fccf506ed7
This is the internal implementation, not available from the API or interface yet. The algorithm takes into account past and future frames, both to get more coherent animation and reduce noise. Ref D3889.
2684 lines
81 KiB
C++
2684 lines
81 KiB
C++
/*
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* Copyright 2011-2013 Blender Foundation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <climits>
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#include <limits.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include "device/device.h"
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#include "device/device_denoising.h"
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#include "device/device_intern.h"
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#include "device/device_split_kernel.h"
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#include "render/buffers.h"
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#include "kernel/filter/filter_defines.h"
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#ifdef WITH_CUDA_DYNLOAD
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# include "cuew.h"
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#else
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# include "util/util_opengl.h"
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# include <cuda.h>
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# include <cudaGL.h>
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#endif
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#include "util/util_debug.h"
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#include "util/util_foreach.h"
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#include "util/util_logging.h"
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#include "util/util_map.h"
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#include "util/util_md5.h"
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#include "util/util_opengl.h"
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#include "util/util_path.h"
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#include "util/util_string.h"
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#include "util/util_system.h"
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#include "util/util_types.h"
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#include "util/util_time.h"
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#include "kernel/split/kernel_split_data_types.h"
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CCL_NAMESPACE_BEGIN
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#ifndef WITH_CUDA_DYNLOAD
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/* Transparently implement some functions, so majority of the file does not need
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* to worry about difference between dynamically loaded and linked CUDA at all.
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*/
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namespace {
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const char *cuewErrorString(CUresult result)
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{
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/* We can only give error code here without major code duplication, that
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* should be enough since dynamic loading is only being disabled by folks
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* who knows what they're doing anyway.
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*
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* NOTE: Avoid call from several threads.
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*/
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static string error;
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error = string_printf("%d", result);
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return error.c_str();
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}
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const char *cuewCompilerPath()
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{
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return CYCLES_CUDA_NVCC_EXECUTABLE;
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}
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int cuewCompilerVersion()
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{
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return (CUDA_VERSION / 100) + (CUDA_VERSION % 100 / 10);
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}
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} /* namespace */
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#endif /* WITH_CUDA_DYNLOAD */
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class CUDADevice;
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class CUDASplitKernel : public DeviceSplitKernel {
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CUDADevice *device;
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public:
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explicit CUDASplitKernel(CUDADevice *device);
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virtual uint64_t state_buffer_size(device_memory& kg, device_memory& data, size_t num_threads);
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virtual bool enqueue_split_kernel_data_init(const KernelDimensions& dim,
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RenderTile& rtile,
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int num_global_elements,
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device_memory& kernel_globals,
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device_memory& kernel_data_,
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device_memory& split_data,
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device_memory& ray_state,
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device_memory& queue_index,
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device_memory& use_queues_flag,
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device_memory& work_pool_wgs);
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virtual SplitKernelFunction* get_split_kernel_function(const string& kernel_name,
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const DeviceRequestedFeatures&);
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virtual int2 split_kernel_local_size();
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virtual int2 split_kernel_global_size(device_memory& kg, device_memory& data, DeviceTask *task);
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};
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/* Utility to push/pop CUDA context. */
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class CUDAContextScope {
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public:
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CUDAContextScope(CUDADevice *device);
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~CUDAContextScope();
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private:
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CUDADevice *device;
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};
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class CUDADevice : public Device
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{
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public:
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DedicatedTaskPool task_pool;
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CUdevice cuDevice;
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CUcontext cuContext;
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CUmodule cuModule, cuFilterModule;
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size_t device_texture_headroom;
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size_t device_working_headroom;
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bool move_texture_to_host;
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size_t map_host_used;
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size_t map_host_limit;
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int can_map_host;
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int cuDevId;
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int cuDevArchitecture;
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bool first_error;
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CUDASplitKernel *split_kernel;
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struct CUDAMem {
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CUDAMem()
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: texobject(0), array(0), map_host_pointer(0), free_map_host(false) {}
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CUtexObject texobject;
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CUarray array;
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void *map_host_pointer;
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bool free_map_host;
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};
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typedef map<device_memory*, CUDAMem> CUDAMemMap;
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CUDAMemMap cuda_mem_map;
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struct PixelMem {
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GLuint cuPBO;
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CUgraphicsResource cuPBOresource;
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GLuint cuTexId;
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int w, h;
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};
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map<device_ptr, PixelMem> pixel_mem_map;
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/* Bindless Textures */
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device_vector<TextureInfo> texture_info;
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bool need_texture_info;
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CUdeviceptr cuda_device_ptr(device_ptr mem)
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{
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return (CUdeviceptr)mem;
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}
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static bool have_precompiled_kernels()
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{
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string cubins_path = path_get("lib");
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return path_exists(cubins_path);
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}
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virtual bool show_samples() const
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{
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/* The CUDADevice only processes one tile at a time, so showing samples is fine. */
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return true;
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}
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virtual BVHLayoutMask get_bvh_layout_mask() const {
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return BVH_LAYOUT_BVH2;
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}
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/*#ifdef NDEBUG
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#define cuda_abort()
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#else
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#define cuda_abort() abort()
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#endif*/
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void cuda_error_documentation()
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{
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if(first_error) {
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fprintf(stderr, "\nRefer to the Cycles GPU rendering documentation for possible solutions:\n");
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fprintf(stderr, "https://docs.blender.org/manual/en/dev/render/cycles/gpu_rendering.html\n\n");
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first_error = false;
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}
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}
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#define cuda_assert(stmt) \
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{ \
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CUresult result = stmt; \
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\
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if(result != CUDA_SUCCESS) { \
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string message = string_printf("CUDA error: %s in %s, line %d", cuewErrorString(result), #stmt, __LINE__); \
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if(error_msg == "") \
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error_msg = message; \
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fprintf(stderr, "%s\n", message.c_str()); \
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/*cuda_abort();*/ \
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cuda_error_documentation(); \
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} \
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} (void) 0
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bool cuda_error_(CUresult result, const string& stmt)
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{
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if(result == CUDA_SUCCESS)
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return false;
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string message = string_printf("CUDA error at %s: %s", stmt.c_str(), cuewErrorString(result));
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if(error_msg == "")
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error_msg = message;
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fprintf(stderr, "%s\n", message.c_str());
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cuda_error_documentation();
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return true;
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}
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#define cuda_error(stmt) cuda_error_(stmt, #stmt)
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void cuda_error_message(const string& message)
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{
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if(error_msg == "")
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error_msg = message;
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fprintf(stderr, "%s\n", message.c_str());
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cuda_error_documentation();
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}
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CUDADevice(DeviceInfo& info, Stats &stats, Profiler &profiler, bool background_)
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: Device(info, stats, profiler, background_),
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texture_info(this, "__texture_info", MEM_TEXTURE)
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{
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first_error = true;
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background = background_;
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cuDevId = info.num;
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cuDevice = 0;
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cuContext = 0;
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cuModule = 0;
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cuFilterModule = 0;
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split_kernel = NULL;
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need_texture_info = false;
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device_texture_headroom = 0;
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device_working_headroom = 0;
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move_texture_to_host = false;
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map_host_limit = 0;
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map_host_used = 0;
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can_map_host = 0;
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/* Intialize CUDA. */
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if(cuda_error(cuInit(0)))
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return;
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/* Setup device and context. */
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if(cuda_error(cuDeviceGet(&cuDevice, cuDevId)))
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return;
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/* CU_CTX_MAP_HOST for mapping host memory when out of device memory.
