blender/intern/cycles/kernel/kernel_compat_cuda.h
Thomas Dinges c9f1ed1e4c Cycles: Add support for bindless textures.
This adds support for CUDA Texture objects (also known as Bindless textures) for Kepler GPUs (Geforce 6xx and above).
This is used for all 2D/3D textures, data still uses arrays as before.

User benefits:
* No more limits of image textures on Kepler.
 We had 5 float4 and 145 byte4 slots there before, now we have 1024 float4 and 1024 byte4.
 This can be extended further if we need to (just change the define).

* Single channel textures slots (byte and float) are now supported on Kepler as well (1024 slots for each type).

ToDo / Issues:
* 3D textures don't work yet, at least don't show up during render. I have no idea whats wrong yet.
* Dynamically allocate bindless_mapping array?

I hope Fermi still works fine, but that should be tested on a Fermi card before pushing to master.

Part of my GSoC 2016.

Reviewers: sergey, #cycles, brecht

Subscribers: swerner, jtheninja, brecht, sergey

Differential Revision: https://developer.blender.org/D1999
2016-05-19 13:14:37 +02:00

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C

/*
* Copyright 2011-2013 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef __KERNEL_COMPAT_CUDA_H__
#define __KERNEL_COMPAT_CUDA_H__
#define __KERNEL_GPU__
#define __KERNEL_CUDA__
#define CCL_NAMESPACE_BEGIN
#define CCL_NAMESPACE_END
/* Selective nodes compilation. */
#ifndef __NODES_MAX_GROUP__
# define __NODES_MAX_GROUP__ NODE_GROUP_LEVEL_MAX
#endif
#ifndef __NODES_FEATURES__
# define __NODES_FEATURES__ NODE_FEATURE_ALL
#endif
#include <cuda.h>
#include <float.h>
/* Qualifier wrappers for different names on different devices */
#define ccl_device __device__ __inline__
#define ccl_device_inline __device__ __inline__
#define ccl_device_noinline __device__ __noinline__
#define ccl_global
#define ccl_constant
#define ccl_may_alias
#define ccl_addr_space
/* No assert supported for CUDA */
#define kernel_assert(cond)
/* Types */
#include "util_half.h"
#include "util_types.h"
/* Textures */
typedef texture<float4, 1> texture_float4;
typedef texture<float2, 1> texture_float2;
typedef texture<float, 1> texture_float;
typedef texture<uint, 1> texture_uint;
typedef texture<int, 1> texture_int;
typedef texture<uint4, 1> texture_uint4;
typedef texture<uchar4, 1> texture_uchar4;
typedef texture<float4, 2> texture_image_float4;
typedef texture<float4, 3> texture_image3d_float4;
typedef texture<uchar4, 2, cudaReadModeNormalizedFloat> texture_image_uchar4;
/* Macros to handle different memory storage on different devices */
/* On Fermi cards (4xx and 5xx), we use regular textures for both data and images.
* On Kepler (6xx) and above, we use Bindless Textures for images and arrays for data.
*
* Arrays are necessary in order to use the full VRAM on newer cards, and it's slightly faster.
* Using Arrays on Fermi turned out to be slower.*/
/* Fermi */
#if __CUDA_ARCH__ < 300
# define __KERNEL_CUDA_TEX_STORAGE__
# define kernel_tex_fetch(t, index) tex1Dfetch(t, index)
# define kernel_tex_image_interp(t, x, y) tex2D(t, x, y)
# define kernel_tex_image_interp_3d(t, x, y, z) tex3D(t, x, y, z)
/* Kepler */
#else
# define kernel_tex_fetch(t, index) t[(index)]
# define kernel_tex_image_interp_float4(t, x, y) tex2D<float4>(t, x, y)
# define kernel_tex_image_interp_float(t, x, y) tex2D<float>(t, x, y)
# define kernel_tex_image_interp_3d_float4(t, x, y, z) tex3D<float4>(t, x, y, z)
# define kernel_tex_image_interp_3d_float(t, x, y, z) tex3D<float>(t, x, y, z)
#endif
#define kernel_data __data
/* Use fast math functions */
#define cosf(x) __cosf(((float)(x)))
#define sinf(x) __sinf(((float)(x)))
#define powf(x, y) __powf(((float)(x)), ((float)(y)))
#define tanf(x) __tanf(((float)(x)))
#define logf(x) __logf(((float)(x)))
#define expf(x) __expf(((float)(x)))
#endif /* __KERNEL_COMPAT_CUDA_H__ */