forked from bartvdbraak/blender
a18112249d
* Non-Progressive integrator is now available on the GPU (CUDA, sm_20 and above). Implementation details: * kernel_path_trace() has been split up into two functions: kernel_path_trace_non_progressive() and kernel_path_trace_progressive(). * We compile two CUDA kernel entry functions (in kernel.cu) for the two integrators, they are still inside one .cubin file but due to the kernel separation there should be no performance problem. I tested with the BMW file on my Geforce 540M and the render times were the same for 100 samples (1.57 min in my case). This is part of my GSoC project, SVN merge of r59032 + manual merge of UI changes for this from my branch.
1046 lines
27 KiB
C++
1046 lines
27 KiB
C++
/*
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* Copyright 2011, Blender Foundation.
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*
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* This program is free software; you can redistribute it and/or
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* modify it under the terms of the GNU General Public License
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* as published by the Free Software Foundation; either version 2
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* of the License, or (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software Foundation,
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* Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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*/
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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.h"
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#include "device_intern.h"
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#include "buffers.h"
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#include "util_cuda.h"
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#include "util_debug.h"
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#include "util_map.h"
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#include "util_opengl.h"
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#include "util_path.h"
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#include "util_system.h"
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#include "util_types.h"
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#include "util_time.h"
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CCL_NAMESPACE_BEGIN
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class CUDADevice : public Device
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{
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public:
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TaskPool task_pool;
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CUdevice cuDevice;
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CUcontext cuContext;
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CUmodule cuModule;
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map<device_ptr, bool> tex_interp_map;
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int cuDevId;
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bool first_error;
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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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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 const char *cuda_error_string(CUresult result)
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{
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switch(result) {
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case CUDA_SUCCESS: return "No errors";
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case CUDA_ERROR_INVALID_VALUE: return "Invalid value";
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case CUDA_ERROR_OUT_OF_MEMORY: return "Out of memory";
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case CUDA_ERROR_NOT_INITIALIZED: return "Driver not initialized";
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case CUDA_ERROR_DEINITIALIZED: return "Driver deinitialized";
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case CUDA_ERROR_NO_DEVICE: return "No CUDA-capable device available";
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case CUDA_ERROR_INVALID_DEVICE: return "Invalid device";
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case CUDA_ERROR_INVALID_IMAGE: return "Invalid kernel image";
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case CUDA_ERROR_INVALID_CONTEXT: return "Invalid context";
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case CUDA_ERROR_CONTEXT_ALREADY_CURRENT: return "Context already current";
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case CUDA_ERROR_MAP_FAILED: return "Map failed";
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case CUDA_ERROR_UNMAP_FAILED: return "Unmap failed";
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case CUDA_ERROR_ARRAY_IS_MAPPED: return "Array is mapped";
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case CUDA_ERROR_ALREADY_MAPPED: return "Already mapped";
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case CUDA_ERROR_NO_BINARY_FOR_GPU: return "No binary for GPU";
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case CUDA_ERROR_ALREADY_ACQUIRED: return "Already acquired";
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case CUDA_ERROR_NOT_MAPPED: return "Not mapped";