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* CU_CTX_LMEM_RESIZE_TO_MAX for reserving local memory ahead of render,
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* so we can predict which memory to map to host. */
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cuda_assert(cuDeviceGetAttribute(&can_map_host, CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY, cuDevice));
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unsigned int ctx_flags = CU_CTX_LMEM_RESIZE_TO_MAX;
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if(can_map_host) {
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ctx_flags |= CU_CTX_MAP_HOST;
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init_host_memory();
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}
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/* Create context. */
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CUresult result;
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if(background) {
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result = cuCtxCreate(&cuContext, ctx_flags, cuDevice);
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}
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else {
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result = cuGLCtxCreate(&cuContext, ctx_flags, cuDevice);
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if(result != CUDA_SUCCESS) {
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result = cuCtxCreate(&cuContext, ctx_flags, cuDevice);
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background = true;
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}
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}
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if(cuda_error_(result, "cuCtxCreate"))
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return;
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int major, minor;
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cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevId);
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cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevId);
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cuDevArchitecture = major*100 + minor*10;
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/* Pop context set by cuCtxCreate. */
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cuCtxPopCurrent(NULL);
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}
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~CUDADevice()
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{
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task_pool.stop();
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delete split_kernel;
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texture_info.free();
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cuda_assert(cuCtxDestroy(cuContext));
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}
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bool support_device(const DeviceRequestedFeatures& /*requested_features*/)
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{
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int major, minor;
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cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevId);
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cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevId);
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/* We only support sm_30 and above */
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if(major < 3) {
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cuda_error_message(string_printf("CUDA device supported only with compute capability 3.0 or up, found %d.%d.", major, minor));
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return false;
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}
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return true;
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}
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bool use_adaptive_compilation()
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{
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return DebugFlags().cuda.adaptive_compile;
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}
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bool use_split_kernel()
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{
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return DebugFlags().cuda.split_kernel;
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}
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/* Common NVCC flags which stays the same regardless of shading model,
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* kernel sources md5 and only depends on compiler or compilation settings.
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*/
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string compile_kernel_get_common_cflags(
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const DeviceRequestedFeatures& requested_features,
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bool filter=false, bool split=false)
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{
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const int machine = system_cpu_bits();
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const string source_path = path_get("source");
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const string include_path = source_path;
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string cflags = string_printf("-m%d "
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"--ptxas-options=\"-v\" "
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"--use_fast_math "
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"-DNVCC "
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"-I\"%s\"",
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machine,
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include_path.c_str());
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if(!filter && use_adaptive_compilation()) {
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cflags += " " + requested_features.get_build_options();
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}
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const char *extra_cflags = getenv("CYCLES_CUDA_EXTRA_CFLAGS");
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if(extra_cflags) {
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cflags += string(" ") + string(extra_cflags);
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}
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#ifdef WITH_CYCLES_DEBUG
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cflags += " -D__KERNEL_DEBUG__";
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#endif
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if(split) {
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cflags += " -D__SPLIT__";
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}
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return cflags;
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}
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bool compile_check_compiler() {
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const char *nvcc = cuewCompilerPath();
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if(nvcc == NULL) {
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cuda_error_message("CUDA nvcc compiler not found. "
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"Install CUDA toolkit in default location.");
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return false;
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}
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const int cuda_version = cuewCompilerVersion();
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VLOG(1) << "Found nvcc " << nvcc
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<< ", CUDA version " << cuda_version
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<< ".";
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const int major = cuda_version / 10, minor = cuda_version % 10;
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if(cuda_version == 0) {
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cuda_error_message("CUDA nvcc compiler version could not be parsed.");
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return false;
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}
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if(cuda_version < 80) {
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printf("Unsupported CUDA version %d.%d detected, "
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"you need CUDA 8.0 or newer.\n",
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major, minor);
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return false;
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}
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else if(cuda_version != 80) {
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printf("CUDA version %d.%d detected, build may succeed but only "
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"CUDA 8.0 is officially supported.\n",
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major, minor);
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}
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return true;
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}
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string compile_kernel(const DeviceRequestedFeatures& requested_features,
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bool filter=false, bool split=false)
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{
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const char *name, *source;
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if(filter) {
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name = "filter";
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source = "filter.cu";
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}
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else if(split) {
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name = "kernel_split";
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source = "kernel_split.cu";
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}
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else {
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name = "kernel";
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source = "kernel.cu";
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}
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/* Compute cubin name. */
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int major, minor;
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cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevId);
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cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevId);
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/* Attempt to use kernel provided with Blender. */
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if(!use_adaptive_compilation()) {
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const string cubin = path_get(string_printf("lib/%s_sm_%d%d.cubin",
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name, major, minor));
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VLOG(1) << "Testing for pre-compiled kernel " << cubin << ".";
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if(path_exists(cubin)) {
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VLOG(1) << "Using precompiled kernel.";
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return cubin;
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}
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}
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const string common_cflags =
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compile_kernel_get_common_cflags(requested_features, filter, split);
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/* Try to use locally compiled kernel. */
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const string source_path = path_get("source");
