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Cycles: Improve denoising speed on GPUs with small tile sizes 

Previously, the NLM kernels would be launched once per offset with one thread per pixel.
However, with the smaller tile sizes that are now feasible, there wasn't enough work to fully occupy GPUs which results in a significant slowdown.

Therefore, the kernels are now launched in a single call that handles all offsets at once.
This has two downsides: Memory accesses to accumulating buffers are now atomic, and more importantly, the temporary memory now has to be allocated for every shift at once, increasing the required memory.
On the other hand, of course, the smaller tiles significantly reduce the size of the memory.

The main bottleneck right now is the construction of the transformation - there is nothing to be parallelized there, one thread per pixel is the maximum.
I tried to parallelize the SVD implementation by storing the matrix in shared memory and launching one block per pixel, but that wasn't really going anywhere.

To make the new code somewhat readable, the handling of rectangular regions was cleaned up a bit and commented, it should be easier to understand what's going on now.
Also, some variables have been renamed to make the difference between buffer width and stride more apparent, in addition to some general style cleanup.
This commit is contained in:
Lukas Stockner 2017-11-10 04:34:14 +01:00
parent df7b9fa2ee
commit fa3d50af95
18 changed files with 651 additions and 424 deletions
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21
intern/cycles/device/device_cpu.cpp
View File

@ -190,9 +190,9 @@ public:
KernelFunctions<void(*)(int, int, float*, float*, float*, float*, int*, int, int)> filter_nlm_update_output_kernel;
KernelFunctions<void(*)(float*, float*, int*, int)> filter_nlm_normalize_kernel;
KernelFunctions<void(*)(float*, int, int, int, float*, int*, int*, int, int, float)> filter_construct_transform_kernel;
KernelFunctions<void(*)(int, int, float*, float*, float*, int*, float*, float3*, int*, int*, int, int, int, int)> filter_nlm_construct_gramian_kernel;
KernelFunctions<void(*)(int, int, int, int, int, float*, int*, float*, float3*, int*, int)> filter_finalize_kernel;
KernelFunctions<void(*)(float*, int, int, int, float*, int*, int*, int, int, float)> filter_construct_transform_kernel;
KernelFunctions<void(*)(int, int, float*, float*, float*, int*, float*, float3*, int*, int*, int, int, int)> filter_nlm_construct_gramian_kernel;
KernelFunctions<void(*)(int, int, int, float*, int*, float*, float3*, int*, int)> filter_finalize_kernel;
KernelFunctions<void(*)(KernelGlobals *, ccl_constant KernelData*, ccl_global void*, int, ccl_global char*,
int, int, int, int, int, int, int, int, ccl_global int*, int,
@ -565,13 +565,13 @@ public:
(float*) color_variance_ptr,
difference,
local_rect,
task->buffer.w,
task->buffer.stride,
task->buffer.pass_stride,
1.0f,
task->nlm_k_2);
filter_nlm_blur_kernel()(difference, blurDifference, local_rect, task->buffer.w, 4);
filter_nlm_calc_weight_kernel()(blurDifference, difference, local_rect, task->buffer.w, 4);
filter_nlm_blur_kernel()(difference, blurDifference, local_rect, task->buffer.w, 4);
filter_nlm_blur_kernel()(difference, blurDifference, local_rect, task->buffer.stride, 4);
filter_nlm_calc_weight_kernel()(blurDifference, difference, local_rect, task->buffer.stride, 4);
filter_nlm_blur_kernel()(difference, blurDifference, local_rect, task->buffer.stride, 4);
filter_nlm_construct_gramian_kernel()(dx, dy,
blurDifference,
(float*) task->buffer.mem.device_pointer,
@ -580,9 +580,8 @@ public:
(float*) task->storage.XtWX.device_pointer,
(float3*) task->storage.XtWY.device_pointer,
local_rect,
&task->reconstruction_state.filter_rect.x,
task->buffer.w,
task->buffer.h,
&task->reconstruction_state.filter_window.x,
task->buffer.stride,
4,
task->buffer.pass_stride);
}
@ -591,8 +590,6 @@ public:
filter_finalize_kernel()(x,
y,
y*task->filter_area.z + x,
task->buffer.w,
task->buffer.h,
(float*) output_ptr,
(int*) task->storage.rank.device_pointer,
(float*) task->storage.XtWX.device_pointer,