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case CUDA_ERROR_NOT_MAPPED_AS_ARRAY: return "Mapped resource not available for access as an array";
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case CUDA_ERROR_NOT_MAPPED_AS_POINTER: return "Mapped resource not available for access as a pointer";
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case CUDA_ERROR_ECC_UNCORRECTABLE: return "Uncorrectable ECC error detected";
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case CUDA_ERROR_UNSUPPORTED_LIMIT: return "CUlimit not supported by device";
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case CUDA_ERROR_INVALID_SOURCE: return "Invalid source";
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case CUDA_ERROR_FILE_NOT_FOUND: return "File not found";
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case CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND: return "Link to a shared object failed to resolve";
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case CUDA_ERROR_SHARED_OBJECT_INIT_FAILED: return "Shared object initialization failed";
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case CUDA_ERROR_INVALID_HANDLE: return "Invalid handle";
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case CUDA_ERROR_NOT_FOUND: return "Not found";
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case CUDA_ERROR_NOT_READY: return "CUDA not ready";
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case CUDA_ERROR_LAUNCH_FAILED: return "Launch failed";
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case CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES: return "Launch exceeded resources";
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case CUDA_ERROR_LAUNCH_TIMEOUT: return "Launch exceeded timeout";
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case CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING: return "Launch with incompatible texturing";
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case CUDA_ERROR_UNKNOWN: return "Unknown error";
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default: return "Unknown CUDA error value";
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}
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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, "http://wiki.blender.org/index.php/Doc:2.6/Manual/Render/Cycles/GPU_Rendering\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", cuda_error_string(result), #stmt); \
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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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}
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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(), cuda_error_string(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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void cuda_push_context()
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{
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cuda_assert(cuCtxSetCurrent(cuContext))
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}
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void cuda_pop_context()
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{
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cuda_assert(cuCtxSetCurrent(NULL));
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}
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CUDADevice(DeviceInfo& info, Stats &stats, bool background_) : Device(stats)
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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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/* intialize */
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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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CUresult result;
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if(background) {
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result = cuCtxCreate(&cuContext, 0, cuDevice);
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}
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else {
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result = cuGLCtxCreate(&cuContext, 0, cuDevice);
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if(result != CUDA_SUCCESS) {
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result = cuCtxCreate(&cuContext, 0, 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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cuda_pop_context();
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}
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~CUDADevice()
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{
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task_pool.stop();
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cuda_push_context();
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cuda_assert(cuCtxDetach(cuContext))
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}
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bool support_device(bool experimental)
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{
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if(!experimental) {
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int major, minor;
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cuDeviceComputeCapability(&major, &minor, cuDevId);
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if(major < 2) {
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cuda_error_message(string_printf("CUDA device supported only with compute capability 2.0 or up, found %d.%d.", major, minor));
