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blender/intern/cycles/device/device_cuda.cpp
Lukas Stockner 43b374e8c5 Cycles: Implement denoising option for reducing noise in the rendered image 
This commit contains the first part of the new Cycles denoising option,
which filters the resulting image using information gathered during rendering
to get rid of noise while preserving visual features as well as possible.

To use the option, enable it in the render layer options. The default settings
fit a wide range of scenes, but the user can tweak individual settings to
control the tradeoff between a noise-free image, image details, and calculation
time.

Note that the denoiser may still change in the future and that some features
are not implemented yet. The most important missing feature is animation
denoising, which uses information from multiple frames at once to produce a
flicker-free and smoother result. These features will be added in the future.

Finally, thanks to all the people who supported this project:

- Google (through the GSoC) and Theory Studios for sponsoring the development
- The authors of the papers I used for implementing the denoiser (more details
  on them will be included in the technical docs)
- The other Cycles devs for feedback on the code, especially Sergey for
  mentoring the GSoC project and Brecht for the code review!
- And of course the users who helped with testing, reported bugs and things
  that could and/or should work better!
2017-05-07 14:40:58 +02:00

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/*
* 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.
*/
#include <climits>
#include <limits.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "device/device.h"
#include "device/device_denoising.h"
#include "device/device_intern.h"
#include "device/device_split_kernel.h"
#include "render/buffers.h"
#include "kernel/filter/filter_defines.h"
#ifdef WITH_CUDA_DYNLOAD
# include "cuew.h"
#else
# include "util/util_opengl.h"
# include <cuda.h>
# include <cudaGL.h>
#endif
#include "util/util_debug.h"
#include "util/util_logging.h"
#include "util/util_map.h"
#include "util/util_md5.h"
#include "util/util_opengl.h"
#include "util/util_path.h"
#include "util/util_string.h"
#include "util/util_system.h"
#include "util/util_types.h"
#include "util/util_time.h"
#include "kernel/split/kernel_split_data_types.h"
CCL_NAMESPACE_BEGIN
#ifndef WITH_CUDA_DYNLOAD
/* Transparently implement some functions, so majority of the file does not need
* to worry about difference between dynamically loaded and linked CUDA at all.
*/
namespace {
const char *cuewErrorString(CUresult result)
{
/* We can only give error code here without major code duplication, that
* should be enough since dynamic loading is only being disabled by folks
* who knows what they're doing anyway.
*
* NOTE: Avoid call from several threads.
*/
static string error;
error = string_printf("%d", result);
return error.c_str();
}
const char *cuewCompilerPath(void)
{
return CYCLES_CUDA_NVCC_EXECUTABLE;
}
int cuewCompilerVersion(void)
{
return (CUDA_VERSION / 100) + (CUDA_VERSION % 100 / 10);
}
} /* namespace */
#endif /* WITH_CUDA_DYNLOAD */
class CUDADevice;
class CUDASplitKernel : public DeviceSplitKernel {
CUDADevice *device;
public:
explicit CUDASplitKernel(CUDADevice *device);
virtual uint64_t state_buffer_size(device_memory& kg, device_memory& data, size_t num_threads);
virtual bool enqueue_split_kernel_data_init(const KernelDimensions& dim,
RenderTile& rtile,
int num_global_elements,
device_memory& kernel_globals,
device_memory& kernel_data_,
device_memory& split_data,
device_memory& ray_state,
device_memory& queue_index,
device_memory& use_queues_flag,
device_memory& work_pool_wgs);
virtual SplitKernelFunction* get_split_kernel_function(string kernel_name, const DeviceRequestedFeatures&);
virtual int2 split_kernel_local_size();
virtual int2 split_kernel_global_size(device_memory& kg, device_memory& data, DeviceTask *task);
};
class CUDADevice : public Device
{
public:
DedicatedTaskPool task_pool;
CUdevice cuDevice;
CUcontext cuContext;
CUmodule cuModule, cuFilterModule;
map<device_ptr, bool> tex_interp_map;
map<device_ptr, uint> tex_bindless_map;
int cuDevId;
int cuDevArchitecture;
bool first_error;
CUDASplitKernel *split_kernel;
struct PixelMem {
GLuint cuPBO;
CUgraphicsResource cuPBOresource;
GLuint cuTexId;
int w, h;
};
map<device_ptr, PixelMem> pixel_mem_map;
/* Bindless Textures */
device_vector<uint> bindless_mapping;
bool need_bindless_mapping;
CUdeviceptr cuda_device_ptr(device_ptr mem)
{
return (CUdeviceptr)mem;
}
static bool have_precompiled_kernels()
{
string cubins_path = path_get("lib");
return path_exists(cubins_path);
}
virtual bool show_samples() const
{
/* The CUDADevice only processes one tile at a time, so showing samples is fine. */
return true;
}
/*#ifdef NDEBUG
#define cuda_abort()
#else
#define cuda_abort() abort()
#endif*/
void cuda_error_documentation()
{
if(first_error) {
fprintf(stderr, "\nRefer to the Cycles GPU rendering documentation for possible solutions:\n");
fprintf(stderr, "https://docs.blender.org/manual/en/dev/render/cycles/gpu_rendering.html\n\n");
first_error = false;
}
}
#define cuda_assert(stmt) \
{ \
CUresult result = stmt; \
\
if(result != CUDA_SUCCESS) { \
string message = string_printf("CUDA error: %s in %s, line %d", cuewErrorString(result), #stmt, __LINE__); \
if(error_msg == "") \
error_msg = message; \
fprintf(stderr, "%s\n", message.c_str()); \
/*cuda_abort();*/ \
cuda_error_documentation(); \
} \
} (void)0
bool cuda_error_(CUresult result, const string& stmt)
{
if(result == CUDA_SUCCESS)
return false;
string message = string_printf("CUDA error at %s: %s", stmt.c_str(), cuewErrorString(result));
if(error_msg == "")
error_msg = message;
fprintf(stderr, "%s\n", message.c_str());
cuda_error_documentation();
return true;
}
#define cuda_error(stmt) cuda_error_(stmt, #stmt)
void cuda_error_message(const string& message)
{
if(error_msg == "")
error_msg = message;
fprintf(stderr, "%s\n", message.c_str());
cuda_error_documentation();
}
void cuda_push_context()
{
cuda_assert(cuCtxSetCurrent(cuContext));
}
void cuda_pop_context()
{
cuda_assert(cuCtxSetCurrent(NULL));
}
CUDADevice(DeviceInfo& info, Stats &stats, bool background_)
: Device(info, stats, background_)
{
first_error = true;
background = background_;
cuDevId = info.num;
cuDevice = 0;
cuContext = 0;
split_kernel = NULL;
need_bindless_mapping = false;
/* intialize */
if(cuda_error(cuInit(0)))
return;
/* setup device and context */
if(cuda_error(cuDeviceGet(&cuDevice, cuDevId)))
return;
CUresult result;
if(background) {
result = cuCtxCreate(&cuContext, 0, cuDevice);
}
else {
result = cuGLCtxCreate(&cuContext, 0, cuDevice);
if(result != CUDA_SUCCESS) {
result = cuCtxCreate(&cuContext, 0, cuDevice);
background = true;
}
}
if(cuda_error_(result, "cuCtxCreate"))
return;
int major, minor;
cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevId);
cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevId);
cuDevArchitecture = major*100 + minor*10;
cuda_pop_context();
}
~CUDADevice()
{
task_pool.stop();
delete split_kernel;
if(info.has_bindless_textures) {
tex_free(bindless_mapping);
}
cuda_assert(cuCtxDestroy(cuContext));
}
bool support_device(const DeviceRequestedFeatures& /*requested_features*/)
{
int major, minor;
cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevId);
cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevId);
/* We only support sm_20 and above */
if(major < 2) {
cuda_error_message(string_printf("CUDA device supported only with compute capability 2.0 or up, found %d.%d.", major, minor));
return false;
}
return true;
}
bool use_adaptive_compilation()
{
return DebugFlags().cuda.adaptive_compile;
}
bool use_split_kernel()
{
return DebugFlags().cuda.split_kernel;
}
/* Common NVCC flags which stays the same regardless of shading model,
* kernel sources md5 and only depends on compiler or compilation settings.