230
intern/cycles/device/device_cuda.cpp
View File

@ -1087,6 +1087,19 @@ public:
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)
{
@ -1095,60 +1108,65 @@ public:
CUDAContextScope scope(this);
int4 rect = task->rect;
int w = align_up(rect.z-rect.x, 4);
int h = rect.w-rect.y;
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;
CUdeviceptr difference = task->nlm_state.temporary_1_ptr;
CUdeviceptr blurDifference = task->nlm_state.temporary_2_ptr;
CUdeviceptr weightAccum = task->nlm_state.temporary_3_ptr;
cuda_assert(cuMemsetD8(weightAccum, 0, sizeof(float)*w*h));
cuda_assert(cuMemsetD8(out_ptr, 0, sizeof(float)*w*h));
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMUpdateOutput, cuNLMNormalize;
cuda_assert(cuModuleGetFunction(&cuNLMCalcDifference, cuFilterModule, "kernel_cuda_filter_nlm_calc_difference"));
cuda_assert(cuModuleGetFunction(&cuNLMBlur, cuFilterModule, "kernel_cuda_filter_nlm_blur"));
cuda_assert(cuModuleGetFunction(&cuNLMCalcWeight, cuFilterModule, "kernel_cuda_filter_nlm_calc_weight"));
cuda_assert(cuModuleGetFunction(&cuNLMUpdateOutput, cuFilterModule, "kernel_cuda_filter_nlm_update_output"));
cuda_assert(cuModuleGetFunction(&cuNLMNormalize, cuFilterModule, "kernel_cuda_filter_nlm_normalize"));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcDifference, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMBlur, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcWeight, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMUpdateOutput, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMNormalize, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuNLMCalcDifference, rect.z-rect.x, rect.w-rect.y);
int dx, dy;
int4 local_rect;
int shift_stride = stride*h;
int num_shifts = (2*r+1)*(2*r+1);
int mem_size = sizeof(float)*shift_stride*2*num_shifts;
int channel_offset = 0;
void *calc_difference_args[] = {&dx, &dy, &guide_ptr, &variance_ptr, &difference, &local_rect, &w, &channel_offset, &a, &k_2};
void *blur_args[] = {&difference, &blurDifference, &local_rect, &w, &f};
void *calc_weight_args[] = {&blurDifference, &difference, &local_rect, &w, &f};
void *update_output_args[] = {&dx, &dy, &blurDifference, &image_ptr, &out_ptr, &weightAccum, &local_rect, &w, &f};
for(int i = 0; i < (2*r+1)*(2*r+1); i++) {
dy = i / (2*r+1) - r;
dx = i % (2*r+1) - r;
local_rect = make_int4(max(0, -dx), max(0, -dy), rect.z-rect.x - max(0, dx), rect.w-rect.y - max(0, dy));
CUdeviceptr temporary_mem;
cuda_assert(cuMemAlloc(&temporary_mem, mem_size));
CUdeviceptr difference = temporary_mem;
CUdeviceptr blurDifference = temporary_mem + sizeof(float)*shift_stride * num_shifts;
CUDA_LAUNCH_KERNEL(cuNLMCalcDifference, calc_difference_args);
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL(cuNLMCalcWeight, calc_weight_args);
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL(cuNLMUpdateOutput, update_output_args);
CUdeviceptr weightAccum = task->nlm_state.temporary_3_ptr;
cuda_assert(cuMemsetD8(weightAccum, 0, sizeof(float)*shift_stride));
cuda_assert(cuMemsetD8(out_ptr, 0, sizeof(float)*shift_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, &difference, &w, &h, &stride, &shift_stride, &r, &channel_offset, &a, &k_2};
void *blur_args[] = {&difference, &blurDifference, &w, &h, &stride, &shift_stride, &r, &f};
void *calc_weight_args[] = {&blurDifference, &difference, &w, &h, &stride, &shift_stride, &r, &f};
void *update_output_args[] = {&blurDifference, &image_ptr, &out_ptr, &weightAccum, &w, &h, &stride, &shift_stride, &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);
}
local_rect = make_int4(0, 0, rect.z-rect.x, rect.w-rect.y);
void *normalize_args[] = {&out_ptr, &weightAccum, &local_rect, &w};
CUDA_LAUNCH_KERNEL(cuNLMNormalize, normalize_args);
cuda_assert(cuCtxSynchronize());
cuMemFree(temporary_mem);
{
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();
}
@ -1194,91 +1212,81 @@ public:
mem_zero(task->storage.XtWX);
mem_zero(task->storage.XtWY);
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMConstructGramian, cuFinalize;
cuda_assert(cuModuleGetFunction(&cuNLMCalcDifference, cuFilterModule, "kernel_cuda_filter_nlm_calc_difference"));
cuda_assert(cuModuleGetFunction(&cuNLMBlur, cuFilterModule, "kernel_cuda_filter_nlm_blur"));
cuda_assert(cuModuleGetFunction(&cuNLMCalcWeight, cuFilterModule, "kernel_cuda_filter_nlm_calc_weight"));
cuda_assert(cuModuleGetFunction(&cuNLMConstructGramian, cuFilterModule, "kernel_cuda_filter_nlm_construct_gramian"));
cuda_assert(cuModuleGetFunction(&cuFinalize, cuFilterModule, "kernel_cuda_filter_finalize"));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcDifference, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMBlur, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMCalcWeight, CU_FUNC_CACHE_PREFER_L1));
cuda_assert(cuFuncSetCacheConfig(cuNLMConstructGramian, CU_FUNC_CACHE_PREFER_SHARED));
cuda_assert(cuFuncSetCacheConfig(cuFinalize, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuNLMCalcDifference,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
CUdeviceptr difference = task->reconstruction_state.temporary_1_ptr;
CUdeviceptr blurDifference = task->reconstruction_state.temporary_2_ptr;
int r = task->radius;
int f = 4;
float a = 1.0f;
for(int i = 0; i < (2*r+1)*(2*r+1); i++) {
int dy = i / (2*r+1) - r;
int dx = i % (2*r+1) - r;
float k_2 = task->nlm_k_2;
int local_rect[4] = {max(0, -dx), max(0, -dy),
task->reconstruction_state.source_w - max(0, dx),
task->reconstruction_state.source_h - max(0, dy)};
int w = task->reconstruction_state.source_w;
int h = task->reconstruction_state.source_h;
int stride = task->buffer.stride;
void *calc_difference_args[] = {&dx, &dy,
&color_ptr,
&color_variance_ptr,
&difference,
&local_rect,
&task->buffer.w,
&task->buffer.pass_stride,
&a,
&task->nlm_k_2};
CUDA_LAUNCH_KERNEL(cuNLMCalcDifference, calc_difference_args);
int shift_stride = stride*h;
int num_shifts = (2*r+1)*(2*r+1);
int mem_size = sizeof(float)*shift_stride*num_shifts;
void *blur_args[] = {&difference,
&blurDifference,
&local_rect,
&task->buffer.w,
&f};
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
CUdeviceptr temporary_mem;
cuda_assert(cuMemAlloc(&temporary_mem, 2*mem_size));
CUdeviceptr difference = temporary_mem;
CUdeviceptr blurDifference = temporary_mem + mem_size;
void *calc_weight_args[] = {&blurDifference,
&difference,
&local_rect,
&task->buffer.w,
&f};
CUDA_LAUNCH_KERNEL(cuNLMCalcWeight, calc_weight_args);
{
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"));
/* Reuse previous arguments. */
CUDA_LAUNCH_KERNEL(cuNLMBlur, blur_args);
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));
void *construct_gramian_args[] = {&dx, &dy,
&blurDifference,
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, &difference, &w, &h, &stride, &shift_stride, &r, &task->buffer.pass_stride, &a, &k_2};
void *blur_args[] = {&difference, &blurDifference, &w, &h, &stride, &shift_stride, &r, &f};
void *calc_weight_args[] = {&blurDifference, &difference, &w, &h, &stride, &shift_stride, &r, &f};
void *construct_gramian_args[] = {&blurDifference,
&task->buffer.mem.device_pointer,
&task->storage.transform.device_pointer,
&task->storage.rank.device_pointer,
&task->storage.XtWX.device_pointer,
&task->storage.XtWY.device_pointer,
&local_rect,
&task->reconstruction_state.filter_rect,
&task->buffer.w,
&task->buffer.h,
&task->reconstruction_state.filter_window,
&w, &h, &stride,
&shift_stride, &r,
&f,
&task->buffer.pass_stride};
CUDA_LAUNCH_KERNEL(cuNLMConstructGramian, construct_gramian_args);
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);
}
cuMemFree(temporary_mem);
{
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);
}
void *finalize_args[] = {&task->buffer.w,
&task->buffer.h,
&output_ptr,
&task->storage.rank.device_pointer,
&task->storage.XtWX.device_pointer,
&task->storage.XtWY.device_pointer,
&task->filter_area,
&task->reconstruction_state.buffer_params.x,
&task->render_buffer.samples};
CUDA_LAUNCH_KERNEL(cuFinalize, finalize_args);
cuda_assert(cuCtxSynchronize());
return !have_error();

24
intern/cycles/device/device_denoising.cpp
View File

@ -57,10 +57,9 @@ void DenoisingTask::init_from_devicetask(const DeviceTask &task)
render_buffer.denoising_clean_offset = task.pass_denoising_clean;
/* Expand filter_area by radius pixels and clamp the result to the extent of the neighboring tiles */
rect = make_int4(max(tiles->x[0], filter_area.x - radius),
max(tiles->y[0], filter_area.y - radius),
min(tiles->x[3], filter_area.x + filter_area.z + radius),
min(tiles->y[3], filter_area.y + filter_area.w + radius));
rect = rect_from_shape(filter_area.x, filter_area.y, filter_area.z, filter_area.w);
rect = rect_expand(rect, radius);
rect = rect_clip(rect, make_int4(tiles->x[0], tiles->y[0], tiles->x[3], tiles->y[3]));
}
void DenoisingTask::tiles_from_rendertiles(RenderTile *rtiles)
@ -93,9 +92,10 @@ bool DenoisingTask::run_denoising()
{
/* Allocate denoising buffer. */
buffer.passes = 14;
buffer.w = align_up(rect.z - rect.x, 4);
buffer.width = rect.z - rect.x;
buffer.stride = align_up(buffer.width, 4);
buffer.h = rect.w - rect.y;
buffer.pass_stride = align_up(buffer.w * buffer.h, divide_up(device->mem_address_alignment(), sizeof(float)));
buffer.pass_stride = align_up(buffer.stride * buffer.h, divide_up(device->mem_address_alignment(), sizeof(float)));
buffer.mem.alloc_to_device(buffer.pass_stride * buffer.passes, false);
device_ptr null_ptr = (device_ptr) 0;
@ -203,15 +203,17 @@ bool DenoisingTask::run_denoising()
functions.construct_transform();
storage.temporary_1.alloc_to_device(buffer.w*buffer.h, false);
storage.temporary_2.alloc_to_device(buffer.w*buffer.h, false);
reconstruction_state.temporary_1_ptr = storage.temporary_1.device_pointer;
reconstruction_state.temporary_2_ptr = storage.temporary_2.device_pointer;
device_only_memory<float> temporary_1(device, "Denoising NLM temporary 1");
device_only_memory<float> temporary_2(device, "Denoising NLM temporary 2");
temporary_1.alloc_to_device(buffer.pass_stride, false);
temporary_2.alloc_to_device(buffer.pass_stride, false);
reconstruction_state.temporary_1_ptr = temporary_1.device_pointer;
reconstruction_state.temporary_2_ptr = temporary_2.device_pointer;
storage.XtWX.alloc_to_device(storage.w*storage.h*XTWX_SIZE, false);
storage.XtWY.alloc_to_device(storage.w*storage.h*XTWY_SIZE, false);
reconstruction_state.filter_rect = make_int4(filter_area.x-rect.x, filter_area.y-rect.y, storage.w, storage.h);
reconstruction_state.filter_window = rect_from_shape(filter_area.x-rect.x, filter_area.y-rect.y, storage.w, storage.h);
int tile_coordinate_offset = filter_area.y*render_buffer.stride + filter_area.x;
reconstruction_state.buffer_params = make_int4(render_buffer.offset + tile_coordinate_offset,
render_buffer.stride,