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return false;
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}
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}
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return true;
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}
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string compile_kernel()
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{
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/* compute cubin name */
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int major, minor;
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cuDeviceComputeCapability(&major, &minor, cuDevId);
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/* attempt to use kernel provided with blender */
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string cubin = path_get(string_printf("lib/kernel_sm_%d%d.cubin", major, minor));
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if(path_exists(cubin))
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return cubin;
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/* not found, try to use locally compiled kernel */
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string kernel_path = path_get("kernel");
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string md5 = path_files_md5_hash(kernel_path);
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cubin = string_printf("cycles_kernel_sm%d%d_%s.cubin", major, minor, md5.c_str());
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cubin = path_user_get(path_join("cache", cubin));
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/* if exists already, use it */
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if(path_exists(cubin))
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return cubin;
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#ifdef _WIN32
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if(cuHavePrecompiledKernels()) {
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if(major < 2)
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cuda_error_message(string_printf("CUDA device requires compute capability 2.0 or up, found %d.%d. Your GPU is not supported.", major, minor));
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else
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cuda_error_message(string_printf("CUDA binary kernel for this graphics card compute capability (%d.%d) not found.", major, minor));
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return "";
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}
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#endif
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/* if not, find CUDA compiler */
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string nvcc = cuCompilerPath();
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if(nvcc == "") {
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cuda_error_message("CUDA nvcc compiler not found. Install CUDA toolkit in default location.");
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return "";
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}
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int cuda_version = cuCompilerVersion();
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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 "";
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}
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if(cuda_version != 50)
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printf("CUDA version %d.%d detected, build may succeed but only CUDA 5.0 is officially supported.\n", cuda_version/10, cuda_version%10);
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/* compile */
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string kernel = path_join(kernel_path, "kernel.cu");
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string include = kernel_path;
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const int machine = system_cpu_bits();
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string arch_flags;
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/* build flags depending on CUDA version and arch */
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if(cuda_version < 50) {
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/* CUDA 4.x */
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if(major == 1) {
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/* sm_1x */
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arch_flags = "--maxrregcount=24 --opencc-options -OPT:Olimit=0";
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}
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else if(major == 2) {
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/* sm_2x */
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arch_flags = "--maxrregcount=24";
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}
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else {
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/* sm_3x */
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arch_flags = "--maxrregcount=32";
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}
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}
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else {
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/* CUDA 5.x */
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if(major == 1) {