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const string kernel_md5 = path_files_md5_hash(source_path);
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/* We include cflags into md5 so changing cuda toolkit or changing other
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* compiler command line arguments makes sure cubin gets re-built.
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*/
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const string cubin_md5 = util_md5_string(kernel_md5 + common_cflags);
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const string cubin_file = string_printf("cycles_%s_sm%d%d_%s.cubin",
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name, major, minor,
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cubin_md5.c_str());
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const string cubin = path_cache_get(path_join("kernels", cubin_file));
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VLOG(1) << "Testing for locally compiled kernel " << cubin << ".";
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if(path_exists(cubin)) {
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VLOG(1) << "Using locally compiled kernel.";
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return cubin;
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}
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|
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#ifdef _WIN32
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if(have_precompiled_kernels()) {
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if(major < 3) {
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cuda_error_message(string_printf(
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"CUDA device requires compute capability 3.0 or up, "
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"found %d.%d. Your GPU is not supported.",
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major, minor));
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}
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else {
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cuda_error_message(string_printf(
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"CUDA binary kernel for this graphics card compute "
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"capability (%d.%d) not found.",
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major, minor));
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}
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return "";
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}
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#endif
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|
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/* Compile. */
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if(!compile_check_compiler()) {
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return "";
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}
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const char *nvcc = cuewCompilerPath();
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const string kernel = path_join(
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path_join(source_path, "kernel"),
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path_join("kernels",
|
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path_join("cuda", source)));
|
|
double starttime = time_dt();
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printf("Compiling CUDA kernel ...\n");
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path_create_directories(cubin);
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|
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string command = string_printf("\"%s\" "
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"-arch=sm_%d%d "
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"--cubin \"%s\" "
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"-o \"%s\" "
|
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"%s ",
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nvcc,
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major, minor,
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kernel.c_str(),
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cubin.c_str(),
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common_cflags.c_str());
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printf("%s\n", command.c_str());
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|
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if(system(command.c_str()) == -1) {
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cuda_error_message("Failed to execute compilation command, "
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|
"see console for details.");
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return "";
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}
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|
|
/* Verify if compilation succeeded */
|
|
if(!path_exists(cubin)) {
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cuda_error_message("CUDA kernel compilation failed, "
|
|
"see console for details.");
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return "";
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}
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|
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printf("Kernel compilation finished in %.2lfs.\n", time_dt() - starttime);
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|
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return cubin;
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}
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|
|
|
bool load_kernels(const DeviceRequestedFeatures& requested_features)
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|
{
|
|
/* 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 */
|
|
CUDAContextScope scope(this);
|
|
|
|
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()));
|
|
|
|
if(result == CUDA_SUCCESS) {
|
|
reserve_local_memory(requested_features);
|
|
}
|
|
|
|
return (result == CUDA_SUCCESS);
|
|
}
|
|
|
|
void reserve_local_memory(const DeviceRequestedFeatures& requested_features)
|
|
{
|
|
if(use_split_kernel()) {
|
|
/* Split kernel mostly uses global memory and adaptive compilation,
|
|
* difficult to predict how much is needed currently. */
|
|
return;
|
|
}
|
|
|
|
/* Together with CU_CTX_LMEM_RESIZE_TO_MAX, this reserves local memory
|
|
* needed for kernel launches, so that we can reliably figure out when
|
|
* to allocate scene data in mapped host memory. */
|
|
CUDAContextScope scope(this);
|
|
|
|
size_t total = 0, free_before = 0, free_after = 0;
|
|
cuMemGetInfo(&free_before, &total);
|
|
|
|
/* Get kernel function. */
|
|
CUfunction cuPathTrace;
|
|
|
|
if(requested_features.use_integrator_branched) {
|
|
cuda_assert(cuModuleGetFunction(&cuPathTrace, cuModule, "kernel_cuda_branched_path_trace"));
|
|
}
|
|
else {
|
|
cuda_assert(cuModuleGetFunction(&cuPathTrace, cuModule, "kernel_cuda_path_trace"));
|
|
}
|
|
|
|
cuda_assert(cuFuncSetCacheConfig(cuPathTrace, CU_FUNC_CACHE_PREFER_L1));
|
|
|
|
int min_blocks, num_threads_per_block;
|
|
cuda_assert(cuOccupancyMaxPotentialBlockSize(&min_blocks, &num_threads_per_block, cuPathTrace, NULL, 0, 0));
|
|
|
|
/* Launch kernel, using just 1 block appears sufficient to reserve
|
|
* memory for all multiprocessors. It would be good to do this in
|
|
* parallel for the multi GPU case still to make it faster. */
|
|
CUdeviceptr d_work_tiles = 0;
|
|
uint total_work_size = 0;
|
|
|
|
void *args[] = {&d_work_tiles,
|
|
&total_work_size};
|
|
|
|
cuda_assert(cuLaunchKernel(cuPathTrace,
|
|
1, 1, 1,
|
|
num_threads_per_block, 1, 1,
|
|
0, 0, args, 0));
|
|
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
cuMemGetInfo(&free_after, &total);
|
|
VLOG(1) << "Local memory reserved "
|
|
<< string_human_readable_number(free_before - free_after) << " bytes. ("
|
|
<< string_human_readable_size(free_before - free_after) << ")";
|
|
|
|
#if 0
|
|
/* For testing mapped host memory, fill up device memory. */
|
|
const size_t keep_mb = 1024;
|
|
|
|
while(free_after > keep_mb * 1024 * 1024LL) {
|
|
CUdeviceptr tmp;
|
|
cuda_assert(cuMemAlloc(&tmp, 10 * 1024 * 1024LL));
|
|
cuMemGetInfo(&free_after, &total);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
void init_host_memory()
|
|
{
|
|
/* Limit amount of host mapped memory, because allocating too much can
|
|
* cause system instability. Leave at least half or 4 GB of system
|
|
* memory free, whichever is smaller. */
|
|
size_t default_limit = 4 * 1024 * 1024 * 1024LL;
|
|
size_t system_ram = system_physical_ram();
|
|
|
|
if(system_ram > 0) {
|
|
if(system_ram / 2 > default_limit) {
|
|
map_host_limit = system_ram - default_limit;
|
|
}
|
|
else {
|
|
map_host_limit = system_ram / 2;
|
|
}
|
|
}
|
|
else {
|
|
VLOG(1) << "Mapped host memory disabled, failed to get system RAM";
|
|
map_host_limit = 0;
|
|
}
|
|
|
|
/* Amount of device memory to keep is free after texture memory
|
|
* and working memory allocations respectively. We set the working
|
|
* memory limit headroom lower so that some space is left after all
|
|
* texture memory allocations. */
|
|
device_working_headroom = 32 * 1024 * 1024LL; // 32MB
|
|
device_texture_headroom = 128 * 1024 * 1024LL; // 128MB
|
|
|
|
VLOG(1) << "Mapped host memory limit set to "
|
|
<< string_human_readable_number(map_host_limit) << " bytes. ("
|
|
<< string_human_readable_size(map_host_limit) << ")";
|
|
}
|
|
|
|
void load_texture_info()
|
|
{
|
|
if(need_texture_info) {
|
|
texture_info.copy_to_device();
|
|
need_texture_info = false;
|
|
}
|
|
}
|
|
|
|
void move_textures_to_host(size_t size, bool for_texture)
|
|
{
|
|
/* Signal to reallocate textures in host memory only. */
|
|
move_texture_to_host = true;
|
|
|
|
while(size > 0) {
|
|
/* Find suitable memory allocation to move. */
|
|
device_memory *max_mem = NULL;
|
|
size_t max_size = 0;
|
|
bool max_is_image = false;
|
|
|
|
foreach(CUDAMemMap::value_type& pair, cuda_mem_map) {
|
|
device_memory& mem = *pair.first;
|
|
CUDAMem *cmem = &pair.second;
|
|
|
|
bool is_texture = (mem.type == MEM_TEXTURE) && (&mem != &texture_info);
|
|
bool is_image = is_texture && (mem.data_height > 1);
|
|
|
|
/* Can't move this type of memory. */
|
|
if(!is_texture || cmem->array) {
|
|
continue;
|
|
}
|
|
|
|
/* Already in host memory. */
|
|
if(cmem->map_host_pointer) {
|
|
continue;
|
|
}
|
|
|
|
/* For other textures, only move image textures. */
|
|
if(for_texture && !is_image) {
|
|
continue;
|
|
}
|
|
|
|
/* Try to move largest allocation, prefer moving images. */
|
|
if(is_image > max_is_image ||
|
|
(is_image == max_is_image && mem.device_size > max_size)) {
|
|
max_is_image = is_image;
|
|
max_size = mem.device_size;
|
|
max_mem = &mem;
|
|
}
|
|
}
|
|
|
|
/* Move to host memory. This part is mutex protected since
|
|
* multiple CUDA devices could be moving the memory. The
|
|
* first one will do it, and the rest will adopt the pointer. */
|
|
if(max_mem) {
|
|
VLOG(1) << "Move memory from device to host: " << max_mem->name;
|
|
|
|
static thread_mutex move_mutex;
|
|
thread_scoped_lock lock(move_mutex);
|
|
|
|
/* Preserve the original device pointer, in case of multi device
|
|
* we can't change it because the pointer mapping would break. */
|
|
device_ptr prev_pointer = max_mem->device_pointer;
|
|
size_t prev_size = max_mem->device_size;
|
|
|
|
tex_free(*max_mem);
|
|
tex_alloc(*max_mem);
|
|
size = (max_size >= size)? 0: size - max_size;
|
|
|
|
max_mem->device_pointer = prev_pointer;
|
|
max_mem->device_size = prev_size;
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
/* Update texture info array with new pointers. */
|
|
load_texture_info();
|
|
|
|
move_texture_to_host = false;
|
|
}
|
|
|
|
CUDAMem *generic_alloc(device_memory& mem, size_t pitch_padding = 0)
|
|
{
|
|
CUDAContextScope scope(this);
|
|
|
|
CUdeviceptr device_pointer = 0;
|
|
size_t size = mem.memory_size() + pitch_padding;
|
|
|
|
CUresult mem_alloc_result = CUDA_ERROR_OUT_OF_MEMORY;
|
|
const char *status = "";
|
|
|
|
/* First try allocating in device memory, respecting headroom. We make
|
|
* an exception for texture info. It is small and frequently accessed,
|
|
* so treat it as working memory.