5
intern/cycles/device/device_denoising.h
View File

@ -94,7 +94,7 @@ public:
device_ptr temporary_1_ptr; /* There two images are used as temporary storage. */
device_ptr temporary_2_ptr;
int4 filter_rect;
int4 filter_window;
int4 buffer_params;
int source_w;
@ -148,8 +148,9 @@ public:
struct DenoiseBuffers {
int pass_stride;
int passes;
int w;
int stride;
int h;
int width;
device_only_memory<float> mem;
DenoiseBuffers(Device *device)

4
intern/cycles/device/opencl/opencl.h
View File

@ -353,7 +353,9 @@ public:
void tex_free(device_memory& mem);
size_t global_size_round_up(int group_size, int global_size);
void enqueue_kernel(cl_kernel kernel, size_t w, size_t h, size_t max_workgroup_size = -1);
void enqueue_kernel(cl_kernel kernel, size_t w, size_t h,
bool x_workgroups = false,
size_t max_workgroup_size = -1);
void set_kernel_arg_mem(cl_kernel kernel, cl_uint *narg, const char *name);
void set_kernel_arg_buffers(cl_kernel kernel, cl_uint *narg);

213
intern/cycles/device/opencl/opencl_base.cpp
View File

@ -560,7 +560,7 @@ size_t OpenCLDeviceBase::global_size_round_up(int group_size, int global_size)
return global_size + ((r == 0)? 0: group_size - r);
}
void OpenCLDeviceBase::enqueue_kernel(cl_kernel kernel, size_t w, size_t h, size_t max_workgroup_size)
void OpenCLDeviceBase::enqueue_kernel(cl_kernel kernel, size_t w, size_t h, bool x_workgroups, size_t max_workgroup_size)
{
size_t workgroup_size, max_work_items[3];
@ -574,8 +574,15 @@ void OpenCLDeviceBase::enqueue_kernel(cl_kernel kernel, size_t w, size_t h, size
}
/* Try to divide evenly over 2 dimensions. */
size_t sqrt_workgroup_size = max((size_t)sqrt((double)workgroup_size), 1);
size_t local_size[2] = {sqrt_workgroup_size, sqrt_workgroup_size};
size_t local_size[2];
if(x_workgroups) {
local_size[0] = workgroup_size;
local_size[1] = 1;
}
else {
size_t sqrt_workgroup_size = max((size_t)sqrt((double)workgroup_size), 1);
local_size[0] = local_size[1] = sqrt_workgroup_size;
}
/* Some implementations have max size 1 on 2nd dimension. */
if(local_size[1] > max_work_items[1]) {
@ -731,17 +738,25 @@ bool OpenCLDeviceBase::denoising_non_local_means(device_ptr image_ptr,
device_ptr out_ptr,
DenoisingTask *task)
{
int4 rect = task->rect;
int w = rect.z-rect.x;
int h = rect.w-rect.y;
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;
cl_mem difference = CL_MEM_PTR(task->nlm_state.temporary_1_ptr);
cl_mem blurDifference = CL_MEM_PTR(task->nlm_state.temporary_2_ptr);
cl_mem weightAccum = CL_MEM_PTR(task->nlm_state.temporary_3_ptr);
int shift_stride = stride*h;
int num_shifts = (2*r+1)*(2*r+1);
int mem_size = sizeof(float)*shift_stride*num_shifts;
cl_mem weightAccum = CL_MEM_PTR(task->nlm_state.temporary_3_ptr);
cl_mem difference = clCreateBuffer(cxContext, CL_MEM_READ_WRITE, mem_size, NULL, &ciErr);
opencl_assert_err(ciErr, "clCreateBuffer denoising_non_local_means");
cl_mem blurDifference = clCreateBuffer(cxContext, CL_MEM_READ_WRITE, mem_size, NULL, &ciErr);
opencl_assert_err(ciErr, "clCreateBuffer denoising_non_local_means");
cl_mem image_mem = CL_MEM_PTR(image_ptr);
cl_mem guide_mem = CL_MEM_PTR(guide_ptr);
@ -757,31 +772,45 @@ bool OpenCLDeviceBase::denoising_non_local_means(device_ptr image_ptr,
cl_kernel ckNLMUpdateOutput = denoising_program(ustring("filter_nlm_update_output"));
cl_kernel ckNLMNormalize = denoising_program(ustring("filter_nlm_normalize"));
for(int i = 0; i < (2*r+1)*(2*r+1); i++) {
int dy = i / (2*r+1) - r;
int dx = i % (2*r+1) - r;
int4 local_rect = make_int4(max(0, -dx), max(0, -dy), rect.z-rect.x - max(0, dx), rect.w-rect.y - max(0, dy));
kernel_set_args(ckNLMCalcDifference, 0,
dx, dy, guide_mem, variance_mem,
difference, local_rect, w, 0, a, k_2);
kernel_set_args(ckNLMBlur, 0,
difference, blurDifference, local_rect, w, f);
kernel_set_args(ckNLMCalcWeight, 0,
blurDifference, difference, local_rect, w, f);
kernel_set_args(ckNLMUpdateOutput, 0,
dx, dy, blurDifference, image_mem,
out_mem, weightAccum, local_rect, w, f);
kernel_set_args(ckNLMCalcDifference, 0,
guide_mem,
variance_mem,
difference,
w, h, stride,
shift_stride,
r, 0, a, k_2);
kernel_set_args(ckNLMBlur, 0,
difference,
blurDifference,
w, h, stride,
shift_stride,
r, f);
kernel_set_args(ckNLMCalcWeight, 0,
blurDifference,
difference,
w, h, stride,
shift_stride,
r, f);
kernel_set_args(ckNLMUpdateOutput, 0,
blurDifference,
image_mem,
out_mem,
weightAccum,
w, h, stride,
shift_stride,
r, f);
enqueue_kernel(ckNLMCalcDifference, w, h);
enqueue_kernel(ckNLMBlur, w, h);
enqueue_kernel(ckNLMCalcWeight, w, h);
enqueue_kernel(ckNLMBlur, w, h);
enqueue_kernel(ckNLMUpdateOutput, w, h);
}
enqueue_kernel(ckNLMCalcDifference, w*h, num_shifts, true);
enqueue_kernel(ckNLMBlur, w*h, num_shifts, true);
enqueue_kernel(ckNLMCalcWeight, w*h, num_shifts, true);
enqueue_kernel(ckNLMBlur, w*h, num_shifts, true);
enqueue_kernel(ckNLMUpdateOutput, w*h, num_shifts, true);
opencl_assert(clReleaseMemObject(difference));
opencl_assert(clReleaseMemObject(blurDifference));
int4 local_rect = make_int4(0, 0, w, h);
kernel_set_args(ckNLMNormalize, 0,
out_mem, weightAccum, local_rect, w);
out_mem, weightAccum, w, h, stride);
enqueue_kernel(ckNLMNormalize, w, h);
return true;
@ -837,81 +866,63 @@ bool OpenCLDeviceBase::denoising_reconstruct(device_ptr color_ptr,
cl_kernel ckNLMConstructGramian = denoising_program(ustring("filter_nlm_construct_gramian"));
cl_kernel ckFinalize = denoising_program(ustring("filter_finalize"));
cl_mem difference = CL_MEM_PTR(task->reconstruction_state.temporary_1_ptr);
cl_mem blurDifference = CL_MEM_PTR(task->reconstruction_state.temporary_2_ptr);
int w = task->reconstruction_state.source_w;
int h = task->reconstruction_state.source_h;
int stride = task->buffer.stride;
int r = task->radius;
int f = 4;
float a = 1.0f;
for(int i = 0; i < (2*r+1)*(2*r+1); i++) {
int dy = i / (2*r+1) - r;
int dx = i % (2*r+1) - r;
int shift_stride = stride*h;
int num_shifts = (2*task->radius + 1)*(2*task->radius + 1);
int mem_size = sizeof(float)*shift_stride*num_shifts;
int local_rect[4] = {max(0, -dx), max(0, -dy),
task->reconstruction_state.source_w - max(0, dx),
task->reconstruction_state.source_h - max(0, dy)};
cl_mem difference = clCreateBuffer(cxContext, CL_MEM_READ_WRITE, mem_size, NULL, &ciErr);
opencl_assert_err(ciErr, "clCreateBuffer denoising_reconstruct");
cl_mem blurDifference = clCreateBuffer(cxContext, CL_MEM_READ_WRITE, mem_size, NULL, &ciErr);
opencl_assert_err(ciErr, "clCreateBuffer denoising_reconstruct");
kernel_set_args(ckNLMCalcDifference, 0,
dx, dy,
color_mem,
color_variance_mem,
difference,
local_rect,
task->buffer.w,
task->buffer.pass_stride,
a, task->nlm_k_2);
enqueue_kernel(ckNLMCalcDifference,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
kernel_set_args(ckNLMCalcDifference, 0,
color_mem,
color_variance_mem,
difference,
w, h, stride,
shift_stride,
task->radius,
task->buffer.pass_stride,
1.0f, task->nlm_k_2);
kernel_set_args(ckNLMBlur, 0,
difference,
blurDifference,
w, h, stride,
shift_stride,
task->radius, 4);
kernel_set_args(ckNLMCalcWeight, 0,
blurDifference,
difference,
w, h, stride,
shift_stride,
task->radius, 4);
kernel_set_args(ckNLMConstructGramian, 0,
blurDifference,
buffer_mem,
transform_mem,
rank_mem,
XtWX_mem,
XtWY_mem,
task->reconstruction_state.filter_window,
w, h, stride,
shift_stride,
task->radius, 4,
task->buffer.pass_stride);
kernel_set_args(ckNLMBlur, 0,
difference,
blurDifference,
local_rect,
task->buffer.w,
f);
enqueue_kernel(ckNLMBlur,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
enqueue_kernel(ckNLMCalcDifference, w*h, num_shifts, true);
enqueue_kernel(ckNLMBlur, w*h, num_shifts, true);
enqueue_kernel(ckNLMCalcWeight, w*h, num_shifts, true);
enqueue_kernel(ckNLMBlur, w*h, num_shifts, true);
enqueue_kernel(ckNLMConstructGramian, w*h, num_shifts, true, 256);
kernel_set_args(ckNLMCalcWeight, 0,
blurDifference,
difference,
local_rect,
task->buffer.w,
f);
enqueue_kernel(ckNLMCalcWeight,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
/* Reuse previous arguments. */
enqueue_kernel(ckNLMBlur,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
kernel_set_args(ckNLMConstructGramian, 0,
dx, dy,
blurDifference,
buffer_mem,
transform_mem,
rank_mem,
XtWX_mem,
XtWY_mem,
local_rect,
task->reconstruction_state.filter_rect,
task->buffer.w,
task->buffer.h,
f,
task->buffer.pass_stride);
enqueue_kernel(ckNLMConstructGramian,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h,
256);
}
opencl_assert(clReleaseMemObject(difference));
opencl_assert(clReleaseMemObject(blurDifference));
kernel_set_args(ckFinalize, 0,
task->buffer.w,
task->buffer.h,
output_mem,
rank_mem,
XtWX_mem,
@ -919,9 +930,7 @@ bool OpenCLDeviceBase::denoising_reconstruct(device_ptr color_ptr,
task->filter_area,
task->reconstruction_state.buffer_params,
task->render_buffer.samples);
enqueue_kernel(ckFinalize,
task->reconstruction_state.source_w,
task->reconstruction_state.source_h);
enqueue_kernel(ckFinalize, w, h);
return true;
}