*/
string compile_kernel_get_common_cflags(
const DeviceRequestedFeatures& requested_features,
bool filter=false, bool split=false)
{
const int cuda_version = cuewCompilerVersion();
const int machine = system_cpu_bits();
const string source_path = path_get("source");
const string include_path = source_path;
string cflags = string_printf("-m%d "
"--ptxas-options=\"-v\" "
"--use_fast_math "
"-DNVCC "
"-D__KERNEL_CUDA_VERSION__=%d "
"-I\"%s\"",
machine,
cuda_version,
include_path.c_str());
if(!filter && use_adaptive_compilation()) {
cflags += " " + requested_features.get_build_options();
}
const char *extra_cflags = getenv("CYCLES_CUDA_EXTRA_CFLAGS");
if(extra_cflags) {
cflags += string(" ") + string(extra_cflags);
}
#ifdef WITH_CYCLES_DEBUG
cflags += " -D__KERNEL_DEBUG__";
#endif
if(split) {
cflags += " -D__SPLIT__";
}
return cflags;
}
bool compile_check_compiler() {
const char *nvcc = cuewCompilerPath();
if(nvcc == NULL) {
cuda_error_message("CUDA nvcc compiler not found. "
"Install CUDA toolkit in default location.");
return false;
}
const int cuda_version = cuewCompilerVersion();
VLOG(1) << "Found nvcc " << nvcc
<< ", CUDA version " << cuda_version
<< ".";
const int major = cuda_version / 10, minor = cuda_version & 10;
if(cuda_version == 0) {
cuda_error_message("CUDA nvcc compiler version could not be parsed.");
return false;
}
if(cuda_version < 80) {
printf("Unsupported CUDA version %d.%d detected, "
"you need CUDA 8.0 or newer.\n",
major, minor);
return false;
}
else if(cuda_version != 80) {
printf("CUDA version %d.%d detected, build may succeed but only "
"CUDA 8.0 is officially supported.\n",
major, minor);
}
return true;
}
string compile_kernel(const DeviceRequestedFeatures& requested_features,
bool filter=false, bool split=false)
{
const char *name, *source;
if(filter) {
name = "filter";
source = "filter.cu";
}
else if(split) {
name = "kernel_split";
source = "kernel_split.cu";
}
else {
name = "kernel";
source = "kernel.cu";
}
/* Compute cubin name. */
int major, minor;
cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevId);
cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevId);
/* Attempt to use kernel provided with Blender. */
if(!use_adaptive_compilation()) {
const string cubin = path_get(string_printf("lib/%s_sm_%d%d.cubin",
name, major, minor));
VLOG(1) << "Testing for pre-compiled kernel " << cubin << ".";
if(path_exists(cubin)) {
VLOG(1) << "Using precompiled kernel.";
return cubin;
}
}
const string common_cflags =
compile_kernel_get_common_cflags(requested_features, filter, split);
/* Try to use locally compiled kernel. */
const string source_path = path_get("source");
const string kernel_md5 = path_files_md5_hash(source_path);
/* We include cflags into md5 so changing cuda toolkit or changing other
* compiler command line arguments makes sure cubin gets re-built.
*/
const string cubin_md5 = util_md5_string(kernel_md5 + common_cflags);
const string cubin_file = string_printf("cycles_%s_sm%d%d_%s.cubin",
name, major, minor,
cubin_md5.c_str());
const string cubin = path_cache_get(path_join("kernels", cubin_file));
VLOG(1) << "Testing for locally compiled kernel " << cubin << ".";
if(path_exists(cubin)) {
VLOG(1) << "Using locally compiled kernel.";
return cubin;
}
#ifdef _WIN32
if(have_precompiled_kernels()) {
if(major < 2) {
cuda_error_message(string_printf(
"CUDA device requires compute capability 2.0 or up, "
"found %d.%d. Your GPU is not supported.",
major, minor));
}
else {
cuda_error_message(string_printf(
"CUDA binary kernel for this graphics card compute "
"capability (%d.%d) not found.",
major, minor));
}
return "";
}
#endif
/* Compile. */
if(!compile_check_compiler()) {
return "";
}
const char *nvcc = cuewCompilerPath();
const string kernel = path_join(
path_join(source_path, "kernel"),
path_join("kernels",
path_join("cuda", source)));
double starttime = time_dt();
printf("Compiling CUDA kernel ...\n");
path_create_directories(cubin);
string command = string_printf("\"%s\" "
"-arch=sm_%d%d "
"--cubin \"%s\" "
"-o \"%s\" "
"%s ",
nvcc,
major, minor,
kernel.c_str(),
cubin.c_str(),
common_cflags.c_str());
printf("%s\n", command.c_str());
if(system(command.c_str()) == -1) {
cuda_error_message("Failed to execute compilation command, "
"see console for details.");
return "";
}
/* Verify if compilation succeeded */
if(!path_exists(cubin)) {
cuda_error_message("CUDA kernel compilation failed, "
"see console for details.");
return "";
}
printf("Kernel compilation finished in %.2lfs.\n", time_dt() - starttime);
return cubin;
}
bool load_kernels(const DeviceRequestedFeatures& requested_features)
{
/* check if cuda init succeeded */
if(cuContext == 0)
return false;
/* check if GPU is supported */
if(!support_device(requested_features))
return false;
/* get kernel */
string cubin = compile_kernel(requested_features, false, use_split_kernel());
if(cubin == "")
return false;
string filter_cubin = compile_kernel(requested_features, true, false);
if(filter_cubin == "")
return false;
/* open module */
cuda_push_context();
string cubin_data;
CUresult result;
if(path_read_text(cubin, cubin_data))
result = cuModuleLoadData(&cuModule, cubin_data.c_str());
else
result = CUDA_ERROR_FILE_NOT_FOUND;
if(cuda_error_(result, "cuModuleLoad"))
cuda_error_message(string_printf("Failed loading CUDA kernel %s.", cubin.c_str()));
if(path_read_text(filter_cubin, cubin_data))
result = cuModuleLoadData(&cuFilterModule, cubin_data.c_str());
else
result = CUDA_ERROR_FILE_NOT_FOUND;
if(cuda_error_(result, "cuModuleLoad"))
cuda_error_message(string_printf("Failed loading CUDA kernel %s.", filter_cubin.c_str()));
cuda_pop_context();
return (result == CUDA_SUCCESS);
}
void load_bindless_mapping()
{
if(info.has_bindless_textures && need_bindless_mapping) {
tex_free(bindless_mapping);
tex_alloc("__bindless_mapping", bindless_mapping, INTERPOLATION_NONE, EXTENSION_REPEAT);
need_bindless_mapping = false;
}
}
void mem_alloc(const char *name, device_memory& mem, MemoryType /*type*/)
{
if(name) {
VLOG(1) << "Buffer allocate: " << name << ", "
<< string_human_readable_number(mem.memory_size()) << " bytes. ("
<< string_human_readable_size(mem.memory_size()) << ")";
}
cuda_push_context();
CUdeviceptr device_pointer;
size_t size = mem.memory_size();
cuda_assert(cuMemAlloc(&device_pointer, size));
mem.device_pointer = (device_ptr)device_pointer;
mem.device_size = size;
stats.mem_alloc(size);
cuda_pop_context();
}
void mem_copy_to(device_memory& mem)
{
cuda_push_context();
if(mem.device_pointer)
cuda_assert(cuMemcpyHtoD(cuda_device_ptr(mem.device_pointer), (void*)mem.data_pointer, mem.memory_size()));
cuda_pop_context();
}
void mem_copy_from(device_memory& mem, int y, int w, int h, int elem)
{
size_t offset = elem*y*w;
size_t size = elem*w*h;
cuda_push_context();
if(mem.device_pointer) {
cuda_assert(cuMemcpyDtoH((uchar*)mem.data_pointer + offset,
(CUdeviceptr)(mem.device_pointer + offset), size));
}
else {
memset((char*)mem.data_pointer + offset, 0, size);
}
cuda_pop_context();
}
void mem_zero(device_memory& mem)
{
if(mem.data_pointer) {
memset((void*)mem.data_pointer, 0, mem.memory_size());
}
cuda_push_context();
if(mem.device_pointer)
cuda_assert(cuMemsetD8(cuda_device_ptr(mem.device_pointer), 0, mem.memory_size()));
cuda_pop_context();
}
void mem_free(device_memory& mem)
{
if(mem.device_pointer) {
cuda_push_context();
cuda_assert(cuMemFree(cuda_device_ptr(mem.device_pointer)));
cuda_pop_context();
mem.device_pointer = 0;
stats.mem_free(mem.device_size);
mem.device_size = 0;
}
}
virtual device_ptr mem_alloc_sub_ptr(device_memory& mem, int offset, int /*size*/, MemoryType /*type*/)
{
return (device_ptr) (((char*) mem.device_pointer) + mem.memory_elements_size(offset));
}
void const_copy_to(const char *name, void *host, size_t size)
{
CUdeviceptr mem;
size_t bytes;
cuda_push_context();
cuda_assert(cuModuleGetGlobal(&mem, &bytes, cuModule, name));
//assert(bytes == size);
cuda_assert(cuMemcpyHtoD(mem, host, size));
cuda_pop_context();
}
void tex_alloc(const char *name,
device_memory& mem,
InterpolationType interpolation,
ExtensionType extension)
{
VLOG(1) << "Texture allocate: " << name << ", "
<< string_human_readable_number(mem.memory_size()) << " bytes. ("
<< string_human_readable_size(mem.memory_size()) << ")";
/* Check if we are on sm_30 or above.