|
|
*
|
|
* If there is not enough room for working memory, we will try to move
|
|
* textures to host memory, assuming the performance impact would have
|
|
* been worse for working memory. */
|
|
bool is_texture = (mem.type == MEM_TEXTURE) && (&mem != &texture_info);
|
|
bool is_image = is_texture && (mem.data_height > 1);
|
|
|
|
size_t headroom = (is_texture)? device_texture_headroom:
|
|
device_working_headroom;
|
|
|
|
size_t total = 0, free = 0;
|
|
cuMemGetInfo(&free, &total);
|
|
|
|
/* Move textures to host memory if needed. */
|
|
if(!move_texture_to_host && !is_image && (size + headroom) >= free) {
|
|
move_textures_to_host(size + headroom - free, is_texture);
|
|
cuMemGetInfo(&free, &total);
|
|
}
|
|
|
|
/* Allocate in device memory. */
|
|
if(!move_texture_to_host && (size + headroom) < free) {
|
|
mem_alloc_result = cuMemAlloc(&device_pointer, size);
|
|
if(mem_alloc_result == CUDA_SUCCESS) {
|
|
status = " in device memory";
|
|
}
|
|
}
|
|
|
|
/* Fall back to mapped host memory if needed and possible. */
|
|
void *map_host_pointer = 0;
|
|
bool free_map_host = false;
|
|
|
|
if(mem_alloc_result != CUDA_SUCCESS && can_map_host &&
|
|
map_host_used + size < map_host_limit) {
|
|
if(mem.shared_pointer) {
|
|
/* Another device already allocated host memory. */
|
|
mem_alloc_result = CUDA_SUCCESS;
|
|
map_host_pointer = mem.shared_pointer;
|
|
}
|
|
else {
|
|
/* Allocate host memory ourselves. */
|
|
mem_alloc_result = cuMemHostAlloc(&map_host_pointer, size,
|
|
CU_MEMHOSTALLOC_DEVICEMAP |
|
|
CU_MEMHOSTALLOC_WRITECOMBINED);
|
|
mem.shared_pointer = map_host_pointer;
|
|
free_map_host = true;
|
|
}
|
|
|
|
if(mem_alloc_result == CUDA_SUCCESS) {
|
|
cuda_assert(cuMemHostGetDevicePointer_v2(&device_pointer, mem.shared_pointer, 0));
|
|
map_host_used += size;
|
|
status = " in host memory";
|
|
|
|
/* Replace host pointer with our host allocation. Only works if
|
|
* CUDA memory layout is the same and has no pitch padding. Also
|
|
* does not work if we move textures to host during a render,
|
|
* since other devices might be using the memory. */
|
|
if(!move_texture_to_host && pitch_padding == 0 &&
|
|
mem.host_pointer && mem.host_pointer != mem.shared_pointer) {
|
|
memcpy(mem.shared_pointer, mem.host_pointer, size);
|
|
mem.host_free();
|
|
mem.host_pointer = mem.shared_pointer;
|
|
}
|
|
}
|
|
else {
|
|
status = " failed, out of host memory";
|
|
}
|
|
}
|
|
else if(mem_alloc_result != CUDA_SUCCESS) {
|
|
status = " failed, out of device and host memory";
|
|
}
|
|
|
|
if(mem_alloc_result != CUDA_SUCCESS) {
|
|
cuda_assert(mem_alloc_result);
|
|
}
|
|
|
|
if(mem.name) {
|
|
VLOG(1) << "Buffer allocate: " << mem.name << ", "
|
|
<< string_human_readable_number(mem.memory_size()) << " bytes. ("
|
|
<< string_human_readable_size(mem.memory_size()) << ")"
|
|
<< status;
|
|
}
|
|
|
|
mem.device_pointer = (device_ptr)device_pointer;
|
|
mem.device_size = size;
|
|
stats.mem_alloc(size);
|
|
|
|
if(!mem.device_pointer) {
|
|
return NULL;
|
|
}
|
|
|
|
/* Insert into map of allocations. */
|
|
CUDAMem *cmem = &cuda_mem_map[&mem];
|
|
cmem->map_host_pointer = map_host_pointer;
|
|
cmem->free_map_host = free_map_host;
|
|
return cmem;
|
|
}
|
|
|
|
void generic_copy_to(device_memory& mem)
|
|
{
|
|
if(mem.host_pointer && mem.device_pointer) {
|
|
CUDAContextScope scope(this);
|
|
|
|
if(mem.host_pointer != mem.shared_pointer) {
|
|
cuda_assert(cuMemcpyHtoD(cuda_device_ptr(mem.device_pointer),
|
|
mem.host_pointer,
|
|
mem.memory_size()));
|
|
}
|
|
}
|
|
}
|
|
|
|
void generic_free(device_memory& mem)
|
|
{
|
|
if(mem.device_pointer) {
|
|
CUDAContextScope scope(this);
|
|
const CUDAMem& cmem = cuda_mem_map[&mem];
|
|
|
|
if(cmem.map_host_pointer) {
|
|
/* Free host memory. */
|
|
if(cmem.free_map_host) {
|
|
cuMemFreeHost(cmem.map_host_pointer);
|
|
if(mem.host_pointer == mem.shared_pointer) {
|
|
mem.host_pointer = 0;
|
|
}
|
|
mem.shared_pointer = 0;
|
|
}
|
|
|
|
map_host_used -= mem.device_size;
|
|
}
|
|
else {
|
|
/* Free device memory. */
|
|
cuMemFree(mem.device_pointer);
|
|
}
|
|
|
|
stats.mem_free(mem.device_size);
|
|
mem.device_pointer = 0;
|
|
mem.device_size = 0;
|
|
|
|
cuda_mem_map.erase(cuda_mem_map.find(&mem));
|
|
}
|
|
}
|
|
|
|
void mem_alloc(device_memory& mem)
|
|
{
|
|
if(mem.type == MEM_PIXELS && !background) {
|
|
pixels_alloc(mem);
|
|
}
|
|
else if(mem.type == MEM_TEXTURE) {
|
|
assert(!"mem_alloc not supported for textures.");
|
|
}
|
|
else {
|
|
generic_alloc(mem);
|
|
}
|
|
}
|
|
|
|
void mem_copy_to(device_memory& mem)
|
|
{
|
|
if(mem.type == MEM_PIXELS) {
|
|
assert(!"mem_copy_to not supported for pixels.");
|
|
}
|
|
else if(mem.type == MEM_TEXTURE) {
|
|
tex_free(mem);
|
|
tex_alloc(mem);
|
|
}
|
|
else {
|
|
if(!mem.device_pointer) {
|
|
generic_alloc(mem);
|
|
}
|
|
|
|
generic_copy_to(mem);
|
|
}
|
|
}
|
|
|
|
void mem_copy_from(device_memory& mem, int y, int w, int h, int elem)
|
|
{
|
|
if(mem.type == MEM_PIXELS && !background) {
|
|
pixels_copy_from(mem, y, w, h);
|
|
}
|
|
else if(mem.type == MEM_TEXTURE) {
|
|
assert(!"mem_copy_from not supported for textures.");
|
|
}
|
|
else {
|
|
CUDAContextScope scope(this);
|
|
size_t offset = elem*y*w;
|
|
size_t size = elem*w*h;
|
|
|
|
if(mem.host_pointer && mem.device_pointer) {
|
|
cuda_assert(cuMemcpyDtoH((uchar*)mem.host_pointer + offset,
|
|
(CUdeviceptr)(mem.device_pointer + offset), size));
|
|
}
|
|
else if(mem.host_pointer) {
|
|
memset((char*)mem.host_pointer + offset, 0, size);
|
|
}
|
|
}
|
|
}
|
|
|
|
void mem_zero(device_memory& mem)
|
|
{
|
|
if(!mem.device_pointer) {
|
|
mem_alloc(mem);
|
|
}
|
|
|
|
if(mem.host_pointer) {
|
|
memset(mem.host_pointer, 0, mem.memory_size());
|
|
}
|
|
|
|
if(mem.device_pointer &&
|
|
(!mem.host_pointer || mem.host_pointer != mem.shared_pointer)) {
|
|
CUDAContextScope scope(this);
|
|
cuda_assert(cuMemsetD8(cuda_device_ptr(mem.device_pointer), 0, mem.memory_size()));
|
|
}
|
|
}
|
|
|
|
void mem_free(device_memory& mem)
|
|
{
|
|
if(mem.type == MEM_PIXELS && !background) {
|
|
pixels_free(mem);
|
|
}
|
|
else if(mem.type == MEM_TEXTURE) {
|
|
tex_free(mem);
|
|
}
|
|
else {
|
|
generic_free(mem);
|
|
}
|
|
}
|
|
|
|
virtual device_ptr mem_alloc_sub_ptr(device_memory& mem, int offset, int /*size*/)
|
|
{
|
|
return (device_ptr) (((char*) mem.device_pointer) + mem.memory_elements_size(offset));
|
|
}
|
|
|
|
void const_copy_to(const char *name, void *host, size_t size)
|
|
{
|
|
CUDAContextScope scope(this);
|
|
CUdeviceptr mem;
|
|
size_t bytes;
|
|
|
|
cuda_assert(cuModuleGetGlobal(&mem, &bytes, cuModule, name));
|
|
//assert(bytes == size);
|
|
cuda_assert(cuMemcpyHtoD(mem, host, size));
|
|
}
|
|
|
|
void tex_alloc(device_memory& mem)
|
|
{
|
|
CUDAContextScope scope(this);
|
|
|
|
/* General variables for both architectures */
|