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/* sm_1x */
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arch_flags = "--maxrregcount=24 --opencc-options -OPT:Olimit=0 --use_fast_math";
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}
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else if(major == 2) {
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/* sm_2x */
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arch_flags = "--maxrregcount=32 --use_fast_math";
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}
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else {
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/* sm_3x */
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arch_flags = "--maxrregcount=32 --use_fast_math";
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}
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}
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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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string command = string_printf("\"%s\" -arch=sm_%d%d -m%d --cubin \"%s\" "
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"-o \"%s\" --ptxas-options=\"-v\" %s -I\"%s\" -DNVCC -D__KERNEL_CUDA_VERSION__=%d",
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nvcc.c_str(), major, minor, machine, kernel.c_str(), cubin.c_str(), arch_flags.c_str(), include.c_str(), cuda_version);
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printf("%s\n", command.c_str());
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if(system(command.c_str()) == -1) {
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cuda_error_message("Failed to execute compilation command, see console for details.");
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return "";
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}
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/* verify if compilation succeeded */
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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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printf("Kernel compilation finished in %.2lfs.\n", time_dt() - starttime);
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return cubin;
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}
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bool load_kernels(bool experimental)
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{
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/* check if cuda init succeeded */
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if(cuContext == 0)
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return false;
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/* check if GPU is supported with current feature set */
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if(!support_device(experimental))
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return false;
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/* get kernel */
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string cubin = compile_kernel();
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if(cubin == "")
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return false;
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/* open module */
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cuda_push_context();
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CUresult result = cuModuleLoad(&cuModule, cubin.c_str());
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if(cuda_error_(result, "cuModuleLoad"))
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cuda_error_message(string_printf("Failed loading CUDA kernel %s.", cubin.c_str()));
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cuda_pop_context();
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return (result == CUDA_SUCCESS);
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}
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void mem_alloc(device_memory& mem, MemoryType type)
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{
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cuda_push_context();
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CUdeviceptr device_pointer;
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size_t size = mem.memory_size();
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cuda_assert(cuMemAlloc(&device_pointer, size))
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mem.device_pointer = (device_ptr)device_pointer;
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stats.mem_alloc(size);
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cuda_pop_context();
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}
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void mem_copy_to(device_memory& mem)
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{
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cuda_push_context();
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if(mem.device_pointer)
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cuda_assert(cuMemcpyHtoD(cuda_device_ptr(mem.device_pointer), (void*)mem.data_pointer, mem.memory_size()))
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cuda_pop_context();
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}
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void mem_copy_from(device_memory& mem, int y, int w, int h, int elem)
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{