* We use arrays and bindles textures for storage there */
bool has_bindless_textures = info.has_bindless_textures;
/* General variables for both architectures */
string bind_name = name;
size_t dsize = datatype_size(mem.data_type);
size_t size = mem.memory_size();
CUaddress_mode address_mode = CU_TR_ADDRESS_MODE_WRAP;
switch(extension) {
case EXTENSION_REPEAT:
address_mode = CU_TR_ADDRESS_MODE_WRAP;
break;
case EXTENSION_EXTEND:
address_mode = CU_TR_ADDRESS_MODE_CLAMP;
break;
case EXTENSION_CLIP:
address_mode = CU_TR_ADDRESS_MODE_BORDER;
break;
default:
assert(0);
break;
}
CUfilter_mode filter_mode;
if(interpolation == INTERPOLATION_CLOSEST) {
filter_mode = CU_TR_FILTER_MODE_POINT;
}
else {
filter_mode = CU_TR_FILTER_MODE_LINEAR;
}
CUarray_format_enum format;
switch(mem.data_type) {
case TYPE_UCHAR: format = CU_AD_FORMAT_UNSIGNED_INT8; break;
case TYPE_UINT: format = CU_AD_FORMAT_UNSIGNED_INT32; break;
case TYPE_INT: format = CU_AD_FORMAT_SIGNED_INT32; break;
case TYPE_FLOAT: format = CU_AD_FORMAT_FLOAT; break;
case TYPE_HALF: format = CU_AD_FORMAT_HALF; break;
default: assert(0); return;
}
/* General variables for Fermi */
CUtexref texref = NULL;
if(!has_bindless_textures) {
if(mem.data_depth > 1) {
/* Kernel uses different bind names for 2d and 3d float textures,
* so we have to adjust couple of things here.
*/
vector<string> tokens;
string_split(tokens, name, "_");
bind_name = string_printf("__tex_image_%s_3d_%s",
tokens[2].c_str(),
tokens[3].c_str());
}
cuda_push_context();
cuda_assert(cuModuleGetTexRef(&texref, cuModule, bind_name.c_str()));
cuda_pop_context();
if(!texref) {
return;
}
}
/* Data Storage */
if(interpolation == INTERPOLATION_NONE) {
if(has_bindless_textures) {
mem_alloc(NULL, mem, MEM_READ_ONLY);
mem_copy_to(mem);
cuda_push_context();
CUdeviceptr cumem;
size_t cubytes;
cuda_assert(cuModuleGetGlobal(&cumem, &cubytes, cuModule, bind_name.c_str()));
if(cubytes == 8) {
/* 64 bit device pointer */
uint64_t ptr = mem.device_pointer;
cuda_assert(cuMemcpyHtoD(cumem, (void*)&ptr, cubytes));
}
else {
/* 32 bit device pointer */
uint32_t ptr = (uint32_t)mem.device_pointer;
cuda_assert(cuMemcpyHtoD(cumem, (void*)&ptr, cubytes));
}
cuda_pop_context();
}
else {
mem_alloc(NULL, mem, MEM_READ_ONLY);
mem_copy_to(mem);
cuda_push_context();
cuda_assert(cuTexRefSetAddress(NULL, texref, cuda_device_ptr(mem.device_pointer), size));
cuda_assert(cuTexRefSetFilterMode(texref, CU_TR_FILTER_MODE_POINT));
cuda_assert(cuTexRefSetFlags(texref, CU_TRSF_READ_AS_INTEGER));
cuda_pop_context();
}
}
/* Texture Storage */
else {
CUarray handle = NULL;
cuda_push_context();
if(mem.data_depth > 1) {
CUDA_ARRAY3D_DESCRIPTOR desc;
desc.Width = mem.data_width;
desc.Height = mem.data_height;
desc.Depth = mem.data_depth;
desc.Format = format;
desc.NumChannels = mem.data_elements;
desc.Flags = 0;
cuda_assert(cuArray3DCreate(&handle, &desc));
}
else {
CUDA_ARRAY_DESCRIPTOR desc;
desc.Width = mem.data_width;
desc.Height = mem.data_height;
desc.Format = format;
desc.NumChannels = mem.data_elements;
cuda_assert(cuArrayCreate(&handle, &desc));
}
if(!handle) {
cuda_pop_context();
return;
}
/* Allocate 3D, 2D or 1D memory */
if(mem.data_depth > 1) {
CUDA_MEMCPY3D param;
memset(&param, 0, sizeof(param));
param.dstMemoryType = CU_MEMORYTYPE_ARRAY;
param.dstArray = handle;
param.srcMemoryType = CU_MEMORYTYPE_HOST;
param.srcHost = (void*)mem.data_pointer;
param.srcPitch = mem.data_width*dsize*mem.data_elements;
param.WidthInBytes = param.srcPitch;
param.Height = mem.data_height;
param.Depth = mem.data_depth;
cuda_assert(cuMemcpy3D(&param));
}
else if(mem.data_height > 1) {
CUDA_MEMCPY2D param;
memset(&param, 0, sizeof(param));
param.dstMemoryType = CU_MEMORYTYPE_ARRAY;
param.dstArray = handle;
param.srcMemoryType = CU_MEMORYTYPE_HOST;
param.srcHost = (void*)mem.data_pointer;
param.srcPitch = mem.data_width*dsize*mem.data_elements;
param.WidthInBytes = param.srcPitch;
param.Height = mem.data_height;
cuda_assert(cuMemcpy2D(&param));
}
else
cuda_assert(cuMemcpyHtoA(handle, 0, (void*)mem.data_pointer, size));
/* Fermi and Kepler */
mem.device_pointer = (device_ptr)handle;
mem.device_size = size;
stats.mem_alloc(size);
/* Bindless Textures - Kepler */
if(has_bindless_textures) {
int flat_slot = 0;
if(string_startswith(name, "__tex_image")) {
int pos = string(name).rfind("_");
flat_slot = atoi(name + pos + 1);
}
else {
assert(0);
}
CUDA_RESOURCE_DESC resDesc;
memset(&resDesc, 0, sizeof(resDesc));
resDesc.resType = CU_RESOURCE_TYPE_ARRAY;
resDesc.res.array.hArray = handle;
resDesc.flags = 0;
CUDA_TEXTURE_DESC texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode[0] = address_mode;
texDesc.addressMode[1] = address_mode;
texDesc.addressMode[2] = address_mode;
texDesc.filterMode = filter_mode;
texDesc.flags = CU_TRSF_NORMALIZED_COORDINATES;
CUtexObject tex = 0;
cuda_assert(cuTexObjectCreate(&tex, &resDesc, &texDesc, NULL));
/* Safety check */
if((uint)tex > UINT_MAX) {
assert(0);
}
/* Resize once */
if(flat_slot >= bindless_mapping.size()) {
/* Allocate some slots in advance, to reduce amount
* of re-allocations.
*/
bindless_mapping.resize(flat_slot + 128);
}
/* Set Mapping and tag that we need to (re-)upload to device */
bindless_mapping.get_data()[flat_slot] = (uint)tex;
tex_bindless_map[mem.device_pointer] = (uint)tex;
need_bindless_mapping = true;
}
/* Regular Textures - Fermi */
else {
cuda_assert(cuTexRefSetArray(texref, handle, CU_TRSA_OVERRIDE_FORMAT));
cuda_assert(cuTexRefSetFilterMode(texref, filter_mode));
cuda_assert(cuTexRefSetFlags(texref, CU_TRSF_NORMALIZED_COORDINATES));
}
cuda_pop_context();
}
/* Fermi, Data and Image Textures */
if(!has_bindless_textures) {
cuda_push_context();
cuda_assert(cuTexRefSetAddressMode(texref, 0, address_mode));
cuda_assert(cuTexRefSetAddressMode(texref, 1, address_mode));
if(mem.data_depth > 1) {
cuda_assert(cuTexRefSetAddressMode(texref, 2, address_mode));
}
cuda_assert(cuTexRefSetFormat(texref, format, mem.data_elements));
cuda_pop_context();
}
/* Fermi and Kepler */
tex_interp_map[mem.device_pointer] = (interpolation != INTERPOLATION_NONE);
}
void tex_free(device_memory& mem)
{
if(mem.device_pointer) {
if(tex_interp_map[mem.device_pointer]) {
cuda_push_context();
cuArrayDestroy((CUarray)mem.device_pointer);
cuda_pop_context();
/* Free CUtexObject (Bindless Textures) */
if(info.has_bindless_textures && tex_bindless_map[mem.device_pointer]) {
uint flat_slot = tex_bindless_map[mem.device_pointer];
cuTexObjectDestroy(flat_slot);
}
tex_interp_map.erase(tex_interp_map.find(mem.device_pointer));
mem.device_pointer = 0;
stats.mem_free(mem.device_size);
mem.device_size = 0;
}
else {
tex_interp_map.erase(tex_interp_map.find(mem.device_pointer));
mem_free(mem);
}
}
}
bool denoising_set_tiles(device_ptr *buffers, DenoisingTask *task)
{
mem_alloc("Denoising Tile Info", task->tiles_mem, MEM_READ_ONLY);
TilesInfo *tiles = (TilesInfo*) task->tiles_mem.data_pointer;
for(int i = 0; i < 9; i++) {
tiles->buffers[i] = buffers[i];
}
mem_copy_to(task->tiles_mem);
return !have_error();
}
#define CUDA_GET_BLOCKSIZE(func, w, h) \
int threads_per_block; \
cuda_assert(cuFuncGetAttribute(&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, func)); \
int threads = (int)sqrt((float)threads_per_block); \
int xblocks = ((w) + threads - 1)/threads; \
int yblocks = ((h) + threads - 1)/threads;