|
string bind_name = mem.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(mem.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(mem.interpolation == INTERPOLATION_CLOSEST) {
|
|
filter_mode = CU_TR_FILTER_MODE_POINT;
|
|
}
|
|
else {
|
|
filter_mode = CU_TR_FILTER_MODE_LINEAR;
|
|
}
|
|
|
|
/* Data Storage */
|
|
if(mem.interpolation == INTERPOLATION_NONE) {
|
|
generic_alloc(mem);
|
|
generic_copy_to(mem);
|
|
|
|
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));
|
|
}
|
|
return;
|
|
}
|
|
|
|
/* Image Texture Storage */
|
|
CUarray_format_enum format;
|
|
switch(mem.data_type) {
|
|
case TYPE_UCHAR: format = CU_AD_FORMAT_UNSIGNED_INT8; break;
|
|
case TYPE_UINT16: format = CU_AD_FORMAT_UNSIGNED_INT16; 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;
|
|
}
|
|
|
|
CUDAMem *cmem = NULL;
|
|
CUarray array_3d = NULL;
|
|
size_t src_pitch = mem.data_width * dsize * mem.data_elements;
|
|
size_t dst_pitch = src_pitch;
|
|
|
|
if(mem.data_depth > 1) {
|
|
/* 3D texture using array, there is no API for linear memory. */
|
|
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;
|
|
|
|
VLOG(1) << "Array 3D allocate: " << mem.name << ", "
|
|
<< string_human_readable_number(mem.memory_size()) << " bytes. ("
|
|
<< string_human_readable_size(mem.memory_size()) << ")";
|
|
|
|
cuda_assert(cuArray3DCreate(&array_3d, &desc));
|
|
|
|
if(!array_3d) {
|
|
return;
|
|
}
|
|
|
|
CUDA_MEMCPY3D param;
|
|
memset(¶m, 0, sizeof(param));
|
|
param.dstMemoryType = CU_MEMORYTYPE_ARRAY;
|
|
param.dstArray = array_3d;
|
|
param.srcMemoryType = CU_MEMORYTYPE_HOST;
|
|
param.srcHost = mem.host_pointer;
|
|
param.srcPitch = src_pitch;
|
|
param.WidthInBytes = param.srcPitch;
|
|
param.Height = mem.data_height;
|
|
param.Depth = mem.data_depth;
|
|
|
|
cuda_assert(cuMemcpy3D(¶m));
|
|
|
|
mem.device_pointer = (device_ptr)array_3d;
|
|
mem.device_size = size;
|
|
stats.mem_alloc(size);
|
|
|
|
cmem = &cuda_mem_map[&mem];
|
|
cmem->texobject = 0;
|
|
cmem->array = array_3d;
|
|
}
|
|
else if(mem.data_height > 0) {
|
|
/* 2D texture, using pitch aligned linear memory. */
|
|
int alignment = 0;
|
|
cuda_assert(cuDeviceGetAttribute(&alignment, CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT, cuDevice));
|
|
dst_pitch = align_up(src_pitch, alignment);
|
|
size_t dst_size = dst_pitch * mem.data_height;
|
|
|
|
cmem = generic_alloc(mem, dst_size - mem.memory_size());
|
|
if(!cmem) {
|
|
return;
|
|
}
|
|
|
|
CUDA_MEMCPY2D param;
|
|
memset(¶m, 0, sizeof(param));
|
|
param.dstMemoryType = CU_MEMORYTYPE_DEVICE;
|
|
param.dstDevice = mem.device_pointer;
|
|
param.dstPitch = dst_pitch;
|
|
param.srcMemoryType = CU_MEMORYTYPE_HOST;
|
|
param.srcHost = mem.host_pointer;
|
|
param.srcPitch = src_pitch;
|
|
param.WidthInBytes = param.srcPitch;
|
|
param.Height = mem.data_height;
|
|
|
|
cuda_assert(cuMemcpy2DUnaligned(¶m));
|
|
}
|
|
else {
|
|
/* 1D texture, using linear memory. */
|
|
cmem = generic_alloc(mem);
|
|
if(!cmem) {
|
|
return;
|
|
}
|
|
|
|
cuda_assert(cuMemcpyHtoD(mem.device_pointer, mem.host_pointer, size));
|
|
}
|
|
|
|
/* Kepler+, bindless textures. */
|
|
int flat_slot = 0;
|
|
if(string_startswith(mem.name, "__tex_image")) {
|
|
int pos = string(mem.name).rfind("_");
|
|
flat_slot = atoi(mem.name + pos + 1);
|
|
}
|
|
else {
|
|
assert(0);
|
|
}
|
|
|
|
CUDA_RESOURCE_DESC resDesc;
|
|
memset(&resDesc, 0, sizeof(resDesc));
|
|
|
|
if(array_3d) {
|
|
resDesc.resType = CU_RESOURCE_TYPE_ARRAY;
|
|
resDesc.res.array.hArray = array_3d;
|
|
resDesc.flags = 0;
|
|
}
|
|
else if(mem.data_height > 0) {
|
|
resDesc.resType = CU_RESOURCE_TYPE_PITCH2D;
|
|
resDesc.res.pitch2D.devPtr = mem.device_pointer;
|
|
resDesc.res.pitch2D.format = format;
|
|
resDesc.res.pitch2D.numChannels = mem.data_elements;
|
|
resDesc.res.pitch2D.height = mem.data_height;
|
|
resDesc.res.pitch2D.width = mem.data_width;
|
|
resDesc.res.pitch2D.pitchInBytes = dst_pitch;
|
|
}
|
|
else {
|
|
resDesc.resType = CU_RESOURCE_TYPE_LINEAR;
|
|
resDesc.res.linear.devPtr = mem.device_pointer;
|
|
resDesc.res.linear.format = format;
|
|
resDesc.res.linear.numChannels = mem.data_elements;
|
|
resDesc.res.linear.sizeInBytes = mem.device_size;
|
|
}
|
|
|
|
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;
|
|
|
|
cuda_assert(cuTexObjectCreate(&cmem->texobject, &resDesc, &texDesc, NULL));
|
|
|
|
/* Resize once */
|
|
if(flat_slot >= texture_info.size()) {
|
|
/* Allocate some slots in advance, to reduce amount
|
|
* of re-allocations. */
|
|
texture_info.resize(flat_slot + 128);
|
|
}
|
|
|
|
/* Set Mapping and tag that we need to (re-)upload to device */
|
|
TextureInfo& info = texture_info[flat_slot];
|
|
info.data = (uint64_t)cmem->texobject;
|
|
info.cl_buffer = 0;
|
|
info.interpolation = mem.interpolation;
|
|
info.extension = mem.extension;
|
|
info.width = mem.data_width;
|
|
info.height = mem.data_height;
|
|
info.depth = mem.data_depth;
|
|
need_texture_info = true;
|
|
}
|
|
|
|
void tex_free(device_memory& mem)
|
|
{
|
|
if(mem.device_pointer) {
|
|
CUDAContextScope scope(this);
|
|
const CUDAMem& cmem = cuda_mem_map[&mem];
|
|
|
|
if(cmem.texobject) {
|
|
/* Free bindless texture. */
|
|
cuTexObjectDestroy(cmem.texobject);
|
|
}
|
|
|
|
if(cmem.array) {
|
|
/* Free array. */
|
|
cuArrayDestroy(cmem.array);
|
|
stats.mem_free(mem.device_size);
|
|
mem.device_pointer = 0;
|
|
mem.device_size = 0;
|
|
|
|
cuda_mem_map.erase(cuda_mem_map.find(&mem));
|
|
}
|
|
else {
|
|
generic_free(mem);
|
|
}
|
|
}
|
|
}
|
|
|
|
#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));
|
|
|
|
/* Similar as above, but for 1-dimensional blocks. */
|
|
#define CUDA_GET_BLOCKSIZE_1D(func, w, h) \
|
|
int threads_per_block; \
|
|
cuda_assert(cuFuncGetAttribute(&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, func)); \
|
|
int xblocks = ((w) + threads_per_block - 1)/threads_per_block; \
|
|
int yblocks = h;
|
|
|
|
#define CUDA_LAUNCH_KERNEL_1D(func, args) \
|
|
cuda_assert(cuLaunchKernel(func, \
|
|
xblocks, yblocks, 1, \
|
|
threads_per_block, 1, 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;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
int stride = task->buffer.stride;
|
|
int w = task->buffer.width;
|
|
int h = task->buffer.h;
|
|
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;
|
|
|
|
int pass_stride = task->buffer.pass_stride;
|
|
int num_shifts = (2*r+1)*(2*r+1);
|
|
int channel_offset = task->nlm_state.is_color? task->buffer.pass_stride : 0;
|
|
int frame_offset = 0;
|
|
|
|
if(have_error())
|
|
return false;
|
|
|
|
CUdeviceptr difference = cuda_device_ptr(task->buffer.temporary_mem.device_pointer);
|
|
CUdeviceptr blurDifference = difference + sizeof(float)*pass_stride*num_shifts;
|
|
CUdeviceptr weightAccum = difference + 2*sizeof(float)*pass_stride*num_shifts;
|
|
CUdeviceptr scale_ptr = 0;