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size_t offset = elem*y*w;
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size_t size = elem*w*h;
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cuda_push_context();
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if(mem.device_pointer) {
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cuda_assert(cuMemcpyDtoH((uchar*)mem.data_pointer + offset,
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(CUdeviceptr)((uchar*)mem.device_pointer + offset), size))
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}
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else {
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memset((char*)mem.data_pointer + offset, 0, size);
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}
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cuda_pop_context();
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}
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void mem_zero(device_memory& mem)
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{
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memset((void*)mem.data_pointer, 0, mem.memory_size());
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cuda_push_context();
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if(mem.device_pointer)
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cuda_assert(cuMemsetD8(cuda_device_ptr(mem.device_pointer), 0, mem.memory_size()))
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cuda_pop_context();
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}
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void mem_free(device_memory& mem)
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{
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if(mem.device_pointer) {
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cuda_push_context();
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cuda_assert(cuMemFree(cuda_device_ptr(mem.device_pointer)))
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cuda_pop_context();
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mem.device_pointer = 0;
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stats.mem_free(mem.memory_size());
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}
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}
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void const_copy_to(const char *name, void *host, size_t size)
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{
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CUdeviceptr mem;
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size_t bytes;
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cuda_push_context();
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cuda_assert(cuModuleGetGlobal(&mem, &bytes, cuModule, name))
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//assert(bytes == size);
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cuda_assert(cuMemcpyHtoD(mem, host, size))
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cuda_pop_context();
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}
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void tex_alloc(const char *name, device_memory& mem, bool interpolation, bool periodic)
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{
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/* determine format */
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CUarray_format_enum format;
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size_t dsize = datatype_size(mem.data_type);
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size_t size = mem.memory_size();
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switch(mem.data_type) {
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case TYPE_UCHAR: format = CU_AD_FORMAT_UNSIGNED_INT8; break;
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case TYPE_UINT: format = CU_AD_FORMAT_UNSIGNED_INT32; break;
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case TYPE_INT: format = CU_AD_FORMAT_SIGNED_INT32; break;
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case TYPE_FLOAT: format = CU_AD_FORMAT_FLOAT; break;
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default: assert(0); return;
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}
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CUtexref texref = NULL;
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cuda_push_context();
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cuda_assert(cuModuleGetTexRef(&texref, cuModule, name))
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if(!texref) {
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cuda_pop_context();
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return;
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}
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if(interpolation) {
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CUarray handle = NULL;
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CUDA_ARRAY_DESCRIPTOR desc;
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desc.Width = mem.data_width;
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desc.Height = mem.data_height;
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desc.Format = format;
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desc.NumChannels = mem.data_elements;
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cuda_assert(cuArrayCreate(&handle, &desc))
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if(!handle) {
