fd397a7d28
It is defined to & for CPU side compilation, and defined to an empty for any GPU platform. The idea here is to use this macro instead of #ifdef block with bunch of duplicated lines just to make it so CPU code is efficient. Eventually we might switch to references on CUDA as well, but that would require some intensive testing.
174 lines
4.7 KiB
C
174 lines
4.7 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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/* TODO(sergey): In theory we might use references with CUDA, however
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* performance impact yet to be investigated.
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*/
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#define ccl_ref
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#define ccl_align(n) __align__(n)
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#define ATTR_FALLTHROUGH
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#define CCL_MAX_LOCAL_SIZE (CUDA_THREADS_BLOCK_WIDTH*CUDA_THREADS_BLOCK_WIDTH)
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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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