|
|
|
|
cuda_assert(cuMemsetD8(weightAccum, 0, sizeof(float)*pass_stride));
|
|
cuda_assert(cuMemsetD8(out_ptr, 0, sizeof(float)*pass_stride));
|
|
|
|
{
|
|
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMUpdateOutput;
|
|
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(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_GET_BLOCKSIZE_1D(cuNLMCalcDifference, w*h, num_shifts);
|
|
|
|
void *calc_difference_args[] = {&guide_ptr, &variance_ptr, &scale_ptr, &difference, &w, &h, &stride, &pass_stride, &r, &channel_offset, &frame_offset, &a, &k_2};
|
|
void *blur_args[] = {&difference, &blurDifference, &w, &h, &stride, &pass_stride, &r, &f};
|
|
void *calc_weight_args[] = {&blurDifference, &difference, &w, &h, &stride, &pass_stride, &r, &f};
|
|
void *update_output_args[] = {&blurDifference, &image_ptr, &out_ptr, &weightAccum, &w, &h, &stride, &pass_stride, &channel_offset, &r, &f};
|
|
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcDifference, calc_difference_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcWeight, calc_weight_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMUpdateOutput, update_output_args);
|
|
}
|
|
|
|
{
|
|
CUfunction cuNLMNormalize;
|
|
cuda_assert(cuModuleGetFunction(&cuNLMNormalize, cuFilterModule, "kernel_cuda_filter_nlm_normalize"));
|
|
cuda_assert(cuFuncSetCacheConfig(cuNLMNormalize, CU_FUNC_CACHE_PREFER_L1));
|
|
void *normalize_args[] = {&out_ptr, &weightAccum, &w, &h, &stride};
|
|
CUDA_GET_BLOCKSIZE(cuNLMNormalize, w, h);
|
|
CUDA_LAUNCH_KERNEL(cuNLMNormalize, normalize_args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
}
|
|
|
|
return !have_error();
|
|
}
|
|
|
|
bool denoising_construct_transform(DenoisingTask *task)
|
|
{
|
|
if(have_error())
|
|
return false;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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->tile_info_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,
|
|
&task->buffer.frame_stride,
|
|
&task->buffer.use_time};
|
|
CUDA_LAUNCH_KERNEL(cuFilterConstructTransform, args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
return !have_error();
|
|
}
|
|
|
|
bool denoising_accumulate(device_ptr color_ptr,
|
|
device_ptr color_variance_ptr,
|
|
device_ptr scale_ptr,
|
|
int frame,
|
|
DenoisingTask *task)
|
|
{
|
|
if(have_error())
|
|
return false;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
int r = task->radius;
|
|
int f = 4;
|
|
float a = 1.0f;
|
|
float k_2 = task->nlm_k_2;
|
|
|
|
int w = task->reconstruction_state.source_w;
|
|
int h = task->reconstruction_state.source_h;
|
|
int stride = task->buffer.stride;
|
|
int frame_offset = frame * task->buffer.frame_stride;
|
|
int t = task->tile_info->frames[frame];
|
|
|
|
int pass_stride = task->buffer.pass_stride;
|
|
int num_shifts = (2*r+1)*(2*r+1);
|
|
|
|
if(have_error())
|
|
return false;
|
|
|
|
CUdeviceptr difference = cuda_device_ptr(task->buffer.temporary_mem.device_pointer);
|
|
CUdeviceptr blurDifference = difference + sizeof(float)*pass_stride*num_shifts;
|
|
|
|
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMConstructGramian;
|
|
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(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_GET_BLOCKSIZE_1D(cuNLMCalcDifference,
|
|
task->reconstruction_state.source_w * task->reconstruction_state.source_h,
|
|
num_shifts);
|
|
|
|
void *calc_difference_args[] = {&color_ptr,
|
|
&color_variance_ptr,
|
|
&scale_ptr,
|
|
&difference,
|
|
&w, &h,
|
|
&stride, &pass_stride,
|
|
&r, &pass_stride,
|
|
&frame_offset,
|
|
&a, &k_2};
|
|
void *blur_args[] = {&difference, &blurDifference, &w, &h, &stride, &pass_stride, &r, &f};
|
|
void *calc_weight_args[] = {&blurDifference, &difference, &w, &h, &stride, &pass_stride, &r, &f};
|
|
void *construct_gramian_args[] = {&t,
|
|
&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,
|
|
&task->reconstruction_state.filter_window,
|
|
&w, &h, &stride,
|
|
&pass_stride, &r,
|
|
&f,
|
|
&frame_offset,
|
|
&task->buffer.use_time};
|
|
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcDifference, calc_difference_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcWeight, calc_weight_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
|
|
CUDA_LAUNCH_KERNEL_1D(cuNLMConstructGramian, construct_gramian_args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
return !have_error();
|
|
}
|
|
|
|
bool denoising_solve(device_ptr output_ptr,
|
|
DenoisingTask *task)
|
|
{
|
|
CUfunction cuFinalize;
|
|
cuda_assert(cuModuleGetFunction(&cuFinalize, cuFilterModule, "kernel_cuda_filter_finalize"));
|
|
cuda_assert(cuFuncSetCacheConfig(cuFinalize, CU_FUNC_CACHE_PREFER_L1));
|
|
void *finalize_args[] = {&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_GET_BLOCKSIZE(cuFinalize,
|
|
task->reconstruction_state.source_w,
|
|
task->reconstruction_state.source_h);
|
|
CUDA_LAUNCH_KERNEL(cuFinalize, finalize_args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
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;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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());
|
|
|
|
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;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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);
|
|
|
|
void *args[] = {&task->render_buffer.samples,
|
|
&task->tile_info_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.offset};
|
|
CUDA_LAUNCH_KERNEL(cuFilterDivideShadow, args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
return !have_error();
|
|
}
|
|
|
|
bool denoising_get_feature(int mean_offset,
|
|
int variance_offset,
|
|
device_ptr mean_ptr,
|
|
device_ptr variance_ptr,
|
|
float scale,
|
|
DenoisingTask *task)
|
|
{
|
|
if(have_error())
|
|
return false;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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);
|
|
|
|
void *args[] = {&task->render_buffer.samples,
|
|
&task->tile_info_mem.device_pointer,
|
|
&mean_offset,
|
|
&variance_offset,
|
|
&mean_ptr,
|
|
&variance_ptr,
|
|
&scale,
|
|
&task->rect,
|
|
&task->render_buffer.pass_stride,
|
|
&task->render_buffer.offset};
|
|
CUDA_LAUNCH_KERNEL(cuFilterGetFeature, args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
return !have_error();
|
|
}
|
|
|
|
bool denoising_write_feature(int out_offset,
|
|
device_ptr from_ptr,
|
|
device_ptr buffer_ptr,
|
|
DenoisingTask *task)
|
|
{
|
|
if(have_error())
|
|
return false;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
CUfunction cuFilterWriteFeature;
|
|
cuda_assert(cuModuleGetFunction(&cuFilterWriteFeature, cuFilterModule, "kernel_cuda_filter_write_feature"));
|
|
cuda_assert(cuFuncSetCacheConfig(cuFilterWriteFeature, CU_FUNC_CACHE_PREFER_L1));
|
|
CUDA_GET_BLOCKSIZE(cuFilterWriteFeature,
|
|