#define CUDA_LAUNCH_KERNEL(func, args) \
cuda_assert(cuLaunchKernel(func, \
xblocks, yblocks, 1, \
threads, threads, 1, \
0, 0, args, 0));
bool denoising_non_local_means(device_ptr image_ptr, device_ptr guide_ptr, device_ptr variance_ptr, device_ptr out_ptr,
DenoisingTask *task)
{
if(have_error())
return false;
cuda_push_context();
int4 rect = task->rect;
int w = rect.z-rect.x;
int h = rect.w-rect.y;
int r = task->nlm_state.r;
int f = task->nlm_state.f;
float a = task->nlm_state.a;
float k_2 = task->nlm_state.k_2;
CUdeviceptr difference = task->nlm_state.temporary_1_ptr;
CUdeviceptr blurDifference = task->nlm_state.temporary_2_ptr;
CUdeviceptr weightAccum = task->nlm_state.temporary_3_ptr;
cuda_assert(cuMemsetD8(weightAccum, 0, sizeof(float)*w*h));
cuda_assert(cuMemsetD8(out_ptr, 0, sizeof(float)*w*h));
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMUpdateOutput, cuNLMNormalize;
cuda_assert(cuModuleGetFunction(&cuNLMCalcDifference, cuFilterModule, "kernel_cuda_filter_nlm_calc_difference"));
cuda_assert(cuModuleGetFunction(&cuNLMBlur, cuFilterModule, "kernel_cuda_filter_nlm_blur"));
cuda_assert(cuModuleGetFunction(&cuNLMCalcWeight, cuFilterModule, "kernel_cuda_filter_nlm_calc_weight"));
cuda_assert(cuModuleGetFunction(&cuNLMUpdateOutput, cuFilterModule, "kernel_cuda_filter_nlm_update_output"));
cuda_assert(cuModuleGetFunction(&cuNLMNormalize, cuFilterModule, "kernel_cuda_filter_nlm_normalize"));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcDifference, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMBlur, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcWeight, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMUpdateOutput, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMNormalize, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuNLMCalcDifference, rect.z-rect.x, rect.w-rect.y);
int dx, dy;
int4 local_rect;
int channel_offset = 0;
void *calc_difference_args[] = {&dx, &dy, &guide_ptr, &variance_ptr, &difference, &local_rect, &w, &channel_offset, &a, &k_2};
void *blur_args[] = {&difference, &blurDifference, &local_rect, &w, &f};
void *calc_weight_args[] = {&blurDifference, &difference, &local_rect, &w, &f};
void *update_output_args[] = {&dx, &dy, &blurDifference, &image_ptr, &out_ptr, &weightAccum, &local_rect, &w, &f};
for(int i = 0; i < (2*r+1)*(2*r+1); i++) {
dy = i / (2*r+1) - r;
dx = i % (2*r+1) - r;
local_rect = make_int4(max(0, -dx), max(0, -dy), rect.z-rect.x - max(0, dx), rect.w-rect.y - max(0, dy));
CUDA_LAUNCH_KERNEL(cuNLMCalcDifference, calc_difference_args);
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL(cuNLMCalcWeight, calc_weight_args);
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL(cuNLMUpdateOutput, update_output_args);
}
local_rect = make_int4(0, 0, rect.z-rect.x, rect.w-rect.y);
void *normalize_args[] = {&out_ptr, &weightAccum, &local_rect, &w};
CUDA_LAUNCH_KERNEL(cuNLMNormalize, normalize_args);
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
return !have_error();
}
bool denoising_construct_transform(DenoisingTask *task)
{
if(have_error())
return false;
cuda_push_context();
CUfunction cuFilterConstructTransform;
cuda_assert(cuModuleGetFunction(&cuFilterConstructTransform, cuFilterModule, "kernel_cuda_filter_construct_transform"));
cuda_assert(cuFuncSetCacheConfig(cuFilterConstructTransform, CU_FUNC_CACHE_PREFER_SHARED));
CUDA_GET_BLOCKSIZE(cuFilterConstructTransform,
task->storage.w,
task->storage.h);
void *args[] = {&task->buffer.mem.device_pointer,
&task->storage.transform.device_pointer,
&task->storage.rank.device_pointer,
&task->filter_area,
&task->rect,
&task->radius,
&task->pca_threshold,
&task->buffer.pass_stride};
CUDA_LAUNCH_KERNEL(cuFilterConstructTransform, args);
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
return !have_error();
}
bool denoising_reconstruct(device_ptr color_ptr,
device_ptr color_variance_ptr,
device_ptr guide_ptr,
device_ptr guide_variance_ptr,
device_ptr output_ptr,
DenoisingTask *task)
{
if(have_error())
return false;
mem_zero(task->storage.XtWX);
mem_zero(task->storage.XtWY);
cuda_push_context();
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMConstructGramian, cuFinalize;
cuda_assert(cuModuleGetFunction(&cuNLMCalcDifference, cuFilterModule, "kernel_cuda_filter_nlm_calc_difference"));
cuda_assert(cuModuleGetFunction(&cuNLMBlur, cuFilterModule, "kernel_cuda_filter_nlm_blur"));
cuda_assert(cuModuleGetFunction(&cuNLMCalcWeight, cuFilterModule, "kernel_cuda_filter_nlm_calc_weight"));
cuda_assert(cuModuleGetFunction(&cuNLMConstructGramian, cuFilterModule, "kernel_cuda_filter_nlm_construct_gramian"));
cuda_assert(cuModuleGetFunction(&cuFinalize, cuFilterModule, "kernel_cuda_filter_finalize"));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcDifference, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMBlur, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcWeight, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMConstructGramian, CU_FUNC_CACHE_PREFER_SHARED));
cuda_assert(cuFuncSetCacheConfig(cuFinalize, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuNLMCalcDifference,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
CUdeviceptr difference = task->reconstruction_state.temporary_1_ptr;
CUdeviceptr blurDifference = task->reconstruction_state.temporary_2_ptr;
int r = task->radius;
int f = 4;
float a = 1.0f;
for(int i = 0; i < (2*r+1)*(2*r+1); i++) {
int dy = i / (2*r+1) - r;
int dx = i % (2*r+1) - r;
int local_rect[4] = {max(0, -dx), max(0, -dy),
task->reconstruction_state.source_w - max(0, dx),
task->reconstruction_state.source_h - max(0, dy)};
void *calc_difference_args[] = {&dx, &dy,
&guide_ptr,
&guide_variance_ptr,
&difference,
&local_rect,
&task->buffer.w,
&task->buffer.pass_stride,
&a,
&task->nlm_k_2};
CUDA_LAUNCH_KERNEL(cuNLMCalcDifference, calc_difference_args);
void *blur_args[] = {&difference,
&blurDifference,
&local_rect,
&task->buffer.w,
&f};
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
void *calc_weight_args[] = {&blurDifference,
&difference,
&local_rect,
&task->buffer.w,
&f};
CUDA_LAUNCH_KERNEL(cuNLMCalcWeight, calc_weight_args);
/* Reuse previous arguments. */
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
void *construct_gramian_args[] = {&dx, &dy,
&blurDifference,
&task->buffer.mem.device_pointer,
&color_ptr,
&color_variance_ptr,
&task->storage.transform.device_pointer,
&task->storage.rank.device_pointer,
&task->storage.XtWX.device_pointer,
&task->storage.XtWY.device_pointer,
&local_rect,
&task->reconstruction_state.filter_rect,
&task->buffer.w,
&task->buffer.h,
&f,
&task->buffer.pass_stride};
CUDA_LAUNCH_KERNEL(cuNLMConstructGramian, construct_gramian_args);
}
void *finalize_args[] = {&task->buffer.w,
&task->buffer.h,
&output_ptr,
&task->storage.rank.device_pointer,
&task->storage.XtWX.device_pointer,
&task->storage.XtWY.device_pointer,
&task->filter_area,
&task->reconstruction_state.buffer_params.x,
&task->render_buffer.samples};
CUDA_LAUNCH_KERNEL(cuFinalize, finalize_args);
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
return !have_error();
}
bool denoising_combine_halves(device_ptr a_ptr, device_ptr b_ptr,
device_ptr mean_ptr, device_ptr variance_ptr,
int r, int4 rect, DenoisingTask *task)
{
(void) task;
if(have_error())
return false;
cuda_push_context();
CUfunction cuFilterCombineHalves;