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cuda_pop_context();
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return;
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}
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if(mem.data_height > 1) {
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CUDA_MEMCPY2D param;
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memset(¶m, 0, sizeof(param));
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param.dstMemoryType = CU_MEMORYTYPE_ARRAY;
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param.dstArray = handle;
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param.srcMemoryType = CU_MEMORYTYPE_HOST;
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param.srcHost = (void*)mem.data_pointer;
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param.srcPitch = mem.data_width*dsize*mem.data_elements;
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param.WidthInBytes = param.srcPitch;
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param.Height = mem.data_height;
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cuda_assert(cuMemcpy2D(¶m))
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}
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else
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cuda_assert(cuMemcpyHtoA(handle, 0, (void*)mem.data_pointer, size))
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cuda_assert(cuTexRefSetArray(texref, handle, CU_TRSA_OVERRIDE_FORMAT))
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cuda_assert(cuTexRefSetFilterMode(texref, CU_TR_FILTER_MODE_LINEAR))
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cuda_assert(cuTexRefSetFlags(texref, CU_TRSF_NORMALIZED_COORDINATES))
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|
|
mem.device_pointer = (device_ptr)handle;
|
|
|
|
stats.mem_alloc(size);
|
|
}
|
|
else {
|
|
cuda_pop_context();
|
|
|
|
mem_alloc(mem, MEM_READ_ONLY);
|
|
mem_copy_to(mem);
|
|
|
|
cuda_push_context();
|
|
|
|
cuda_assert(cuTexRefSetAddress(NULL, texref, cuda_device_ptr(mem.device_pointer), size))
|
|
cuda_assert(cuTexRefSetFilterMode(texref, CU_TR_FILTER_MODE_POINT))
|
|
cuda_assert(cuTexRefSetFlags(texref, CU_TRSF_READ_AS_INTEGER))
|
|
}
|
|
|
|
if(periodic) {
|
|
cuda_assert(cuTexRefSetAddressMode(texref, 0, CU_TR_ADDRESS_MODE_WRAP))
|
|
cuda_assert(cuTexRefSetAddressMode(texref, 1, CU_TR_ADDRESS_MODE_WRAP))
|
|
}
|
|
else {
|
|
cuda_assert(cuTexRefSetAddressMode(texref, 0, CU_TR_ADDRESS_MODE_CLAMP))
|
|
cuda_assert(cuTexRefSetAddressMode(texref, 1, CU_TR_ADDRESS_MODE_CLAMP))
|
|
}
|
|
cuda_assert(cuTexRefSetFormat(texref, format, mem.data_elements))
|
|
|
|
cuda_pop_context();
|
|
|
|
tex_interp_map[mem.device_pointer] = interpolation;
|
|
}
|
|
|
|
void tex_free(device_memory& mem)
|
|
{
|
|
if(mem.device_pointer) {
|
|
if(tex_interp_map[mem.device_pointer]) {
|
|
cuda_push_context();
|
|
cuArrayDestroy((CUarray)mem.device_pointer);
|
|
cuda_pop_context();
|
|
|
|
tex_interp_map.erase(tex_interp_map.find(mem.device_pointer));
|
|
mem.device_pointer = 0;
|
|
|
|
stats.mem_free(mem.memory_size());
|
|
}
|
|
else {
|
|
tex_interp_map.erase(tex_interp_map.find(mem.device_pointer));
|
|
mem_free(mem);
|
|
}
|
|
}
|
|
}
|
|
|
|
void path_trace(RenderTile& rtile, int sample, bool progressive)
|
|
{
|
|
if(have_error())
|
|
return;
|
|
|
|
cuda_push_context();
|
|
|
|
CUfunction cuPathTrace;
|
|
CUdeviceptr d_buffer = cuda_device_ptr(rtile.buffer);
|
|
CUdeviceptr d_rng_state = cuda_device_ptr(rtile.rng_state);
|
|
|
|
/* get kernel function */
|
|
if(progressive)
|
|
cuda_assert(cuModuleGetFunction(&cuPathTrace, cuModule, "kernel_cuda_path_trace_progressive"))
|
|
else
|
|
cuda_assert(cuModuleGetFunction(&cuPathTrace, cuModule, "kernel_cuda_path_trace_non_progressive"))
|
|
|
|
/* pass in parameters */
|
|
int offset = 0;
|
|
|
|
cuda_assert(cuParamSetv(cuPathTrace, offset, &d_buffer, sizeof(d_buffer)))
|
|
offset += sizeof(d_buffer);
|
|
|
|
cuda_assert(cuParamSetv(cuPathTrace, offset, &d_rng_state, sizeof(d_rng_state)))
|
|
offset += sizeof(d_rng_state);
|
|
|
|
offset = align_up(offset, __alignof(sample));
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, sample))
|
|
offset += sizeof(sample);
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, rtile.x))
|
|
offset += sizeof(rtile.x);
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, rtile.y))
|
|
offset += sizeof(rtile.y);
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, rtile.w))
|
|
offset += sizeof(rtile.w);
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, rtile.h))
|
|
offset += sizeof(rtile.h);
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, rtile.offset))
|
|
offset += sizeof(rtile.offset);
|
|
|
|
cuda_assert(cuParamSeti(cuPathTrace, offset, rtile.stride))
|
|
offset += sizeof(rtile.stride);
|
|
|
|
cuda_assert(cuParamSetSize(cuPathTrace, offset))
|
|
|
|
/* launch kernel: todo find optimal size, cache config for fermi */
|
|
int xthreads = 16;
|
|
int ythreads = 16;
|
|
int xblocks = (rtile.w + xthreads - 1)/xthreads;
|
|
int yblocks = (rtile.h + ythreads - 1)/ythreads;
|
|
|
|
cuda_assert(cuFuncSetCacheConfig(cuPathTrace, CU_FUNC_CACHE_PREFER_L1))
|
|
cuda_assert(cuFuncSetBlockShape(cuPathTrace, xthreads, ythreads, 1))
|
|
cuda_assert(cuLaunchGrid(cuPathTrace, xblocks, yblocks))
|
|
|
|
cuda_assert(cuCtxSynchronize())
|
|
|
|
cuda_pop_context();
|
|
}
|
|
|
|
void tonemap(DeviceTask& task, device_ptr buffer, device_ptr rgba)
|
|
{
|
|
if(have_error())
