx.z;
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default: return 0;
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}
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}
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#define ccl_global_id(d) (ccl_group_id(d) * ccl_local_size(d) + ccl_local_id(d))
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ccl_device_inline uint ccl_local_size(uint d)
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{
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switch(d) {
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case 0: return blockDim.x;
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case 1: return blockDim.y;
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case 2: return blockDim.z;
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default: return 0;
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}
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}
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#define ccl_global_size(d) (ccl_num_groups(d) * ccl_local_size(d))
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ccl_device_inline uint ccl_group_id(uint d)
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{
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switch(d) {
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case 0: return blockIdx.x;
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case 1: return blockIdx.y;
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case 2: return blockIdx.z;
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default: return 0;
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}
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}
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ccl_device_inline uint ccl_num_groups(uint d)
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{
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switch(d) {
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case 0: return gridDim.x;
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case 1: return gridDim.y;
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case 2: return gridDim.z;
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default: return 0;
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}
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}
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/* Textures */
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typedef texture<float4, 1> texture_float4;
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typedef texture<float2, 1> texture_float2;
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typedef texture<float, 1> texture_float;
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typedef texture<uint, 1> texture_uint;
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typedef texture<int, 1> texture_int;
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typedef texture<uint4, 1> texture_uint4;
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typedef texture<uchar, 1> texture_uchar;
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typedef texture<uchar4, 1> texture_uchar4;
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typedef texture<float4, 2> texture_image_float4;
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typedef texture<float4, 3> texture_image3d_float4;
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typedef texture<uchar4, 2, cudaReadModeNormalizedFloat> texture_image_uchar4;
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/* Macros to handle different memory storage on different devices */
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/* On Fermi cards (4xx and 5xx), we use regular textures for both data and images.
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* On Kepler (6xx) and above, we use Bindless Textures for images and arrays for data.
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*
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* Arrays are necessary in order to use the full VRAM on newer cards, and it's slightly faster.
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* Using Arrays on Fermi turned out to be slower.*/
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/* Fermi */
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#if __CUDA_ARCH__ < 300
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# define __KERNEL_CUDA_TEX_STORAGE__
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# define kernel_tex_fetch(t, index) tex1Dfetch(t, index)
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# define kernel_tex_image_interp(t, x, y) tex2D(t, x, y)
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# define kernel_tex_image_interp_3d(t, x, y, z) tex3D(t, x, y, z)
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/* Kepler */
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#else
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# define kernel_tex_fetch(t, index) t[(index)]
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# define kernel_tex_image_interp_float4(t, x, y) tex2D<float4>(t, x, y)
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# define kernel_tex_image_interp_float(t, x, y) tex2D<float>(t, x, y)
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# define kernel_tex_image_interp_3d_float4(t, x, y, z) tex3D<float4>(t, x, y, z)
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# define kernel_tex_image_interp_3d_float(t, x, y, z) tex3D<float>(t, x, y, z)
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#endif
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#define kernel_data __data
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/* Use fast math functions */
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#define cosf(x) __cosf(((float)(x)))
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#define sinf(x) __sinf(((float)(x)))
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#define powf(x, y) __powf(((float)(x)), ((float)(y)))
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#define tanf(x) __tanf(((float)(x)))
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#define logf(x) __logf(((float)(x)))
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#define expf(x) __expf(((float)(x)))
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#endif /* __KERNEL_COMPAT_CUDA_H__ */
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