cuda_assert(cuModuleGetFunction(&cuFilterCombineHalves, cuFilterModule, "kernel_cuda_filter_combine_halves"));
cuda_assert(cuFuncSetCacheConfig(cuFilterCombineHalves, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuFilterCombineHalves,
task->rect.z-task->rect.x,
task->rect.w-task->rect.y);
void *args[] = {&mean_ptr,
&variance_ptr,
&a_ptr,
&b_ptr,
&rect,
&r};
CUDA_LAUNCH_KERNEL(cuFilterCombineHalves, args);
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
return !have_error();
}
bool denoising_divide_shadow(device_ptr a_ptr, device_ptr b_ptr,
device_ptr sample_variance_ptr, device_ptr sv_variance_ptr,
device_ptr buffer_variance_ptr, DenoisingTask *task)
{
(void) task;
if(have_error())
return false;
cuda_push_context();
CUfunction cuFilterDivideShadow;
cuda_assert(cuModuleGetFunction(&cuFilterDivideShadow, cuFilterModule, "kernel_cuda_filter_divide_shadow"));
cuda_assert(cuFuncSetCacheConfig(cuFilterDivideShadow, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuFilterDivideShadow,
task->rect.z-task->rect.x,
task->rect.w-task->rect.y);
bool use_split_variance = use_split_kernel();
void *args[] = {&task->render_buffer.samples,
&task->tiles_mem.device_pointer,
&a_ptr,
&b_ptr,
&sample_variance_ptr,
&sv_variance_ptr,
&buffer_variance_ptr,
&task->rect,
&task->render_buffer.pass_stride,
&task->render_buffer.denoising_data_offset,
&use_split_variance};
CUDA_LAUNCH_KERNEL(cuFilterDivideShadow, args);
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
return !have_error();
}
bool denoising_get_feature(int mean_offset,
int variance_offset,
device_ptr mean_ptr,
device_ptr variance_ptr,
DenoisingTask *task)
{
if(have_error())
return false;
cuda_push_context();
CUfunction cuFilterGetFeature;
cuda_assert(cuModuleGetFunction(&cuFilterGetFeature, cuFilterModule, "kernel_cuda_filter_get_feature"));
cuda_assert(cuFuncSetCacheConfig(cuFilterGetFeature, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuFilterGetFeature,
task->rect.z-task->rect.x,
task->rect.w-task->rect.y);
bool use_split_variance = use_split_kernel();
void *args[] = {&task->render_buffer.samples,
&task->tiles_mem.device_pointer,
&mean_offset,
&variance_offset,
&mean_ptr,
&variance_ptr,
&task->rect,
&task->render_buffer.pass_stride,
&task->render_buffer.denoising_data_offset,
&use_split_variance};
CUDA_LAUNCH_KERNEL(cuFilterGetFeature, args);
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
return !have_error();
}
void denoise(RenderTile &rtile, const DeviceTask &task)
{
DenoisingTask denoising(this);
denoising.functions.construct_transform = function_bind(&CUDADevice::denoising_construct_transform, this, &denoising);
denoising.functions.reconstruct = function_bind(&CUDADevice::denoising_reconstruct, this, _1, _2, _3, _4, _5, &denoising);
denoising.functions.divide_shadow = function_bind(&CUDADevice::denoising_divide_shadow, this, _1, _2, _3, _4, _5, &denoising);
denoising.functions.non_local_means = function_bind(&CUDADevice::denoising_non_local_means, this, _1, _2, _3, _4, &denoising);
denoising.functions.combine_halves = function_bind(&CUDADevice::denoising_combine_halves, this, _1, _2, _3, _4, _5, _6, &denoising);
denoising.functions.get_feature = function_bind(&CUDADevice::denoising_get_feature, this, _1, _2, _3, _4, &denoising);
denoising.functions.set_tiles = function_bind(&CUDADevice::denoising_set_tiles, this, _1, &denoising);
denoising.filter_area = make_int4(rtile.x, rtile.y, rtile.w, rtile.h);
denoising.render_buffer.samples = rtile.sample;
RenderTile rtiles[9];
rtiles[4] = rtile;
task.map_neighbor_tiles(rtiles, this);
denoising.tiles_from_rendertiles(rtiles);
denoising.init_from_devicetask(task);
denoising.run_denoising();
task.unmap_neighbor_tiles(rtiles, this);
}
void path_trace(RenderTile& rtile, int sample, bool branched)
{
if(have_error())
return;
cuda_push_context();
CUfunction cuPathTrace;
CUdeviceptr d_buffer = cuda_device_ptr(rtile.buffer);
CUdeviceptr d_rng_state = cuda_device_ptr(rtile.rng_state);
/* get kernel function */
if(branched) {
cuda_assert(cuModuleGetFunction(&cuPathTrace, cuModule, "kernel_cuda_branched_path_trace"));
}
else {
cuda_assert(cuModuleGetFunction(&cuPathTrace, cuModule, "kernel_cuda_path_trace"));
}
if(have_error())
return;
/* pass in parameters */
void *args[] = {&d_buffer,
&d_rng_state,
&sample,
&rtile.x,
&rtile.y,
&rtile.w,
&rtile.h,
&rtile.offset,
&rtile.stride};
/* launch kernel */
int threads_per_block;
cuda_assert(cuFuncGetAttribute(&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, cuPathTrace));
/*int num_registers;
cuda_assert(cuFuncGetAttribute(&num_registers, CU_FUNC_ATTRIBUTE_NUM_REGS, cuPathTrace));
printf("threads_per_block %d\n", threads_per_block);
printf("num_registers %d\n", num_registers);*/
int xthreads = (int)sqrt(threads_per_block);
int ythreads = (int)sqrt(threads_per_block);
int xblocks = (rtile.w + xthreads - 1)/xthreads;
int yblocks = (rtile.h + ythreads - 1)/ythreads;
cuda_assert(cuFuncSetCacheConfig(cuPathTrace, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuLaunchKernel(cuPathTrace,
xblocks , yblocks, 1, /* blocks */
xthreads, ythreads, 1, /* threads */
0, 0, args, 0));
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
}
void film_convert(DeviceTask& task, device_ptr buffer, device_ptr rgba_byte, device_ptr rgba_half)
{
if(have_error())
return;
cuda_push_context();
CUfunction cuFilmConvert;
CUdeviceptr d_rgba = map_pixels((rgba_byte)? rgba_byte: rgba_half);
CUdeviceptr d_buffer = cuda_device_ptr(buffer);
/* get kernel function */
if(rgba_half) {
cuda_assert(cuModuleGetFunction(&cuFilmConvert, cuModule, "kernel_cuda_convert_to_half_float"));
}
else {
cuda_assert(cuModuleGetFunction(&cuFilmConvert, cuModule, "kernel_cuda_convert_to_byte"));
}
float sample_scale = 1.0f/(task.sample + 1);
/* pass in parameters */
void *args[] = {&d_rgba,
&d_buffer,
&sample_scale,
&task.x,
&task.y,
&task.w,
&task.h,
&task.offset,
&task.stride};
/* launch kernel */
int threads_per_block;
cuda_assert(cuFuncGetAttribute(&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, cuFilmConvert));
int xthreads = (int)sqrt(threads_per_block);
int ythreads = (int)sqrt(threads_per_block);
int xblocks = (task.w + xthreads - 1)/xthreads;
int yblocks = (task.h + ythreads - 1)/ythreads;
cuda_assert(cuFuncSetCacheConfig(cuFilmConvert, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuLaunchKernel(cuFilmConvert,
xblocks , yblocks, 1, /* blocks */
xthreads, ythreads, 1, /* threads */
0, 0, args, 0));
unmap_pixels((rgba_byte)? rgba_byte: rgba_half);
cuda_pop_context();
}
void shader(DeviceTask& task)
{
if(have_error())
return;
cuda_push_context();
CUfunction cuShader;
CUdeviceptr d_input = cuda_device_ptr(task.shader_input);
CUdeviceptr d_output = cuda_device_ptr(task.shader_output);
CUdeviceptr d_output_luma = cuda_device_ptr(task.shader_output_luma);
/* get kernel function */
if(task.shader_eval_type >= SHADER_EVAL_BAKE) {
cuda_assert(cuModuleGetFunction(&cuShader, cuModule, "kernel_cuda_bake"));
}
else {
cuda_assert(cuModuleGetFunction(&cuShader, cuModule, "kernel_cuda_shader"));
}
/* do tasks in smaller chunks, so we can cancel it */
const int shader_chunk_size = 65536;
const int start = task.shader_x;
const int end = task.shader_x + task.shader_w;
int offset = task.offset;
bool canceled = false;
for(int sample = 0; sample < task.num_samples && !canceled; sample++) {