1
intern/cycles/kernel/CMakeLists.txt
View File

@ -254,6 +254,7 @@ set(SRC_UTIL_HEADERS
../util/util_math_int3.h
../util/util_math_int4.h
../util/util_math_matrix.h
../util/util_rect.h
../util/util_static_assert.h
../util/util_transform.h
../util/util_texture.h

54
intern/cycles/kernel/filter/filter_nlm_cpu.h
View File

@ -21,7 +21,7 @@ ccl_device_inline void kernel_filter_nlm_calc_difference(int dx, int dy,
const float *ccl_restrict variance_image,
float *difference_image,
int4 rect,
int w,
int stride,
int channel_offset,
float a,
float k_2)
@ -31,15 +31,15 @@ ccl_device_inline void kernel_filter_nlm_calc_difference(int dx, int dy,
float diff = 0.0f;
int numChannels = channel_offset? 3 : 1;
for(int c = 0; c < numChannels; c++) {
float cdiff = weight_image[c*channel_offset + y*w+x] - weight_image[c*channel_offset + (y+dy)*w+(x+dx)];
float pvar = variance_image[c*channel_offset + y*w+x];
float qvar = variance_image[c*channel_offset + (y+dy)*w+(x+dx)];
float cdiff = weight_image[c*channel_offset + y*stride + x] - weight_image[c*channel_offset + (y+dy)*stride + (x+dx)];
float pvar = variance_image[c*channel_offset + y*stride + x];
float qvar = variance_image[c*channel_offset + (y+dy)*stride + (x+dx)];
diff += (cdiff*cdiff - a*(pvar + min(pvar, qvar))) / (1e-8f + k_2*(pvar+qvar));
}
if(numChannels > 1) {
diff *= 1.0f/numChannels;
}
difference_image[y*w+x] = diff;
difference_image[y*stride + x] = diff;
}
}
}
@ -47,7 +47,7 @@ ccl_device_inline void kernel_filter_nlm_calc_difference(int dx, int dy,
ccl_device_inline void kernel_filter_nlm_blur(const float *ccl_restrict difference_image,
float *out_image,
int4 rect,
int w,
int stride,
int f)
{
int aligned_lowx = rect.x / 4;
@ -56,17 +56,17 @@ ccl_device_inline void kernel_filter_nlm_blur(const float *ccl_restrict differen
const int low = max(rect.y, y-f);
const int high = min(rect.w, y+f+1);
for(int x = rect.x; x < rect.z; x++) {
out_image[y*w+x] = 0.0f;
out_image[y*stride + x] = 0.0f;
}
for(int y1 = low; y1 < high; y1++) {
float4* out_image4 = (float4*)(out_image + y*w);
float4* difference_image4 = (float4*)(difference_image + y1*w);
float4* out_image4 = (float4*)(out_image + y*stride);
float4* difference_image4 = (float4*)(difference_image + y1*stride);
for(int x = aligned_lowx; x < aligned_highx; x++) {
out_image4[x] += difference_image4[x];
}
}
for(int x = rect.x; x < rect.z; x++) {
out_image[y*w+x] *= 1.0f/(high - low);
out_image[y*stride + x] *= 1.0f/(high - low);
}
}
}
@ -74,12 +74,12 @@ ccl_device_inline void kernel_filter_nlm_blur(const float *ccl_restrict differen
ccl_device_inline void kernel_filter_nlm_calc_weight(const float *ccl_restrict difference_image,
float *out_image,
int4 rect,
int w,
int stride,
int f)
{
for(int y = rect.y; y < rect.w; y++) {
for(int x = rect.x; x < rect.z; x++) {
out_image[y*w+x] = 0.0f;
out_image[y*stride + x] = 0.0f;
}
}
for(int dx = -f; dx <= f; dx++) {
@ -87,7 +87,7 @@ ccl_device_inline void kernel_filter_nlm_calc_weight(const float *ccl_restrict d
int neg_dx = min(0, dx);
for(int y = rect.y; y < rect.w; y++) {
for(int x = rect.x-neg_dx; x < rect.z-pos_dx; x++) {
out_image[y*w+x] += difference_image[y*w+dx+x];
out_image[y*stride + x] += difference_image[y*stride + x+dx];
}
}
}
@ -95,7 +95,7 @@ ccl_device_inline void kernel_filter_nlm_calc_weight(const float *ccl_restrict d
for(int x = rect.x; x < rect.z; x++) {
const int low = max(rect.x, x-f);
const int high = min(rect.z, x+f+1);
out_image[y*w+x] = fast_expf(-max(out_image[y*w+x] * (1.0f/(high - low)), 0.0f));
out_image[y*stride + x] = fast_expf(-max(out_image[y*stride + x] * (1.0f/(high - low)), 0.0f));
}
}
}
@ -106,7 +106,7 @@ ccl_device_inline void kernel_filter_nlm_update_output(int dx, int dy,
float *out_image,
float *accum_image,
int4 rect,
int w,
int stride,
int f)
{
for(int y = rect.y; y < rect.w; y++) {
@ -115,11 +115,11 @@ ccl_device_inline void kernel_filter_nlm_update_output(int dx, int dy,
const int high = min(rect.z, x+f+1);
float sum = 0.0f;
for(int x1 = low; x1 < high; x1++) {
sum += difference_image[y*w+x1];
sum += difference_image[y*stride + x1];
}
float weight = sum * (1.0f/(high - low));
accum_image[y*w+x] += weight;
out_image[y*w+x] += weight*image[(y+dy)*w+(x+dx)];
accum_image[y*stride + x] += weight;
out_image[y*stride + x] += weight*image[(y+dy)*stride + (x+dx)];
}
}
}
@ -132,31 +132,31 @@ ccl_device_inline void kernel_filter_nlm_construct_gramian(int dx, int dy,
float *XtWX,
float3 *XtWY,
int4 rect,
int4 filter_rect,
int w, int h, int f,
int4 filter_window,
int stride, int f,
int pass_stride)
{
int4 clip_area = rect_clip(rect, filter_window);
/* fy and fy are in filter-window-relative coordinates, while x and y are in feature-window-relative coordinates. */
for(int fy = max(0, rect.y-filter_rect.y); fy < min(filter_rect.w, rect.w-filter_rect.y); fy++) {
int y = fy + filter_rect.y;
for(int fx = max(0, rect.x-filter_rect.x); fx < min(filter_rect.z, rect.z-filter_rect.x); fx++) {
int x = fx + filter_rect.x;
for(int y = clip_area.y; y < clip_area.w; y++) {
for(int x = clip_area.x; x < clip_area.z; x++) {
const int low = max(rect.x, x-f);
const int high = min(rect.z, x+f+1);
float sum = 0.0f;
for(int x1 = low; x1 < high; x1++) {
sum += difference_image[y*w+x1];
sum += difference_image[y*stride + x1];
}
float weight = sum * (1.0f/(high - low));
int storage_ofs = fy*filter_rect.z + fx;
int storage_ofs = coord_to_local_index(filter_window, x, y);
float *l_transform = transform + storage_ofs*TRANSFORM_SIZE;
float *l_XtWX = XtWX + storage_ofs*XTWX_SIZE;
float3 *l_XtWY = XtWY + storage_ofs*XTWY_SIZE;
int *l_rank = rank + storage_ofs;
kernel_filter_construct_gramian(x, y, 1,
dx, dy, w, h,
dx, dy,
stride,
pass_stride,
buffer,
l_transform, l_rank,

