d37dd97e45
Some of the functions might have been inlined, but others i don't see how that was possible (don't think virtual functions can be inlined here). In any case, better be explicitly optimal in the code.
2329 lines
69 KiB
C++
2329 lines
69 KiB
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.
|
|
*/
|
|
|
|
#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(const 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;
|
|
|
|
cuModule = 0;
|
|
cuFilterModule = 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)
|
|
{
|
|
/* TODO(sergey): Support kernels re-load for CUDA devices.
|
|
*
|
|
* Currently re-loading kernel will invalidate memory pointers,
|
|
* causing problems in cuCtxSynchronize.
|
|
*/
|
|
if(cuFilterModule && cuModule) {
|
|
VLOG(1) << "Skipping kernel reload, not currently supported.";
|
|
return true;
|
|
}
|
|
|
|
/* 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(¶m, 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(¶m));
|
|
}
|
|
else if(mem.data_height > 1) {
|
|
CUDA_MEMCPY2D param;
|
|
memset(¶m, 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(¶m));
|
|
}
|
|
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 = align_up(rect.z-rect.x, 4);
|
|
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 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,
|
|
&color_ptr,
|
|
&color_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,
|
|
&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)
|
|
{
|
|
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)
|
|
{
|
|
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();
|
|
}
|
|
|
|
bool denoising_detect_outliers(device_ptr image_ptr,
|
|
device_ptr variance_ptr,
|
|
device_ptr depth_ptr,
|
|
device_ptr output_ptr,
|
|
DenoisingTask *task)
|
|
{
|
|
if(have_error())
|
|
return false;
|
|
|
|
cuda_push_context();
|
|
|
|
CUfunction cuFilterDetectOutliers;
|
|
cuda_assert(cuModuleGetFunction(&cuFilterDetectOutliers, cuFilterModule, "kernel_cuda_filter_detect_outliers"));
|
|
cuda_assert(cuFuncSetCacheConfig(cuFilterDetectOutliers, CU_FUNC_CACHE_PREFER_L1));
|
|
CUDA_GET_BLOCKSIZE(cuFilterDetectOutliers,
|
|
task->rect.z-task->rect.x,
|
|
task->rect.w-task->rect.y);
|
|
|
|
void *args[] = {&image_ptr,
|
|
&variance_ptr,
|
|
&depth_ptr,
|
|
&output_ptr,
|
|
&task->rect,
|
|
&task->buffer.pass_stride};
|
|
|
|
CUDA_LAUNCH_KERNEL(cuFilterDetectOutliers, 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, &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.detect_outliers = function_bind(&CUDADevice::denoising_detect_outliers, 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(const 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
|