for(int shader_x = start; shader_x < end; shader_x += shader_chunk_size) {
int shader_w = min(shader_chunk_size, end - shader_x);
/* pass in parameters */
void *args[8];
int arg = 0;
args[arg++] = &d_input;
args[arg++] = &d_output;
if(task.shader_eval_type < SHADER_EVAL_BAKE) {
args[arg++] = &d_output_luma;
}
args[arg++] = &task.shader_eval_type;
if(task.shader_eval_type >= SHADER_EVAL_BAKE) {
args[arg++] = &task.shader_filter;
}
args[arg++] = &shader_x;
args[arg++] = &shader_w;
args[arg++] = &offset;
args[arg++] = &sample;
/* launch kernel */
int threads_per_block;
cuda_assert(cuFuncGetAttribute(&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, cuShader));
int xblocks = (shader_w + threads_per_block - 1)/threads_per_block;
cuda_assert(cuFuncSetCacheConfig(cuShader, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuLaunchKernel(cuShader,
xblocks , 1, 1, /* blocks */
threads_per_block, 1, 1, /* threads */
0, 0, args, 0));
cuda_assert(cuCtxSynchronize());
if(task.get_cancel()) {
canceled = true;
break;
}
}
task.update_progress(NULL);
}
cuda_pop_context();
}
CUdeviceptr map_pixels(device_ptr mem)
{
if(!background) {
PixelMem pmem = pixel_mem_map[mem];
CUdeviceptr buffer;
size_t bytes;
cuda_assert(cuGraphicsMapResources(1, &pmem.cuPBOresource, 0));
cuda_assert(cuGraphicsResourceGetMappedPointer(&buffer, &bytes, pmem.cuPBOresource));
return buffer;
}
return cuda_device_ptr(mem);
}
void unmap_pixels(device_ptr mem)
{
if(!background) {
PixelMem pmem = pixel_mem_map[mem];
cuda_assert(cuGraphicsUnmapResources(1, &pmem.cuPBOresource, 0));
}
}
void pixels_alloc(device_memory& mem)
{
if(!background) {
PixelMem pmem;
pmem.w = mem.data_width;
pmem.h = mem.data_height;
cuda_push_context();
glGenBuffers(1, &pmem.cuPBO);
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, pmem.cuPBO);
if(mem.data_type == TYPE_HALF)
glBufferData(GL_PIXEL_UNPACK_BUFFER, pmem.w*pmem.h*sizeof(GLhalf)*4, NULL, GL_DYNAMIC_DRAW);
else
glBufferData(GL_PIXEL_UNPACK_BUFFER, pmem.w*pmem.h*sizeof(uint8_t)*4, NULL, GL_DYNAMIC_DRAW);
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
glGenTextures(1, &pmem.cuTexId);
glBindTexture(GL_TEXTURE_2D, pmem.cuTexId);
if(mem.data_type == TYPE_HALF)
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA16F_ARB, pmem.w, pmem.h, 0, GL_RGBA, GL_HALF_FLOAT, NULL);
else
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA8, pmem.w, pmem.h, 0, GL_RGBA, GL_UNSIGNED_BYTE, NULL);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST);
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST);
glBindTexture(GL_TEXTURE_2D, 0);
CUresult result = cuGraphicsGLRegisterBuffer(&pmem.cuPBOresource, pmem.cuPBO, CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE);
if(result == CUDA_SUCCESS) {
cuda_pop_context();
mem.device_pointer = pmem.cuTexId;
pixel_mem_map[mem.device_pointer] = pmem;
mem.device_size = mem.memory_size();
stats.mem_alloc(mem.device_size);
return;
}
else {
/* failed to register buffer, fallback to no interop */
glDeleteBuffers(1, &pmem.cuPBO);
glDeleteTextures(1, &pmem.cuTexId);
cuda_pop_context();
background = true;
}
}
Device::pixels_alloc(mem);
}
void pixels_copy_from(device_memory& mem, int y, int w, int h)
{
if(!background) {
PixelMem pmem = pixel_mem_map[mem.device_pointer];
cuda_push_context();
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, pmem.cuPBO);
uchar *pixels = (uchar*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_READ_ONLY);
size_t offset = sizeof(uchar)*4*y*w;
memcpy((uchar*)mem.data_pointer + offset, pixels + offset, sizeof(uchar)*4*w*h);
glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
cuda_pop_context();
return;
}
Device::pixels_copy_from(mem, y, w, h);
}
void pixels_free(device_memory& mem)
{
if(mem.device_pointer) {
if(!background) {
PixelMem pmem = pixel_mem_map[mem.device_pointer];
cuda_push_context();
cuda_assert(cuGraphicsUnregisterResource(pmem.cuPBOresource));
glDeleteBuffers(1, &pmem.cuPBO);
glDeleteTextures(1, &pmem.cuTexId);
cuda_pop_context();
pixel_mem_map.erase(pixel_mem_map.find(mem.device_pointer));
mem.device_pointer = 0;
stats.mem_free(mem.device_size);
mem.device_size = 0;
return;
}
Device::pixels_free(mem);
}
}
void draw_pixels(device_memory& mem, int y, int w, int h, int dx, int dy, int width, int height, bool transparent,
const DeviceDrawParams &draw_params)
{
if(!background) {
PixelMem pmem = pixel_mem_map[mem.device_pointer];
float *vpointer;
cuda_push_context();
/* for multi devices, this assumes the inefficient method that we allocate
* all pixels on the device even though we only render to a subset */
size_t offset = 4*y*w;
if(mem.data_type == TYPE_HALF)
offset *= sizeof(GLhalf);
else
offset *= sizeof(uint8_t);
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, pmem.cuPBO);
glBindTexture(GL_TEXTURE_2D, pmem.cuTexId);
if(mem.data_type == TYPE_HALF)
glTexSubImage2D(GL_TEXTURE_2D, 0, 0, 0, w, h, GL_RGBA, GL_HALF_FLOAT, (void*)offset);
else
glTexSubImage2D(GL_TEXTURE_2D, 0, 0, 0, w, h, GL_RGBA, GL_UNSIGNED_BYTE, (void*)offset);
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
glEnable(GL_TEXTURE_2D);
if(transparent) {
glEnable(GL_BLEND);
glBlendFunc(GL_ONE, GL_ONE_MINUS_SRC_ALPHA);
}
glColor3f(1.0f, 1.0f, 1.0f);
if(draw_params.bind_display_space_shader_cb) {
draw_params.bind_display_space_shader_cb();
}
if(!vertex_buffer)
glGenBuffers(1, &vertex_buffer);
glBindBuffer(GL_ARRAY_BUFFER, vertex_buffer);
/* invalidate old contents - avoids stalling if buffer is still waiting in queue to be rendered */
glBufferData(GL_ARRAY_BUFFER, 16 * sizeof(float), NULL, GL_STREAM_DRAW);
vpointer = (float *)glMapBuffer(GL_ARRAY_BUFFER, GL_WRITE_ONLY);
if(vpointer) {
/* texture coordinate - vertex pair */
vpointer[0] = 0.0f;
vpointer[1] = 0.0f;
vpointer[2] = dx;
vpointer[3] = dy;
vpointer[4] = (float)w/(float)pmem.w;
vpointer[5] = 0.0f;
vpointer[6] = (float)width + dx;
vpointer[7] = dy;
vpointer[8] = (float)w/(float)pmem.w;
vpointer[9] = (float)h/(float)pmem.h;
vpointer[10] = (float)width + dx;
vpointer[11] = (float)height + dy;
vpointer[12] = 0.0f;
vpointer[13] = (float)h/(float)pmem.h;
vpointer[14] = dx;
vpointer[15] = (float)height + dy;
glUnmapBuffer(GL_ARRAY_BUFFER);
}
glTexCoordPointer(2, GL_FLOAT, 4 * sizeof(float), 0);
glVertexPointer(2, GL_FLOAT, 4 * sizeof(float), (char *)NULL + 2 * sizeof(float));
glEnableClientState(GL_VERTEX_ARRAY);
glEnableClientState(GL_TEXTURE_COORD_ARRAY);
glDrawArrays(GL_TRIANGLE_FAN, 0, 4);
glDisableClientState(GL_TEXTURE_COORD_ARRAY);
glDisableClientState(GL_VERTEX_ARRAY);
glBindBuffer(GL_ARRAY_BUFFER, 0);
if(draw_params.unbind_display_space_shader_cb) {
draw_params.unbind_display_space_shader_cb();
}
if(transparent)
glDisable(GL_BLEND);
glBindTexture(GL_TEXTURE_2D, 0);
glDisable(GL_TEXTURE_2D);
cuda_pop_context();
return;
}
Device::draw_pixels(mem, y, w, h, dx, dy, width, height, transparent, draw_params);
}
void thread_run(DeviceTask *task)
{
if(task->type == DeviceTask::RENDER) {
RenderTile tile;
bool branched = task->integrator_branched;
/* Upload Bindless Mapping */
load_bindless_mapping();
DeviceRequestedFeatures requested_features;
if(use_split_kernel()) {
if(!use_adaptive_compilation()) {
requested_features.max_closure = 64;
}
if(split_kernel == NULL) {
split_kernel = new CUDASplitKernel(this);
split_kernel->load_kernels(requested_features);
}
}
/* keep rendering tiles until done */
while(task->acquire_tile(this, tile)) {