112
intern/cycles/kernel/filter/filter_nlm_gpu.h
View File

@ -16,57 +16,114 @@
CCL_NAMESPACE_BEGIN
/* Determines pixel coordinates and offset for the current thread.
* Returns whether the thread should do any work.
*
* All coordinates are relative to the denoising buffer!
*
* Window is the rect that should be processed.
* co is filled with (x, y, dx, dy).
*/
ccl_device_inline bool get_nlm_coords_window(int w, int h, int r, int stride,
int4 *rect, int4 *co, int *ofs,
int4 window)
{
/* Determine the pixel offset that this thread should apply. */
int s = 2*r+1;
int si = ccl_global_id(1);
int sx = si % s;
int sy = si / s;
if(sy >= s) {
return false;
}
co->z = sx-r;
co->w = sy-r;
/* Pixels still need to lie inside the denoising buffer after applying the offset,
* so determine the area for which this is the case. */
*rect = make_int4(max(0, -co->z), max(0, -co->w),
w - max(0, co->z), h - max(0, co->w));
/* Find the intersection of the area that we want to process (window) and the area
* that can be processed (rect) to get the final area for this offset. */
int4 clip_area = rect_clip(window, *rect);
/* If the radius is larger than one of the sides of the window,
* there will be shifts for which there is no usable pixel at all. */
if(!rect_is_valid(clip_area)) {
return false;
}
/* Map the linear thread index to pixels inside the clip area. */
int x, y;
if(!local_index_to_coord(clip_area, ccl_global_id(0), &x, &y)) {
return false;
}
co->x = x;
co->y = y;
*ofs = (sy*s + sx) * stride;
return true;
}
ccl_device_inline bool get_nlm_coords(int w, int h, int r, int stride,
int4 *rect, int4 *co, int *ofs)
{
return get_nlm_coords_window(w, h, r, stride, rect, co, ofs, make_int4(0, 0, w, h));
}
ccl_device_inline void kernel_filter_nlm_calc_difference(int x, int y,
int dx, int dy,
const ccl_global float *ccl_restrict weight_image,
const ccl_global float *ccl_restrict variance_image,
ccl_global float *difference_image,
int4 rect, int w,
int4 rect, int stride,
int channel_offset,
float a, float k_2)
{
float diff = 0.0f;
int numChannels = channel_offset? 3 : 1;
for(int c = 0; c < numChannels; c++) {
float cdiff = weight_image[c*channel_offset + y*w+x] - weight_image[c*channel_offset + (y+dy)*w+(x+dx)];
float pvar = variance_image[c*channel_offset + y*w+x];
float qvar = variance_image[c*channel_offset + (y+dy)*w+(x+dx)];
float cdiff = weight_image[c*channel_offset + y*stride + x] - weight_image[c*channel_offset + (y+dy)*stride + (x+dx)];
float pvar = variance_image[c*channel_offset + y*stride + x];
float qvar = variance_image[c*channel_offset + (y+dy)*stride + (x+dx)];
diff += (cdiff*cdiff - a*(pvar + min(pvar, qvar))) / (1e-8f + k_2*(pvar+qvar));
}
if(numChannels > 1) {
diff *= 1.0f/numChannels;
}
difference_image[y*w+x] = diff;
difference_image[y*stride + x] = diff;
}
ccl_device_inline void kernel_filter_nlm_blur(int x, int y,
const ccl_global float *ccl_restrict difference_image,
ccl_global float *out_image,
int4 rect, int w, int f)
int4 rect, int stride, int f)
{
float sum = 0.0f;
const int low = max(rect.y, y-f);
const int high = min(rect.w, y+f+1);
for(int y1 = low; y1 < high; y1++) {
sum += difference_image[y1*w+x];
sum += difference_image[y1*stride + x];
}
sum *= 1.0f/(high-low);
out_image[y*w+x] = sum;
out_image[y*stride + x] = sum;
}
ccl_device_inline void kernel_filter_nlm_calc_weight(int x, int y,
const ccl_global float *ccl_restrict difference_image,
ccl_global float *out_image,
int4 rect, int w, int f)
int4 rect, int stride, int f)
{
float sum = 0.0f;
const int low = max(rect.x, x-f);
const int high = min(rect.z, x+f+1);
for(int x1 = low; x1 < high; x1++) {
sum += difference_image[y*w+x1];
sum += difference_image[y*stride + x1];
}
sum *= 1.0f/(high-low);
out_image[y*w+x] = fast_expf(-max(sum, 0.0f));
out_image[y*stride + x] = fast_expf(-max(sum, 0.0f));
}
ccl_device_inline void kernel_filter_nlm_update_output(int x, int y,
@ -75,25 +132,25 @@ ccl_device_inline void kernel_filter_nlm_update_output(int x, int y,
const ccl_global float *ccl_restrict image,
ccl_global float *out_image,
ccl_global float *accum_image,
int4 rect, int w, int f)
int4 rect, int stride, int f)
{
float sum = 0.0f;
const int low = max(rect.x, x-f);
const int high = min(rect.z, x+f+1);
for(int x1 = low; x1 < high; x1++) {
sum += difference_image[y*w+x1];
sum += difference_image[y*stride + x1];
}
sum *= 1.0f/(high-low);
if(out_image) {
accum_image[y*w+x] += sum;
out_image[y*w+x] += sum*image[(y+dy)*w+(x+dx)];
atomic_add_and_fetch_float(accum_image + y*stride + x, sum);
atomic_add_and_fetch_float(out_image + y*stride + x, sum*image[(y+dy)*stride + (x+dx)]);
}
else {
accum_image[y*w+x] = sum;
accum_image[y*stride + x] = sum;
}
}
ccl_device_inline void kernel_filter_nlm_construct_gramian(int fx, int fy,
ccl_device_inline void kernel_filter_nlm_construct_gramian(int x, int y,
int dx, int dy,
const ccl_global float *ccl_restrict difference_image,
const ccl_global float *ccl_restrict buffer,
@ -102,30 +159,31 @@ ccl_device_inline void kernel_filter_nlm_construct_gramian(int fx, int fy,
ccl_global float *XtWX,
ccl_global float3 *XtWY,
int4 rect,
int4 filter_rect,
int w, int h, int f,
int4 filter_window,
int stride, int f,
int pass_stride,
int localIdx)
{
int y = fy + filter_rect.y;
int x = fx + filter_rect.x;
const int low = max(rect.x, x-f);
const int high = min(rect.z, x+f+1);
float sum = 0.0f;
for(int x1 = low; x1 < high; x1++) {
sum += difference_image[y*w+x1];
sum += difference_image[y*stride + x1];
}
float weight = sum * (1.0f/(high - low));
int storage_ofs = fy*filter_rect.z + fx;
/* Reconstruction data is only stored for pixels inside the filter window,
* so compute the pixels's index in there. */
int storage_ofs = coord_to_local_index(filter_window, x, y);
transform += storage_ofs;
rank += storage_ofs;
XtWX += storage_ofs;
XtWY += storage_ofs;
kernel_filter_construct_gramian(x, y,
filter_rect.z*filter_rect.w,
dx, dy, w, h,
rect_size(filter_window),
dx, dy,
stride,
pass_stride,
buffer,
transform, rank,
@ -136,9 +194,9 @@ ccl_device_inline void kernel_filter_nlm_construct_gramian(int fx, int fy,
ccl_device_inline void kernel_filter_nlm_normalize(int x, int y,
ccl_global float *out_image,
const ccl_global float *ccl_restrict accum_image,
int4 rect, int w)
int stride)
{
out_image[y*w+x] /= accum_image[y*w+x];
out_image[y*stride + x] /= accum_image[y*stride + x];
}
CCL_NAMESPACE_END