|
|
return;
|
|
|
|
cuda_push_context();
|
|
|
|
CUfunction cuFilmConvert;
|
|
CUdeviceptr d_rgba = map_pixels(rgba);
|
|
CUdeviceptr d_buffer = cuda_device_ptr(buffer);
|
|
|
|
/* get kernel function */
|
|
cuda_assert(cuModuleGetFunction(&cuFilmConvert, cuModule, "kernel_cuda_tonemap"))
|
|
|
|
/* pass in parameters */
|
|
int offset = 0;
|
|
|
|
cuda_assert(cuParamSetv(cuFilmConvert, offset, &d_rgba, sizeof(d_rgba)))
|
|
offset += sizeof(d_rgba);
|
|
|
|
cuda_assert(cuParamSetv(cuFilmConvert, offset, &d_buffer, sizeof(d_buffer)))
|
|
offset += sizeof(d_buffer);
|
|
|
|
int sample = task.sample;
|
|
offset = align_up(offset, __alignof(sample));
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.sample))
|
|
offset += sizeof(task.sample);
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.x))
|
|
offset += sizeof(task.x);
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.y))
|
|
offset += sizeof(task.y);
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.w))
|
|
offset += sizeof(task.w);
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.h))
|
|
offset += sizeof(task.h);
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.offset))
|
|
offset += sizeof(task.offset);
|
|
|
|
cuda_assert(cuParamSeti(cuFilmConvert, offset, task.stride))
|
|
offset += sizeof(task.stride);
|
|
|
|
cuda_assert(cuParamSetSize(cuFilmConvert, offset))
|
|
|
|
/* launch kernel: todo find optimal size, cache config for fermi */
|
|
int xthreads = 16;
|
|
int ythreads = 16;
|
|
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(cuFuncSetBlockShape(cuFilmConvert, xthreads, ythreads, 1))
|
|
cuda_assert(cuLaunchGrid(cuFilmConvert, xblocks, yblocks))
|
|
|
|
unmap_pixels(task.rgba);
|
|
|
|
cuda_pop_context();
|
|
}
|
|
|
|
void shader(DeviceTask& task)
|
|
{
|
|
if(have_error())
|
|
return;
|
|
|
|
cuda_push_context();
|
|
|
|
CUfunction cuDisplace;
|
|
CUdeviceptr d_input = cuda_device_ptr(task.shader_input);
|
|
CUdeviceptr d_output = cuda_device_ptr(task.shader_output);
|
|
|
|
/* get kernel function */
|
|
cuda_assert(cuModuleGetFunction(&cuDisplace, cuModule, "kernel_cuda_shader"))
|
|
|
|
/* pass in parameters */
|
|
int offset = 0;
|
|
|
|
cuda_assert(cuParamSetv(cuDisplace, offset, &d_input, sizeof(d_input)))
|
|
offset += sizeof(d_input);
|
|
|
|
cuda_assert(cuParamSetv(cuDisplace, offset, &d_output, sizeof(d_output)))
|
|
offset += sizeof(d_output);
|
|
|
|
int shader_eval_type = task.shader_eval_type;
|
|
offset = align_up(offset, __alignof(shader_eval_type));
|
|
|
|
cuda_assert(cuParamSeti(cuDisplace, offset, task.shader_eval_type))
|
|
offset += sizeof(task.shader_eval_type);
|
|
|
|
cuda_assert(cuParamSeti(cuDisplace, offset, task.shader_x))
|
|
offset += sizeof(task.shader_x);
|
|
|
|
cuda_assert(cuParamSetSize(cuDisplace, offset))
|
|
|
|
/* launch kernel: todo find optimal size, cache config for fermi */
|
|
int xthreads = 16;
|
|
int xblocks = (task.shader_w + xthreads - 1)/xthreads;
|
|
|
|
cuda_assert(cuFuncSetCacheConfig(cuDisplace, CU_FUNC_CACHE_PREFER_L1))
|
|
cuda_assert(cuFuncSetBlockShape(cuDisplace, xthreads, 1, 1))
|
|
cuda_assert(cuLaunchGrid(cuDisplace, xblocks, 1))
|
|
|
|
cuda_pop_context();
|
|
}
|
|
|
|
CUdeviceptr map_pixels(device_ptr mem)
|
|
{
|
|
if(!background) {
|
|
PixelMem pmem = pixel_mem_map[mem];
|
|
CUdeviceptr buffer;
|
|
|
|
size_t bytes;
|
|
cuda_assert(cuGraphicsMapResources(1, &pmem.cuPBOresource, 0))
|
|
cuda_assert(cuGraphicsResourceGetMappedPointer(&buffer, &bytes, pmem.cuPBOresource))
|
|
|
|
return buffer;
|
|
}
|
|
|
|
return cuda_device_ptr(mem);
|
|
}
|
|
|
|
void unmap_pixels(device_ptr mem)
|
|
{
|
|
if(!background) {
|
|
PixelMem pmem = pixel_mem_map[mem];
|
|
|
|
cuda_assert(cuGraphicsUnmapResources(1, &pmem.cuPBOresource, 0))
|
|
}
|
|
}
|
|
|
|
void pixels_alloc(device_memory& mem)
|
|
{
|
|
if(!background) {
|
|
PixelMem pmem;
|
|
|
|
pmem.w = mem.data_width;
|
|
pmem.h = mem.data_height;
|
|
|
|
cuda_push_context();
|
|
|
|
glGenBuffers(1, &pmem.cuPBO);
|
|
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, pmem.cuPBO);
|
|
glBufferData(GL_PIXEL_UNPACK_BUFFER, pmem.w*pmem.h*sizeof(GLfloat)*3, NULL, GL_DYNAMIC_DRAW);
|
|
|
|
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
|
|
|
|
glGenTextures(1, &pmem.cuTexId);
|
|
glBindTexture(GL_TEXTURE_2D, pmem.cuTexId);
|
|
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, pmem.w, pmem.h, 0, GL_RGBA, GL_UNSIGNED_BYTE, NULL);
|
|
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST);
|
|
glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST);
|
|
glBindTexture(GL_TEXTURE_2D, 0);
|
|
|
|
CUresult result = cuGraphicsGLRegisterBuffer(&pmem.cuPBOresource, pmem.cuPBO, CU_GRAPHICS_MAP_RESOURCE_FLAGS_NONE);
|
|
|
|
if(result == CUDA_SUCCESS) {
|
|
cuda_pop_context();
|
|
|
|
mem.device_pointer = pmem.cuTexId;
|
|
pixel_mem_map[mem.device_pointer] = pmem;
|
|
|
|
stats.mem_alloc(mem.memory_size());
|
|
|
|
return;
|
|
}
|
|
else {
|
|
/* failed to register buffer, fallback to no interop */
|
|
glDeleteBuffers(1, &pmem.cuPBO);
|
|
glDeleteTextures(1, &pmem.cuTexId);
|
|
|
|
cuda_pop_context();
|
|
|
|
background = true;
|
|
}
|
|
}
|
|
|
|
Device::pixels_alloc(mem);
|
|
}
|
|
|
|
void pixels_copy_from(device_memory& mem, int y, int w, int h)
|
|
{
|
|
if(!background) {
|
|
PixelMem pmem = pixel_mem_map[mem.device_pointer];
|
|
|
|
cuda_push_context();
|
|
|
|
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, pmem.cuPBO);
|
|
uchar *pixels = (uchar*)glMapBuffer(GL_PIXEL_UNPACK_BUFFER, GL_READ_ONLY);
|
|
size_t offset = sizeof(uchar)*4*y*w;