if(tile.task == RenderTile::PATH_TRACE) {
if(use_split_kernel()) {
device_memory void_buffer;
split_kernel->path_trace(task, tile, void_buffer, void_buffer);
}
else {
int start_sample = tile.start_sample;
int end_sample = tile.start_sample + tile.num_samples;
for(int sample = start_sample; sample < end_sample; sample++) {
if(task->get_cancel()) {
if(task->need_finish_queue == false)
break;
}
path_trace(tile, sample, branched);
tile.sample = sample + 1;
task->update_progress(&tile, tile.w*tile.h);
}
}
}
else if(tile.task == RenderTile::DENOISE) {
tile.sample = tile.start_sample + tile.num_samples;
denoise(tile, *task);
task->update_progress(&tile, tile.w*tile.h);
}
task->release_tile(tile);
if(task->get_cancel()) {
if(task->need_finish_queue == false)
break;
}
}
}
else if(task->type == DeviceTask::SHADER) {
/* Upload Bindless Mapping */
load_bindless_mapping();
shader(*task);
cuda_push_context();
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
}
}
class CUDADeviceTask : public DeviceTask {
public:
CUDADeviceTask(CUDADevice *device, DeviceTask& task)
: DeviceTask(task)
{
run = function_bind(&CUDADevice::thread_run, device, this);
}
};
int get_split_task_count(DeviceTask& /*task*/)
{
return 1;
}
void task_add(DeviceTask& task)
{
if(task.type == DeviceTask::FILM_CONVERT) {
/* must be done in main thread due to opengl access */
film_convert(task, task.buffer, task.rgba_byte, task.rgba_half);
cuda_push_context();
cuda_assert(cuCtxSynchronize());
cuda_pop_context();
}
else {
task_pool.push(new CUDADeviceTask(this, task));
}
}
void task_wait()
{
task_pool.wait();
}
void task_cancel()
{
task_pool.cancel();
}
friend class CUDASplitKernelFunction;
friend class CUDASplitKernel;
};
/* redefine the cuda_assert macro so it can be used outside of the CUDADevice class
* now that the definition of that class is complete
*/
#undef cuda_assert
#define cuda_assert(stmt) \
{ \
CUresult result = stmt; \
\
if(result != CUDA_SUCCESS) { \
string message = string_printf("CUDA error: %s in %s", cuewErrorString(result), #stmt); \
if(device->error_msg == "") \
device->error_msg = message; \
fprintf(stderr, "%s\n", message.c_str()); \
/*cuda_abort();*/ \
device->cuda_error_documentation(); \
} \
} (void)0
/* split kernel */
class CUDASplitKernelFunction : public SplitKernelFunction{
CUDADevice* device;
CUfunction func;
public:
CUDASplitKernelFunction(CUDADevice *device, CUfunction func) : device(device), func(func) {}
/* enqueue the kernel, returns false if there is an error */
bool enqueue(const KernelDimensions &dim, device_memory &/*kg*/, device_memory &/*data*/)
{
return enqueue(dim, NULL);
}
/* enqueue the kernel, returns false if there is an error */
bool enqueue(const KernelDimensions &dim, void *args[])
{
device->cuda_push_context();
if(device->have_error())
return false;
/* we ignore dim.local_size for now, as this is faster */
int threads_per_block;
cuda_assert(cuFuncGetAttribute(&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, func));
int xthreads = (int)sqrt(threads_per_block);
int ythreads = (int)sqrt(threads_per_block);
int xblocks = (dim.global_size[0] + xthreads - 1)/xthreads;
int yblocks = (dim.global_size[1] + ythreads - 1)/ythreads;
cuda_assert(cuFuncSetCacheConfig(func, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuLaunchKernel(func,
xblocks , yblocks, 1, /* blocks */
xthreads, ythreads, 1, /* threads */
0, 0, args, 0));
device->cuda_pop_context();
return !device->have_error();
}
};
CUDASplitKernel::CUDASplitKernel(CUDADevice *device) : DeviceSplitKernel(device), device(device)
{
}
uint64_t CUDASplitKernel::state_buffer_size(device_memory& /*kg*/, device_memory& /*data*/, size_t num_threads)
{
device_vector<uint64_t> size_buffer;
size_buffer.resize(1);
device->mem_alloc(NULL, size_buffer, MEM_READ_WRITE);
device->cuda_push_context();
uint threads = num_threads;
CUdeviceptr d_size = device->cuda_device_ptr(size_buffer.device_pointer);
struct args_t {
uint* num_threads;
CUdeviceptr* size;
};
args_t args = {
&threads,
&d_size
};
CUfunction state_buffer_size;
cuda_assert(cuModuleGetFunction(&state_buffer_size, device->cuModule, "kernel_cuda_state_buffer_size"));
cuda_assert(cuLaunchKernel(state_buffer_size,
1, 1, 1,
1, 1, 1,
0, 0, (void**)&args, 0));
device->cuda_pop_context();
device->mem_copy_from(size_buffer, 0, 1, 1, sizeof(uint64_t));
device->mem_free(size_buffer);
return *size_buffer.get_data();
}
bool CUDASplitKernel::enqueue_split_kernel_data_init(const KernelDimensions& dim,
RenderTile& rtile,
int num_global_elements,
device_memory& /*kernel_globals*/,
device_memory& /*kernel_data*/,
device_memory& split_data,
device_memory& ray_state,
device_memory& queue_index,
device_memory& use_queues_flag,
device_memory& work_pool_wgs)
{
device->cuda_push_context();
CUdeviceptr d_split_data = device->cuda_device_ptr(split_data.device_pointer);
CUdeviceptr d_ray_state = device->cuda_device_ptr(ray_state.device_pointer);
CUdeviceptr d_queue_index = device->cuda_device_ptr(queue_index.device_pointer);
CUdeviceptr d_use_queues_flag = device->cuda_device_ptr(use_queues_flag.device_pointer);
CUdeviceptr d_work_pool_wgs = device->cuda_device_ptr(work_pool_wgs.device_pointer);
CUdeviceptr d_rng_state = device->cuda_device_ptr(rtile.rng_state);
CUdeviceptr d_buffer = device->cuda_device_ptr(rtile.buffer);
int end_sample = rtile.start_sample + rtile.num_samples;
int queue_size = dim.global_size[0] * dim.global_size[1];
struct args_t {
CUdeviceptr* split_data_buffer;
int* num_elements;
CUdeviceptr* ray_state;
CUdeviceptr* rng_state;
int* start_sample;
int* end_sample;
int* sx;
int* sy;
int* sw;
int* sh;
int* offset;
int* stride;
CUdeviceptr* queue_index;
int* queuesize;
CUdeviceptr* use_queues_flag;
CUdeviceptr* work_pool_wgs;
int* num_samples;
CUdeviceptr* buffer;
};
args_t args = {
&d_split_data,
&num_global_elements,
&d_ray_state,
&d_rng_state,
&rtile.start_sample,
&end_sample,
&rtile.x,
&rtile.y,
&rtile.w,
&rtile.h,
&rtile.offset,
&rtile.stride,
&d_queue_index,
&queue_size,
&d_use_queues_flag,
&d_work_pool_wgs,
&rtile.num_samples,
&d_buffer
};
CUfunction data_init;
cuda_assert(cuModuleGetFunction(&data_init, device->cuModule, "kernel_cuda_path_trace_data_init"));
if(device->have_error()) {
return false;
}
CUDASplitKernelFunction(device, data_init).enqueue(dim, (void**)&args);
device->cuda_pop_context();
return !device->have_error();
}
SplitKernelFunction* CUDASplitKernel::get_split_kernel_function(string kernel_name, const DeviceRequestedFeatures&)
{
CUfunction func;
device->cuda_push_context();
cuda_assert(cuModuleGetFunction(&func, device->cuModule, (string("kernel_cuda_") + kernel_name).data()));
if(device->have_error()) {
device->cuda_error_message(string_printf("kernel \"kernel_cuda_%s\" not found in module", kernel_name.data()));
return NULL;
}
device->cuda_pop_context();
return new CUDASplitKernelFunction(device, func);
}
int2 CUDASplitKernel::split_kernel_local_size()
{
return make_int2(32, 1);
}
int2 CUDASplitKernel::split_kernel_global_size(device_memory& kg, device_memory& data, DeviceTask * /*task*/)
{
size_t free;
size_t total;
device->cuda_push_context();
cuda_assert(cuMemGetInfo(&free, &total));
device->cuda_pop_context();
VLOG(1) << "Maximum device allocation size: "
<< string_human_readable_number(free) << " bytes. ("
<< string_human_readable_size(free) << ").";