task->filter_area.z,
|
|
task->filter_area.w);
|
|
|
|
void *args[] = {&task->render_buffer.samples,
|
|
&task->reconstruction_state.buffer_params,
|
|
&task->filter_area,
|
|
&from_ptr,
|
|
&buffer_ptr,
|
|
&out_offset,
|
|
&task->rect};
|
|
CUDA_LAUNCH_KERNEL(cuFilterWriteFeature, args);
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
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;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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());
|
|
|
|
return !have_error();
|
|
}
|
|
|
|
void denoise(RenderTile &rtile, DenoisingTask& denoising)
|
|
{
|
|
denoising.functions.construct_transform = function_bind(&CUDADevice::denoising_construct_transform, this, &denoising);
|
|
denoising.functions.accumulate = function_bind(&CUDADevice::denoising_accumulate, this, _1, _2, _3, _4, &denoising);
|
|
denoising.functions.solve = function_bind(&CUDADevice::denoising_solve, this, _1, &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, _5, &denoising);
|
|
denoising.functions.write_feature = function_bind(&CUDADevice::denoising_write_feature, this, _1, _2, _3, &denoising);
|
|
denoising.functions.detect_outliers = function_bind(&CUDADevice::denoising_detect_outliers, this, _1, _2, _3, _4, &denoising);
|
|
|
|
denoising.filter_area = make_int4(rtile.x, rtile.y, rtile.w, rtile.h);
|
|
denoising.render_buffer.samples = rtile.sample;
|
|
denoising.buffer.gpu_temporary_mem = true;
|
|
|
|
denoising.run_denoising(&rtile);
|
|
}
|
|
|
|
void path_trace(DeviceTask& task, RenderTile& rtile, device_vector<WorkTile>& work_tiles)
|
|
{
|
|
scoped_timer timer(&rtile.buffers->render_time);
|
|
|
|
if(have_error())
|
|
return;
|
|
|
|
CUDAContextScope scope(this);
|
|
CUfunction cuPathTrace;
|
|
|
|
/* Get kernel function. */
|
|
if(task.integrator_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;
|
|
}
|
|
|
|
cuda_assert(cuFuncSetCacheConfig(cuPathTrace, CU_FUNC_CACHE_PREFER_L1));
|
|
|
|
/* Allocate work tile. */
|
|
work_tiles.alloc(1);
|
|
|
|
WorkTile *wtile = work_tiles.data();
|
|
wtile->x = rtile.x;
|
|
wtile->y = rtile.y;
|
|
wtile->w = rtile.w;
|
|
wtile->h = rtile.h;
|
|
wtile->offset = rtile.offset;
|
|
wtile->stride = rtile.stride;
|
|
wtile->buffer = (float*)cuda_device_ptr(rtile.buffer);
|
|
|
|
/* Prepare work size. More step samples render faster, but for now we
|
|
* remain conservative for GPUs connected to a display to avoid driver
|
|
* timeouts and display freezing. */
|
|
int min_blocks, num_threads_per_block;
|
|
cuda_assert(cuOccupancyMaxPotentialBlockSize(&min_blocks, &num_threads_per_block, cuPathTrace, NULL, 0, 0));
|
|
if(!info.display_device) {
|
|
min_blocks *= 8;
|
|
}
|
|
|
|
uint step_samples = divide_up(min_blocks * num_threads_per_block, wtile->w * wtile->h);
|
|
|
|
/* Render all samples. */
|
|
int start_sample = rtile.start_sample;
|
|
int end_sample = rtile.start_sample + rtile.num_samples;
|
|
|
|
for(int sample = start_sample; sample < end_sample; sample += step_samples) {
|
|
/* Setup and copy work tile to device. */
|
|
wtile->start_sample = sample;
|
|
wtile->num_samples = min(step_samples, end_sample - sample);
|
|
work_tiles.copy_to_device();
|
|
|
|
CUdeviceptr d_work_tiles = cuda_device_ptr(work_tiles.device_pointer);
|
|
uint total_work_size = wtile->w * wtile->h * wtile->num_samples;
|
|
uint num_blocks = divide_up(total_work_size, num_threads_per_block);
|
|
|
|
/* Launch kernel. */
|
|
void *args[] = {&d_work_tiles,
|
|
&total_work_size};
|
|
|
|
cuda_assert(cuLaunchKernel(cuPathTrace,
|
|
num_blocks, 1, 1,
|
|
num_threads_per_block, 1, 1,
|
|
0, 0, args, 0));
|
|
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
/* Update progress. */
|
|
rtile.sample = sample + wtile->num_samples;
|
|
task.update_progress(&rtile, rtile.w*rtile.h*wtile->num_samples);
|
|
|
|
if(task.get_cancel()) {
|
|
if(task.need_finish_queue == false)
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
void film_convert(DeviceTask& task, device_ptr buffer, device_ptr rgba_byte, device_ptr rgba_half)
|
|
{
|
|
if(have_error())
|
|
return;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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_assert(cuCtxSynchronize());
|
|
}
|
|
|
|
void shader(DeviceTask& task)
|
|
{
|
|
if(have_error())
|
|
return;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
CUfunction cuShader;
|
|
CUdeviceptr d_input = cuda_device_ptr(task.shader_input);
|
|
CUdeviceptr d_output = cuda_device_ptr(task.shader_output);
|
|
|
|
/* get kernel function */
|
|
if(task.shader_eval_type >= SHADER_EVAL_BAKE) {
|
|
cuda_assert(cuModuleGetFunction(&cuShader, cuModule, "kernel_cuda_bake"));
|
|
}
|
|
else if(task.shader_eval_type == SHADER_EVAL_DISPLACE) {
|
|
cuda_assert(cuModuleGetFunction(&cuShader, cuModule, "kernel_cuda_displace"));
|
|
}
|
|
else {
|
|
cuda_assert(cuModuleGetFunction(&cuShader, cuModule, "kernel_cuda_background"));
|
|
}
|
|
|
|
/* 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;
|
|
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);
|
|
}
|
|
}
|
|
|
|
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)
|
|
{
|
|
PixelMem pmem;
|
|
|
|
pmem.w = mem.data_width;
|
|
pmem.h = mem.data_height;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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) {
|
|
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);
|
|
|
|
background = true;
|
|
}
|
|
}
|
|
|
|
void pixels_copy_from(device_memory& mem, int y, int w, int h)
|
|
{
|
|
PixelMem pmem = pixel_mem_map[mem.device_pointer];
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
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.host_pointer + offset, pixels + offset, sizeof(uchar)*4*w*h);
|
|
glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
|
|
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
|
|
}
|
|
|
|
void pixels_free(device_memory& mem)
|
|
{
|
|
if(mem.device_pointer) {
|
|
PixelMem pmem = pixel_mem_map[mem.device_pointer];
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
cuda_assert(cuGraphicsUnregisterResource(pmem.cuPBOresource));
|
|
glDeleteBuffers(1, &pmem.cuPBO);
|
|
glDeleteTextures(1, &pmem.cuTexId);
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
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)
|
|
{
|
|
assert(mem.type == MEM_PIXELS);
|
|
|
|
if(!background) {
|
|
PixelMem pmem = pixel_mem_map[mem.device_pointer];
|
|
float *vpointer;
|
|
|
|
CUDAContextScope scope(this);
|
|
|
|
/* 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);
|
|
|
|
return;
|
|
}
|
|
|
|
Device::draw_pixels(mem, y, w, h, dx, dy, width, height, transparent, draw_params);
|
|
}
|
|
|
|
void thread_run(DeviceTask *task)
|
|
{
|
|
CUDAContextScope scope(this);
|
|
|
|
if(task->type == DeviceTask::RENDER) {
|
|