8
intern/cycles/kernel/filter/filter_reconstruction.h
View File

@ -19,7 +19,7 @@ CCL_NAMESPACE_BEGIN
ccl_device_inline void kernel_filter_construct_gramian(int x, int y,
int storage_stride,
int dx, int dy,
int w, int h,
int buffer_stride,
int pass_stride,
const ccl_global float *ccl_restrict buffer,
const ccl_global float *ccl_restrict transform,
@ -33,8 +33,8 @@ ccl_device_inline void kernel_filter_construct_gramian(int x, int y,
return;
}
int p_offset = y *w + x;
int q_offset = (y+dy)*w + (x+dx);
int p_offset = y * buffer_stride + x;
int q_offset = (y+dy) * buffer_stride + (x+dx);
#ifdef __KERNEL_GPU__
const int stride = storage_stride;
@ -65,7 +65,7 @@ ccl_device_inline void kernel_filter_construct_gramian(int x, int y,
math_vec3_add_strided(XtWY, (*rank)+1, design_row, weight * q_color, stride);
}
ccl_device_inline void kernel_filter_finalize(int x, int y, int w, int h,
ccl_device_inline void kernel_filter_finalize(int x, int y,
ccl_global float *buffer,
ccl_global int *rank,
int storage_stride,

17
intern/cycles/kernel/kernels/cpu/filter_cpu.h
View File

@ -74,7 +74,7 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_calc_difference)(int dx,
float *variance,
float *difference_image,
int* rect,
int w,
int stride,
int channel_offset,
float a,
float k_2);
@ -82,13 +82,13 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_calc_difference)(int dx,
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_blur)(float *difference_image,
float *out_image,
int* rect,
int w,
int stride,
int f);
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_calc_weight)(float *difference_image,
float *out_image,
int* rect,
int w,
int stride,
int f);
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_update_output)(int dx,
@ -98,7 +98,7 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_update_output)(int dx,
float *out_image,
float *accum_image,
int* rect,
int w,
int stride,
int f);
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_construct_gramian)(int dx,
@ -110,22 +110,19 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_construct_gramian)(int dx,
float *XtWX,
float3 *XtWY,
int *rect,
int *filter_rect,
int w,
int h,
int *filter_window,
int stride,
int f,
int pass_stride);
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_normalize)(float *out_image,
float *accum_image,
int* rect,
int w);
int stride);
void KERNEL_FUNCTION_FULL_NAME(filter_finalize)(int x,
int y,
int storage_ofs,
int w,
int h,
float *buffer,
int *rank,
float *XtWX,

31
intern/cycles/kernel/kernels/cpu/filter_cpu_impl.h
View File

@ -150,7 +150,7 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_calc_difference)(int dx,
float *variance,
float *difference_image,
int *rect,
int w,
int stride,
int channel_offset,
float a,
float k_2)
@ -158,33 +158,33 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_calc_difference)(int dx,
#ifdef KERNEL_STUB
STUB_ASSERT(KERNEL_ARCH, filter_nlm_calc_difference);
#else
kernel_filter_nlm_calc_difference(dx, dy, weight_image, variance, difference_image, load_int4(rect), w, channel_offset, a, k_2);
kernel_filter_nlm_calc_difference(dx, dy, weight_image, variance, difference_image, load_int4(rect), stride, channel_offset, a, k_2);
#endif
}
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_blur)(float *difference_image,
float *out_image,
int *rect,
int w,
int stride,
int f)
{
#ifdef KERNEL_STUB
STUB_ASSERT(KERNEL_ARCH, filter_nlm_blur);
#else
kernel_filter_nlm_blur(difference_image, out_image, load_int4(rect), w, f);
kernel_filter_nlm_blur(difference_image, out_image, load_int4(rect), stride, f);
#endif
}
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_calc_weight)(float *difference_image,
float *out_image,
int *rect,
int w,
int stride,
int f)
{
#ifdef KERNEL_STUB
STUB_ASSERT(KERNEL_ARCH, filter_nlm_calc_weight);
#else
kernel_filter_nlm_calc_weight(difference_image, out_image, load_int4(rect), w, f);
kernel_filter_nlm_calc_weight(difference_image, out_image, load_int4(rect), stride, f);
#endif
}
@ -195,13 +195,13 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_update_output)(int dx,
float *out_image,
float *accum_image,
int *rect,
int w,
int stride,
int f)
{
#ifdef KERNEL_STUB
STUB_ASSERT(KERNEL_ARCH, filter_nlm_update_output);
#else
kernel_filter_nlm_update_output(dx, dy, difference_image, image, out_image, accum_image, load_int4(rect), w, f);
kernel_filter_nlm_update_output(dx, dy, difference_image, image, out_image, accum_image, load_int4(rect), stride, f);
#endif
}
@ -214,36 +214,33 @@ void KERNEL_FUNCTION_FULL_NAME(filter_nlm_construct_gramian)(int dx,
float *XtWX,
float3 *XtWY,
int *rect,
int *filter_rect,
int w,
int h,
int *filter_window,
int stride,
int f,
int pass_stride)
{
#ifdef KERNEL_STUB
STUB_ASSERT(KERNEL_ARCH, filter_nlm_construct_gramian);
#else
kernel_filter_nlm_construct_gramian(dx, dy, difference_image, buffer, transform, rank, XtWX, XtWY, load_int4(rect), load_int4(filter_rect), w, h, f, pass_stride);
kernel_filter_nlm_construct_gramian(dx, dy, difference_image, buffer, transform, rank, XtWX, XtWY, load_int4(rect), load_int4(filter_window), stride, f, pass_stride);
#endif
}
void KERNEL_FUNCTION_FULL_NAME(filter_nlm_normalize)(float *out_image,
float *accum_image,
int *rect,
int w)
int stride)
{
#ifdef KERNEL_STUB
STUB_ASSERT(KERNEL_ARCH, filter_nlm_normalize);
#else
kernel_filter_nlm_normalize(out_image, accum_image, load_int4(rect), w);
kernel_filter_nlm_normalize(out_image, accum_image, load_int4(rect), stride);
#endif
}
void KERNEL_FUNCTION_FULL_NAME(filter_finalize)(int x,
int y,
int storage_ofs,
int w,
int h,
float *buffer,
int *rank,
float *XtWX,
@ -257,7 +254,7 @@ void KERNEL_FUNCTION_FULL_NAME(filter_finalize)(int x,
XtWX += storage_ofs*XTWX_SIZE;
XtWY += storage_ofs*XTWY_SIZE;
rank += storage_ofs;
kernel_filter_finalize(x, y, w, h, buffer, rank, 1, XtWX, XtWY, load_int4(buffer_params), sample);
kernel_filter_finalize(x, y, buffer, rank, 1, XtWX, XtWY, load_int4(buffer_params), sample);
#endif
}