|
|
memcpy((uchar*)mem.data_pointer + offset, pixels + offset, sizeof(uchar)*4*w*h);
|
|
glUnmapBuffer(GL_PIXEL_UNPACK_BUFFER);
|
|
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
|
|
|
|
cuda_pop_context();
|
|
|
|
return;
|
|
}
|
|
|
|
Device::pixels_copy_from(mem, y, w, h);
|
|
}
|
|
|
|
void pixels_free(device_memory& mem)
|
|
{
|
|
if(mem.device_pointer) {
|
|
if(!background) {
|
|
PixelMem pmem = pixel_mem_map[mem.device_pointer];
|
|
|
|
cuda_push_context();
|
|
|
|
cuda_assert(cuGraphicsUnregisterResource(pmem.cuPBOresource))
|
|
glDeleteBuffers(1, &pmem.cuPBO);
|
|
glDeleteTextures(1, &pmem.cuTexId);
|
|
|
|
cuda_pop_context();
|
|
|
|
pixel_mem_map.erase(pixel_mem_map.find(mem.device_pointer));
|
|
mem.device_pointer = 0;
|
|
|
|
stats.mem_free(mem.memory_size());
|
|
|
|
return;
|
|
}
|
|
|
|
Device::pixels_free(mem);
|
|
}
|
|
}
|
|
|
|
void draw_pixels(device_memory& mem, int y, int w, int h, int dy, int width, int height, bool transparent)
|
|
{
|
|
if(!background) {
|
|
PixelMem pmem = pixel_mem_map[mem.device_pointer];
|
|
|
|
cuda_push_context();
|
|
|
|
/* for multi devices, this assumes the ineffecient method that we allocate
|
|
* all pixels on the device even though we only render to a subset */
|
|
size_t offset = sizeof(uint8_t)*4*y*w;
|
|
|
|
glBindBufferARB(GL_PIXEL_UNPACK_BUFFER_ARB, pmem.cuPBO);
|
|
glBindTexture(GL_TEXTURE_2D, pmem.cuTexId);
|
|
glTexSubImage2D(GL_TEXTURE_2D, 0, 0, 0, w, h, GL_RGBA, GL_UNSIGNED_BYTE, (void*)offset);
|
|
glBindBufferARB(GL_PIXEL_UNPACK_BUFFER_ARB, 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);
|
|
|
|
glPushMatrix();
|
|
glTranslatef(0.0f, (float)dy, 0.0f);
|
|
|
|
glBegin(GL_QUADS);
|
|
|
|
glTexCoord2f(0.0f, 0.0f);
|
|
glVertex2f(0.0f, 0.0f);
|
|
glTexCoord2f((float)w/(float)pmem.w, 0.0f);
|
|
glVertex2f((float)width, 0.0f);
|
|
glTexCoord2f((float)w/(float)pmem.w, (float)h/(float)pmem.h);
|
|
glVertex2f((float)width, (float)height);
|
|
glTexCoord2f(0.0f, (float)h/(float)pmem.h);
|
|
glVertex2f(0.0f, (float)height);
|
|
|
|
glEnd();
|
|
|
|
glPopMatrix();
|
|
|
|
if(transparent)
|
|
glDisable(GL_BLEND);
|
|
|
|
glBindTexture(GL_TEXTURE_2D, 0);
|
|
glDisable(GL_TEXTURE_2D);
|
|
|
|
cuda_pop_context();
|
|
|
|
return;
|
|
}
|
|
|
|
Device::draw_pixels(mem, y, w, h, dy, width, height, transparent);
|
|
}
|
|
|
|
void thread_run(DeviceTask *task)
|
|
{
|
|
if(task->type == DeviceTask::PATH_TRACE) {
|
|
RenderTile tile;
|
|
|
|
bool progressive = task->integrator_progressive;
|
|
|
|
/* keep rendering tiles until done */
|
|
while(task->acquire_tile(this, tile)) {
|
|
int start_sample = tile.start_sample;
|
|
int end_sample = tile.start_sample + tile.num_samples;
|
|
|
|
for(int sample = start_sample; sample < end_sample; sample++) {
|
|
if (task->get_cancel()) {
|
|
if(task->need_finish_queue == false)
|
|
break;
|
|
}
|
|
|
|
path_trace(tile, sample, progressive);
|
|
|
|
tile.sample = sample + 1;
|
|
|
|
task->update_progress(tile);
|
|
}
|
|
|
|
task->release_tile(tile);
|
|
}
|
|
}
|
|
else if(task->type == DeviceTask::SHADER) {
|
|
shader(*task);
|
|
|
|
cuda_push_context();
|
|
cuda_assert(cuCtxSynchronize())
|
|
cuda_pop_context();
|
|
}
|
|
}
|
|
|
|
class CUDADeviceTask : public DeviceTask {
|
|
public:
|
|
CUDADeviceTask(CUDADevice *device, DeviceTask& task)
|
|
: DeviceTask(task)
|
|
{
|
|
run = function_bind(&CUDADevice::thread_run, device, this);
|
|
}
|
|
};
|
|
|
|
void task_add(DeviceTask& task)
|
|
{
|
|
if(task.type == DeviceTask::TONEMAP) {
|
|
/* must be done in main thread due to opengl access */
|
|
tonemap(task, task.buffer, task.rgba);
|
|
|
|
cuda_push_context();
|
|
cuda_assert(cuCtxSynchronize())
|
|
cuda_pop_context();
|
|
}
|
|
else {
|
|
task_pool.push(new CUDADeviceTask(this, task));
|
|
}
|
|
}
|
|
|
|
void task_wait()
|
|
{
|
|
task_pool.wait_work();
|
|
}
|
|
|
|
void task_cancel()
|
|
{
|
|
task_pool.cancel();
|
|
}
|
|
};
|
|
|
|
Device *device_cuda_create(DeviceInfo& info, Stats &stats, bool background)
|
|
{
|
|
return new CUDADevice(info, stats, background);
|
|
}
|
|
|
|
void device_cuda_info(vector<DeviceInfo>& devices)
|
|
{
|
|
CUresult result;
|
|
int count = 0;
|
|
|
|
result = cuInit(0);
|
|
if(result != CUDA_SUCCESS) {
|
|
if(result != CUDA_ERROR_NO_DEVICE)
|
|
fprintf(stderr, "CUDA cuInit: %s\n", CUDADevice::cuda_error_string(result));
|
|
return;
|
|
}
|
|
|
|
result = cuDeviceGetCount(&count);
|
|
if(result != CUDA_SUCCESS) {
|
|
fprintf(stderr, "CUDA cuDeviceGetCount: %s\n", CUDADevice::cuda_error_string(result));
|
|
return;
|
|
}
|
|
|
|
vector<DeviceInfo> display_devices;
|
|
|
|
for(int num = 0; num < count; num++) {
|
|
char name[256];
|
|
int attr;
|
|
|
|
if(cuDeviceGetName(name, 256, num) != CUDA_SUCCESS)
|
|
continue;
|
|
|
|
DeviceInfo info;
|
|
|
|
info.type = DEVICE_CUDA;
|
|
info.description = string(name);
|
|
info.id = string_printf("CUDA_%d", num);
|
|
info.num = num;
|
|
|
|
int major, minor;
|
|
cuDeviceComputeCapability(&major, &minor, num);
|
|
info.advanced_shading = (major >= 2);
|
|
info.pack_images = false;
|
|
|
|
/* if device has a kernel timeout, assume it is used for display */
|
|
if(cuDeviceGetAttribute(&attr, CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT, num) == CUDA_SUCCESS && attr == 1) {
|
|
info.display_device = true;
|
|
display_devices.push_back(info);
|
|
}
|
|
else
|
|
devices.push_back(info);
|
|
}
|
|
|
|
if(!display_devices.empty())
|
|
devices.insert(devices.end(), display_devices.begin(), display_devices.end());
|
|
}
|
|
|
|
CCL_NAMESPACE_END
|
|
|