DeviceRequestedFeatures requested_features;
|
|
if(use_split_kernel()) {
|
|
if(split_kernel == NULL) {
|
|
split_kernel = new CUDASplitKernel(this);
|
|
split_kernel->load_kernels(requested_features);
|
|
}
|
|
}
|
|
|
|
device_vector<WorkTile> work_tiles(this, "work_tiles", MEM_READ_ONLY);
|
|
|
|
/* keep rendering tiles until done */
|
|
RenderTile tile;
|
|
DenoisingTask denoising(this, *task);
|
|
|
|
while(task->acquire_tile(this, tile)) {
|
|
if(tile.task == RenderTile::PATH_TRACE) {
|
|
if(use_split_kernel()) {
|
|
device_only_memory<uchar> void_buffer(this, "void_buffer");
|
|
split_kernel->path_trace(task, tile, void_buffer, void_buffer);
|
|
}
|
|
else {
|
|
path_trace(*task, tile, work_tiles);
|
|
}
|
|
}
|
|
else if(tile.task == RenderTile::DENOISE) {
|
|
tile.sample = tile.start_sample + tile.num_samples;
|
|
|
|
denoise(tile, denoising);
|
|
|
|
task->update_progress(&tile, tile.w*tile.h);
|
|
}
|
|
|
|
task->release_tile(tile);
|
|
|
|
if(task->get_cancel()) {
|
|
if(task->need_finish_queue == false)
|
|
break;
|
|
}
|
|
}
|
|
|
|
work_tiles.free();
|
|
}
|
|
else if(task->type == DeviceTask::SHADER) {
|
|
shader(*task);
|
|
|
|
cuda_assert(cuCtxSynchronize());
|
|
}
|
|
}
|
|
|
|
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)
|
|
{
|
|
CUDAContextScope scope(this);
|
|
|
|
/* Load texture info. */
|
|
load_texture_info();
|
|
|
|
/* Synchronize all memory copies before executing task. */
|
|
cuda_assert(cuCtxSynchronize());
|
|
|
|
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);
|
|
}
|
|
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;
|
|
friend class CUDAContextScope;
|
|
};
|
|
|
|
/* 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
|
|
|
|
|
|
/* CUDA context scope. */
|
|
|
|
CUDAContextScope::CUDAContextScope(CUDADevice *device)
|
|
: device(device)
|
|
{
|
|
cuda_assert(cuCtxPushCurrent(device->cuContext));
|
|
}
|
|
|
|
CUDAContextScope::~CUDAContextScope()
|
|
{
|
|
cuda_assert(cuCtxPopCurrent(NULL));
|
|
}
|
|
|
|
/* 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[])
|
|
{
|
|
if(device->have_error())
|
|
return false;
|
|
|
|
CUDAContextScope scope(device);
|
|
|
|
/* 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 xblocks = (dim.global_size[0]*dim.global_size[1] + threads_per_block - 1)/threads_per_block;
|
|
|
|
cuda_assert(cuFuncSetCacheConfig(func, CU_FUNC_CACHE_PREFER_L1));
|
|
|
|
cuda_assert(cuLaunchKernel(func,
|
|
xblocks, 1, 1, /* blocks */
|
|
threads_per_block, 1, 1, /* threads */
|
|
0, 0, args, 0));
|
|
|
|
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)
|
|
{
|
|
CUDAContextScope scope(device);
|
|
|
|
device_vector<uint64_t> size_buffer(device, "size_buffer", MEM_READ_WRITE);
|
|
size_buffer.alloc(1);
|
|
size_buffer.zero_to_device();
|
|
|
|
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));
|
|
|
|
size_buffer.copy_from_device(0, 1, 1);
|
|
size_t size = size_buffer[0];
|
|
size_buffer.free();
|
|
|
|
return size;
|
|
}
|
|
|
|
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)
|
|
{
|
|
CUDAContextScope scope(device);
|
|
|
|
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_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;
|
|
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,
|
|
&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);
|
|
|
|
return !device->have_error();
|
|
}
|
|
|
|
SplitKernelFunction* CUDASplitKernel::get_split_kernel_function(const string& kernel_name,
|
|
const DeviceRequestedFeatures&)
|
|
{
|
|
CUDAContextScope scope(device);
|
|
CUfunction func;
|
|
|
|
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;
|
|
}
|
|
|
|
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*/)
|
|
{
|
|
CUDAContextScope scope(device);
|
|
size_t free;
|
|
size_t total;
|
|
|
|
cuda_assert(cuMemGetInfo(&free, &total));
|
|
|
|
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()
|
|
{
|
|
#ifdef WITH_CUDA_DYNLOAD
|
|
static bool initialized = false;
|
|
static bool result = false;
|
|
|
|
if(initialized)
|
|
return result;
|
|
|
|
initialized = true;
|
|
int cuew_result = cuewInit(CUEW_INIT_CUDA);
|
|
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 was 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, Profiler &profiler, bool background)
|
|
{
|
|
return new CUDADevice(info, stats, profiler, background);
|
|
}
|
|
|
|
static CUresult device_cuda_safe_init()
|
|
{
|
|
#ifdef _WIN32
|
|
__try {
|
|
return cuInit(0);
|
|
}
|
|
__except(EXCEPTION_EXECUTE_HANDLER) {
|
|
/* Ignore crashes inside the CUDA driver and hope we can
|
|
* survive even with corrupted CUDA installs. */
|
|
fprintf(stderr, "Cycles CUDA: driver crashed, continuing without CUDA.\n");
|
|
}
|
|
|
|
return CUDA_ERROR_NO_DEVICE;
|
|
#else
|
|
return cuInit(0);
|
|
#endif
|
|
}
|
|
|
|
void device_cuda_info(vector<DeviceInfo>& devices)
|
|
{
|
|
CUresult result = device_cuda_safe_init();
|
|
if(result != CUDA_SUCCESS) {
|
|
if(result != CUDA_ERROR_NO_DEVICE)
|
|
fprintf(stderr, "CUDA cuInit: %s\n", cuewErrorString(result));
|
|
return;
|
|
}
|
|
|
|
int count = 0;
|
|
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];
|
|
|
|
result = cuDeviceGetName(name, 256, num);
|
|
if(result != CUDA_SUCCESS) {
|
|
fprintf(stderr, "CUDA cuDeviceGetName: %s\n", cuewErrorString(result));
|
|
continue;
|
|
}
|
|
|
|
int major;
|
|
cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, num);
|
|
if(major < 3) {
|
|
VLOG(1) << "Ignoring device \"" << name
|
|
<< "\", this graphics card is no longer supported.";
|
|
continue;
|
|
}
|
|
|
|
DeviceInfo info;
|
|
|
|
info.type = DEVICE_CUDA;
|
|
info.description = string(name);
|
|
info.num = num;
|
|
|
|
info.advanced_shading = (major >= 3);
|
|
info.has_half_images = (major >= 3);
|
|
info.has_volume_decoupled = 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 and no compute preemption, we assume
|
|
* it is connected to a display and will freeze the display while doing
|
|
* computations. */
|
|
int timeout_attr = 0, preempt_attr = 0;
|
|
cuDeviceGetAttribute(&timeout_attr, CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT, num);
|
|
cuDeviceGetAttribute(&preempt_attr, CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED, num);
|
|
|
|
if(timeout_attr && !preempt_attr) {
|
|
VLOG(1) << "Device is recognized as display.";
|
|
info.description += " (Display)";
|
|
info.display_device = true;
|
|
display_devices.push_back(info);
|
|
}
|
|
else {
|
|
devices.push_back(info);
|
|
}
|
|
VLOG(1) << "Added device \"" << name << "\" with id \"" << info.id << "\".";
|
|
}
|
|
|
|
if(!display_devices.empty())
|
|
devices.insert(devices.end(), display_devices.begin(), display_devices.end());
|
|
}
|
|
|
|
string device_cuda_capabilities()
|
|
{
|
|
CUresult result = device_cuda_safe_init();
|
|
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
|