148
intern/cycles/kernel/kernels/cuda/filter.cu
View File

@ -134,95 +134,140 @@ kernel_cuda_filter_construct_transform(float const* __restrict__ buffer,
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_calc_difference(int dx, int dy,
const float *ccl_restrict weight_image,
kernel_cuda_filter_nlm_calc_difference(const float *ccl_restrict weight_image,
const float *ccl_restrict variance_image,
float *difference_image,
int4 rect, int w,
int w,
int h,
int stride,
int shift_stride,
int r,
int channel_offset,
float a, float k_2)
float a,
float k_2)
{
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_calc_difference(x, y, dx, dy, weight_image, variance_image, difference_image, rect, w, channel_offset, a, k_2);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_calc_difference(co.x, co.y, co.z, co.w,
weight_image,
variance_image,
difference_image + ofs,
rect, stride,
channel_offset, a, k_2);
}
}
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_blur(const float *ccl_restrict difference_image, float *out_image, int4 rect, int w, int f)
kernel_cuda_filter_nlm_blur(const float *ccl_restrict difference_image,
float *out_image,
int w,
int h,
int stride,
int shift_stride,
int r,
int f)
{
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_blur(x, y, difference_image, out_image, rect, w, f);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_blur(co.x, co.y,
difference_image + ofs,
out_image + ofs,
rect, stride, f);
}
}
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_calc_weight(const float *ccl_restrict difference_image, float *out_image, int4 rect, int w, int f)
kernel_cuda_filter_nlm_calc_weight(const float *ccl_restrict difference_image,
float *out_image,
int w,
int h,
int stride,
int shift_stride,
int r,
int f)
{
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_calc_weight(x, y, difference_image, out_image, rect, w, f);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_calc_weight(co.x, co.y,
difference_image + ofs,
out_image + ofs,
rect, stride, f);
}
}
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_update_output(int dx, int dy,
const float *ccl_restrict difference_image,
kernel_cuda_filter_nlm_update_output(const float *ccl_restrict difference_image,
const float *ccl_restrict image,
float *out_image, float *accum_image,
int4 rect, int w,
float *out_image,
float *accum_image,
int w,
int h,
int stride,
int shift_stride,
int r,
int f)
{
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_update_output(x, y, dx, dy, difference_image, image, out_image, accum_image, rect, w, f);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_update_output(co.x, co.y, co.z, co.w,
difference_image + ofs,
image,
out_image,
accum_image,
rect, stride, f);
}
}
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_normalize(float *out_image, const float *ccl_restrict accum_image, int4 rect, int w)
kernel_cuda_filter_nlm_normalize(float *out_image,
const float *ccl_restrict accum_image,
int w,
int h,
int stride)
{
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_normalize(x, y, out_image, accum_image, rect, w);
int x = blockDim.x*blockIdx.x + threadIdx.x;
int y = blockDim.y*blockIdx.y + threadIdx.y;
if(x < w && y < h) {
kernel_filter_nlm_normalize(x, y, out_image, accum_image, stride);
}
}
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_construct_gramian(int dx, int dy,
const float *ccl_restrict difference_image,
kernel_cuda_filter_nlm_construct_gramian(const float *ccl_restrict difference_image,
const float *ccl_restrict buffer,
float const* __restrict__ transform,
int *rank,
float *XtWX,
float3 *XtWY,
int4 rect,
int4 filter_rect,
int w, int h, int f,
int4 filter_window,
int w,
int h,
int stride,
int shift_stride,
int r,
int f,
int pass_stride)
{
int x = blockDim.x*blockIdx.x + threadIdx.x + max(0, rect.x-filter_rect.x);
int y = blockDim.y*blockIdx.y + threadIdx.y + max(0, rect.y-filter_rect.y);
if(x < min(filter_rect.z, rect.z-filter_rect.x) && y < min(filter_rect.w, rect.w-filter_rect.y)) {
kernel_filter_nlm_construct_gramian(x, y,
dx, dy,
difference_image,
int4 co, rect;
int ofs;
if(get_nlm_coords_window(w, h, r, shift_stride, &rect, &co, &ofs, filter_window)) {
kernel_filter_nlm_construct_gramian(co.x, co.y,
co.z, co.w,
difference_image + ofs,
buffer,
transform, rank,
XtWX, XtWY,
rect, filter_rect,
w, h, f,
rect, filter_window,
stride, f,
pass_stride,
threadIdx.y*blockDim.x + threadIdx.x);
}
@ -230,10 +275,12 @@ kernel_cuda_filter_nlm_construct_gramian(int dx, int dy,
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_finalize(int w, int h,
float *buffer, int *rank,
float *XtWX, float3 *XtWY,
int4 filter_area, int4 buffer_params,
kernel_cuda_filter_finalize(float *buffer,
int *rank,
float *XtWX,
float3 *XtWY,
int4 filter_area,
int4 buffer_params,
int sample)
{
int x = blockDim.x*blockIdx.x + threadIdx.x;
@ -243,7 +290,10 @@ kernel_cuda_filter_finalize(int w, int h,
rank += storage_ofs;
XtWX += storage_ofs;
XtWY += storage_ofs;
kernel_filter_finalize(x, y, w, h, buffer, rank, filter_area.z*filter_area.w, XtWX, XtWY, buffer_params, sample);
kernel_filter_finalize(x, y, buffer, rank,
filter_area.z*filter_area.w,
XtWX, XtWY,
buffer_params, sample);
}
}