size_t num_elements = max_elements_for_max_buffer_size(kg, data, free / 2);
size_t side = round_down((int)sqrt(num_elements), 32);
int2 global_size = make_int2(side, round_down(num_elements / side, 16));
VLOG(1) << "Global size: " << global_size << ".";
return global_size;
}
bool device_cuda_init(void)
{
#ifdef WITH_CUDA_DYNLOAD
static bool initialized = false;
static bool result = false;
if(initialized)
return result;
initialized = true;
int cuew_result = cuewInit();
if(cuew_result == CUEW_SUCCESS) {
VLOG(1) << "CUEW initialization succeeded";
if(CUDADevice::have_precompiled_kernels()) {
VLOG(1) << "Found precompiled kernels";
result = true;
}
#ifndef _WIN32
else if(cuewCompilerPath() != NULL) {
VLOG(1) << "Found CUDA compiler " << cuewCompilerPath();
result = true;
}
else {
VLOG(1) << "Neither precompiled kernels nor CUDA compiler wad found,"
<< " unable to use CUDA";
}
#endif
}
else {
VLOG(1) << "CUEW initialization failed: "
<< ((cuew_result == CUEW_ERROR_ATEXIT_FAILED)
? "Error setting up atexit() handler"
: "Error opening the library");
}
return result;
#else /* WITH_CUDA_DYNLOAD */
return true;
#endif /* WITH_CUDA_DYNLOAD */
}
Device *device_cuda_create(DeviceInfo& info, Stats &stats, bool background)
{
return new CUDADevice(info, stats, background);
}
void device_cuda_info(vector<DeviceInfo>& devices)
{
CUresult result;
int count = 0;
result = cuInit(0);
if(result != CUDA_SUCCESS) {
if(result != CUDA_ERROR_NO_DEVICE)
fprintf(stderr, "CUDA cuInit: %s\n", cuewErrorString(result));
return;
}
result = cuDeviceGetCount(&count);
if(result != CUDA_SUCCESS) {
fprintf(stderr, "CUDA cuDeviceGetCount: %s\n", cuewErrorString(result));
return;
}
vector<DeviceInfo> display_devices;
for(int num = 0; num < count; num++) {
char name[256];
int attr;
if(cuDeviceGetName(name, 256, num) != CUDA_SUCCESS)
continue;
int major;
cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, num);
if(major < 2) {
continue;
}
DeviceInfo info;
info.type = DEVICE_CUDA;
info.description = string(name);
info.num = num;
info.advanced_shading = (major >= 2);
info.has_bindless_textures = (major >= 3);
info.pack_images = false;
int pci_location[3] = {0, 0, 0};
cuDeviceGetAttribute(&pci_location[0], CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID, num);
cuDeviceGetAttribute(&pci_location[1], CU_DEVICE_ATTRIBUTE_PCI_BUS_ID, num);
cuDeviceGetAttribute(&pci_location[2], CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID, num);
info.id = string_printf("CUDA_%s_%04x:%02x:%02x",
name,
(unsigned int)pci_location[0],
(unsigned int)pci_location[1],
(unsigned int)pci_location[2]);
/* if device has a kernel timeout, assume it is used for display */
if(cuDeviceGetAttribute(&attr, CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT, num) == CUDA_SUCCESS && attr == 1) {
info.description += " (Display)";
info.display_device = true;
display_devices.push_back(info);
}
else
devices.push_back(info);
}
if(!display_devices.empty())
devices.insert(devices.end(), display_devices.begin(), display_devices.end());
}
string device_cuda_capabilities(void)
{
CUresult result = cuInit(0);
if(result != CUDA_SUCCESS) {
if(result != CUDA_ERROR_NO_DEVICE) {
return string("Error initializing CUDA: ") + cuewErrorString(result);
}
return "No CUDA device found\n";
}
int count;
result = cuDeviceGetCount(&count);
if(result != CUDA_SUCCESS) {
return string("Error getting devices: ") + cuewErrorString(result);
}
string capabilities = "";
for(int num = 0; num < count; num++) {
char name[256];
if(cuDeviceGetName(name, 256, num) != CUDA_SUCCESS) {
continue;
}
capabilities += string("\t") + name + "\n";
int value;
#define GET_ATTR(attr) \
{ \
if(cuDeviceGetAttribute(&value, \
CU_DEVICE_ATTRIBUTE_##attr, \
num) == CUDA_SUCCESS) \
{ \
capabilities += string_printf("\t\tCU_DEVICE_ATTRIBUTE_" #attr "\t\t\t%d\n", \
value); \
} \
} (void)0
/* TODO(sergey): Strip all attributes which are not useful for us
* or does not depend on the driver.
*/
GET_ATTR(MAX_THREADS_PER_BLOCK);
GET_ATTR(MAX_BLOCK_DIM_X);
GET_ATTR(MAX_BLOCK_DIM_Y);
GET_ATTR(MAX_BLOCK_DIM_Z);
GET_ATTR(MAX_GRID_DIM_X);
GET_ATTR(MAX_GRID_DIM_Y);
GET_ATTR(MAX_GRID_DIM_Z);
GET_ATTR(MAX_SHARED_MEMORY_PER_BLOCK);
GET_ATTR(SHARED_MEMORY_PER_BLOCK);
GET_ATTR(TOTAL_CONSTANT_MEMORY);
GET_ATTR(WARP_SIZE);
GET_ATTR(MAX_PITCH);
GET_ATTR(MAX_REGISTERS_PER_BLOCK);
GET_ATTR(REGISTERS_PER_BLOCK);
GET_ATTR(CLOCK_RATE);
GET_ATTR(TEXTURE_ALIGNMENT);
GET_ATTR(GPU_OVERLAP);
GET_ATTR(MULTIPROCESSOR_COUNT);
GET_ATTR(KERNEL_EXEC_TIMEOUT);
GET_ATTR(INTEGRATED);
GET_ATTR(CAN_MAP_HOST_MEMORY);
GET_ATTR(COMPUTE_MODE);
GET_ATTR(MAXIMUM_TEXTURE1D_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_HEIGHT);
GET_ATTR(MAXIMUM_TEXTURE3D_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE3D_HEIGHT);
GET_ATTR(MAXIMUM_TEXTURE3D_DEPTH);
GET_ATTR(MAXIMUM_TEXTURE2D_LAYERED_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_LAYERED_HEIGHT);
GET_ATTR(MAXIMUM_TEXTURE2D_LAYERED_LAYERS);
GET_ATTR(MAXIMUM_TEXTURE2D_ARRAY_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_ARRAY_HEIGHT);
GET_ATTR(MAXIMUM_TEXTURE2D_ARRAY_NUMSLICES);
GET_ATTR(SURFACE_ALIGNMENT);
GET_ATTR(CONCURRENT_KERNELS);
GET_ATTR(ECC_ENABLED);
GET_ATTR(TCC_DRIVER);
GET_ATTR(MEMORY_CLOCK_RATE);
GET_ATTR(GLOBAL_MEMORY_BUS_WIDTH);
GET_ATTR(L2_CACHE_SIZE);
GET_ATTR(MAX_THREADS_PER_MULTIPROCESSOR);
GET_ATTR(ASYNC_ENGINE_COUNT);
GET_ATTR(UNIFIED_ADDRESSING);
GET_ATTR(MAXIMUM_TEXTURE1D_LAYERED_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE1D_LAYERED_LAYERS);
GET_ATTR(CAN_TEX2D_GATHER);
GET_ATTR(MAXIMUM_TEXTURE2D_GATHER_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_GATHER_HEIGHT);
GET_ATTR(MAXIMUM_TEXTURE3D_WIDTH_ALTERNATE);
GET_ATTR(MAXIMUM_TEXTURE3D_HEIGHT_ALTERNATE);
GET_ATTR(MAXIMUM_TEXTURE3D_DEPTH_ALTERNATE);
GET_ATTR(TEXTURE_PITCH_ALIGNMENT);
GET_ATTR(MAXIMUM_TEXTURECUBEMAP_WIDTH);
GET_ATTR(MAXIMUM_TEXTURECUBEMAP_LAYERED_WIDTH);
GET_ATTR(MAXIMUM_TEXTURECUBEMAP_LAYERED_LAYERS);
GET_ATTR(MAXIMUM_SURFACE1D_WIDTH);
GET_ATTR(MAXIMUM_SURFACE2D_WIDTH);
GET_ATTR(MAXIMUM_SURFACE2D_HEIGHT);
GET_ATTR(MAXIMUM_SURFACE3D_WIDTH);
GET_ATTR(MAXIMUM_SURFACE3D_HEIGHT);
GET_ATTR(MAXIMUM_SURFACE3D_DEPTH);
GET_ATTR(MAXIMUM_SURFACE1D_LAYERED_WIDTH);
GET_ATTR(MAXIMUM_SURFACE1D_LAYERED_LAYERS);
GET_ATTR(MAXIMUM_SURFACE2D_LAYERED_WIDTH);
GET_ATTR(MAXIMUM_SURFACE2D_LAYERED_HEIGHT);
GET_ATTR(MAXIMUM_SURFACE2D_LAYERED_LAYERS);
GET_ATTR(MAXIMUM_SURFACECUBEMAP_WIDTH);
GET_ATTR(MAXIMUM_SURFACECUBEMAP_LAYERED_WIDTH);
GET_ATTR(MAXIMUM_SURFACECUBEMAP_LAYERED_LAYERS);
GET_ATTR(MAXIMUM_TEXTURE1D_LINEAR_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_LINEAR_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_LINEAR_HEIGHT);
GET_ATTR(MAXIMUM_TEXTURE2D_LINEAR_PITCH);
GET_ATTR(MAXIMUM_TEXTURE2D_MIPMAPPED_WIDTH);
GET_ATTR(MAXIMUM_TEXTURE2D_MIPMAPPED_HEIGHT);
GET_ATTR(COMPUTE_CAPABILITY_MAJOR);
GET_ATTR(COMPUTE_CAPABILITY_MINOR);
GET_ATTR(MAXIMUM_TEXTURE1D_MIPMAPPED_WIDTH);
GET_ATTR(STREAM_PRIORITIES_SUPPORTED);
GET_ATTR(GLOBAL_L1_CACHE_SUPPORTED);
GET_ATTR(LOCAL_L1_CACHE_SUPPORTED);
GET_ATTR(MAX_SHARED_MEMORY_PER_MULTIPROCESSOR);
GET_ATTR(MAX_REGISTERS_PER_MULTIPROCESSOR);
GET_ATTR(MANAGED_MEMORY);
GET_ATTR(MULTI_GPU_BOARD);
GET_ATTR(MULTI_GPU_BOARD_GROUP_ID);
#undef GET_ATTR
capabilities += "\n";
}
return capabilities;
}
CCL_NAMESPACE_END
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