124
intern/cycles/kernel/kernels/opencl/filter.cl
View File

@ -126,113 +126,136 @@ __kernel void kernel_ocl_filter_construct_transform(const ccl_global float *ccl_
}
}
__kernel void kernel_ocl_filter_nlm_calc_difference(int dx,
int dy,
const ccl_global float *ccl_restrict weight_image,
__kernel void kernel_ocl_filter_nlm_calc_difference(const ccl_global float *ccl_restrict weight_image,
const ccl_global float *ccl_restrict variance_image,
ccl_global float *difference_image,
int4 rect,
int w,
int h,
int stride,
int shift_stride,
int r,
int channel_offset,
float a,
float k_2)
{
int x = get_global_id(0) + rect.x;
int y = get_global_id(1) + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_calc_difference(x, y, dx, dy, weight_image, variance_image, difference_image, rect, w, channel_offset, a, k_2);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_calc_difference(co.x, co.y, co.z, co.w,
weight_image,
variance_image,
difference_image + ofs,
rect, stride,
channel_offset, a, k_2);
}
}
__kernel void kernel_ocl_filter_nlm_blur(const ccl_global float *ccl_restrict difference_image,
ccl_global float *out_image,
int4 rect,
int w,
int h,
int stride,
int shift_stride,
int r,
int f)
{
int x = get_global_id(0) + rect.x;
int y = get_global_id(1) + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_blur(x, y, difference_image, out_image, rect, w, f);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_blur(co.x, co.y,
difference_image + ofs,
out_image + ofs,
rect, stride, f);
}
}
__kernel void kernel_ocl_filter_nlm_calc_weight(const ccl_global float *ccl_restrict difference_image,
ccl_global float *out_image,
int4 rect,
int w,
int h,
int stride,
int shift_stride,
int r,
int f)
{
int x = get_global_id(0) + rect.x;
int y = get_global_id(1) + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_calc_weight(x, y, difference_image, out_image, rect, w, f);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_calc_weight(co.x, co.y,
difference_image + ofs,
out_image + ofs,
rect, stride, f);
}
}
__kernel void kernel_ocl_filter_nlm_update_output(int dx,
int dy,
const ccl_global float *ccl_restrict difference_image,
__kernel void kernel_ocl_filter_nlm_update_output(const ccl_global float *ccl_restrict difference_image,
const ccl_global float *ccl_restrict image,
ccl_global float *out_image,
ccl_global float *accum_image,
int4 rect,
int w,
int h,
int stride,
int shift_stride,
int r,
int f)
{
int x = get_global_id(0) + rect.x;
int y = get_global_id(1) + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_update_output(x, y, dx, dy, difference_image, image, out_image, accum_image, rect, w, f);
int4 co, rect;
int ofs;
if(get_nlm_coords(w, h, r, shift_stride, &rect, &co, &ofs)) {
kernel_filter_nlm_update_output(co.x, co.y, co.z, co.w,
difference_image + ofs,
image,
out_image,
accum_image,
rect, stride, f);
}
}
__kernel void kernel_ocl_filter_nlm_normalize(ccl_global float *out_image,
const ccl_global float *ccl_restrict accum_image,
int4 rect,
int w)
int w,
int h,
int stride)
{
int x = get_global_id(0) + rect.x;
int y = get_global_id(1) + rect.y;
if(x < rect.z && y < rect.w) {
kernel_filter_nlm_normalize(x, y, out_image, accum_image, rect, w);
int x = get_global_id(0);
int y = get_global_id(1);
if(x < w && y < h) {
kernel_filter_nlm_normalize(x, y, out_image, accum_image, stride);
}
}
__kernel void kernel_ocl_filter_nlm_construct_gramian(int dx,
int dy,
const ccl_global float *ccl_restrict difference_image,
__kernel void kernel_ocl_filter_nlm_construct_gramian(const ccl_global float *ccl_restrict difference_image,
const ccl_global float *ccl_restrict buffer,
const ccl_global float *ccl_restrict transform,
ccl_global int *rank,
ccl_global float *XtWX,
ccl_global float3 *XtWY,
int4 rect,
int4 filter_rect,
int4 filter_window,
int w,
int h,
int stride,
int shift_stride,
int r,
int f,
int pass_stride)
{
int x = get_global_id(0) + max(0, rect.x-filter_rect.x);
int y = get_global_id(1) + max(0, rect.y-filter_rect.y);
if(x < min(filter_rect.z, rect.z-filter_rect.x) && y < min(filter_rect.w, rect.w-filter_rect.y)) {
kernel_filter_nlm_construct_gramian(x, y,
dx, dy,
difference_image,
int4 co, rect;
int ofs;
if(get_nlm_coords_window(w, h, r, shift_stride, &rect, &co, &ofs, filter_window)) {
kernel_filter_nlm_construct_gramian(co.x, co.y,
co.z, co.w,
difference_image + ofs,
buffer,
transform, rank,
XtWX, XtWY,
rect, filter_rect,
w, h, f,
rect, filter_window,
stride, f,
pass_stride,
get_local_id(1)*get_local_size(0) + get_local_id(0));
}
}
__kernel void kernel_ocl_filter_finalize(int w,
int h,
ccl_global float *buffer,
__kernel void kernel_ocl_filter_finalize(ccl_global float *buffer,
ccl_global int *rank,
ccl_global float *XtWX,
ccl_global float3 *XtWY,
@ -247,7 +270,10 @@ __kernel void kernel_ocl_filter_finalize(int w,
rank += storage_ofs;
XtWX += storage_ofs;
XtWY += storage_ofs;
kernel_filter_finalize(x, y, w, h, buffer, rank, filter_area.z*filter_area.w, XtWX, XtWY, buffer_params, sample);
kernel_filter_finalize(x, y, buffer, rank,
filter_area.z*filter_area.w,
XtWX, XtWY,
buffer_params, sample);
}
}

1
intern/cycles/util/CMakeLists.txt
View File

@ -68,6 +68,7 @@ set(SRC_HEADERS
util_path.h
util_progress.h
util_queue.h
util_rect.h
util_set.h
util_simd.h
util_sky_model.cpp

2
intern/cycles/util/util_math.h
View File

@ -320,6 +320,8 @@ CCL_NAMESPACE_END
#include "util/util_math_float3.h"
#include "util/util_math_float4.h"
#include "util/util_rect.h"
CCL_NAMESPACE_BEGIN
#ifndef __KERNEL_OPENCL__

7
intern/cycles/util/util_math_matrix.h
View File

@ -98,7 +98,10 @@ ccl_device_inline void math_vec3_add(float3 *v, int n, float *x, float3 w)
ccl_device_inline void math_vec3_add_strided(ccl_global float3 *v, int n, float *x, float3 w, int stride)
{
for(int i = 0; i < n; i++) {
v[i*stride] += w*x[i];
ccl_global float *elem = (ccl_global float*) (v + i*stride);
atomic_add_and_fetch_float(elem+0, w.x*x[i]);
atomic_add_and_fetch_float(elem+1, w.y*x[i]);
atomic_add_and_fetch_float(elem+2, w.z*x[i]);
}
}
@ -136,7 +139,7 @@ ccl_device_inline void math_trimatrix_add_gramian_strided(ccl_global float *A,
{
for(int row = 0; row < n; row++) {
for(int col = 0; col <= row; col++) {
MATHS(A, row, col, stride) += v[row]*v[col]*weight;
atomic_add_and_fetch_float(&MATHS(A, row, col, stride), v[row]*v[col]*weight);
}
}
}

73
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/*
* Copyright 2017 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef __UTIL_RECT_H__
#define __UTIL_RECT_H__
#include "util/util_types.h"
CCL_NAMESPACE_BEGIN
/* Rectangles are represented as a int4 containing the coordinates of the lower-left and
* upper-right corners in the order (x0, y0, x1, y1). */
ccl_device_inline int4 rect_from_shape(int x0, int y0, int w, int h)
{
return make_int4(x0, y0, x0 + w, y0 + h);
}
ccl_device_inline int4 rect_expand(int4 rect, int d)
{
return make_int4(rect.x - d, rect.y - d, rect.z + d, rect.w + d);
}
/* Returns the intersection of two rects. */
ccl_device_inline int4 rect_clip(int4 a, int4 b)
{
return make_int4(max(a.x, b.x), max(a.y, b.y), min(a.z, b.z), min(a.w, b.w));
}
ccl_device_inline bool rect_is_valid(int4 rect)
{
return (rect.z > rect.x) && (rect.w > rect.y);
}
/* Returns the local row-major index of the pixel inside the rect. */
ccl_device_inline int coord_to_local_index(int4 rect, int x, int y)
{
int w = rect.z - rect.x;
return (y - rect.y) * w + (x - rect.x);
}
/* Finds the coordinates of a pixel given by its row-major index in the rect,
* and returns whether the pixel is inside it. */
ccl_device_inline bool local_index_to_coord(int4 rect, int idx, int *x, int *y)
{
int w = rect.z - rect.x;
*x = (idx % w) + rect.x;
*y = (idx / w) + rect.y;
return (*y < rect.w);
}
ccl_device_inline int rect_size(int4 rect)
{
return (rect.z - rect.x) * (rect.w - rect.y);
}
CCL_NAMESPACE_END
#endif /* __UTIL_RECT_H__ */