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cxVNNclAlgorithm.cpp
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1 /*=========================================================================
2 This file is part of CustusX, an Image Guided Therapy Application.
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4 Copyright (c) 2008-2014, SINTEF Department of Medical Technology
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32 
33 #include "cxVNNclAlgorithm.h"
34 
35 #include <QFileInfo>
36 #include "cxLogger.h"
37 #include "cxTypeConversions.h"
38 #include "HelperFunctions.hpp"
39 #include <vtkImageData.h>
40 #include <recConfig.h>
41 #include "cxVolumeHelpers.h"
42 
43 namespace cx
44 {
46  mRuntime(new oul::RuntimeMeasurementsManager()),
47  mKernelMeasurementName("vnncl_execute_kernel")
48 {
49  oul::DeviceCriteria criteria;
50  criteria.setTypeCriteria(oul::DEVICE_TYPE_GPU);
51  bool enableProfilling = true;
52  mOulContex = oul::opencl()->createContextPtr(criteria, NULL, enableProfilling);
53  if(enableProfilling)
54  mRuntime->enable();
55 }
56 
58 {}
59 
60 bool VNNclAlgorithm::initCL(QString kernelFilePath, int nMaxPlanes, int nPlanes, int method, int planeMethod, int nStarts, float brightnessWeight, float newnessWeight)
61 {
62  // READ KERNEL FILE
63  report(QString("Kernel path: %1").arg(kernelFilePath));
64  std::string source = oul::readFile(kernelFilePath.toStdString());
65 
66  QFileInfo path(kernelFilePath);
67  path.absolutePath();
68 
69  // BUILD PROGRAM
70  cl::Program clprogram = this->buildCLProgram(source, path.absolutePath().toStdString(), nMaxPlanes, nPlanes, method, planeMethod, nStarts,brightnessWeight, newnessWeight);
71 
72  // CREATE KERNEL
73  mKernel = mOulContex->createKernel(clprogram, "voxel_methods");
74 
75  return true;
76 }
77 
78 cl::Program VNNclAlgorithm::buildCLProgram(std::string program_src, std::string kernelPath, int nMaxPlanes, int nPlanes, int method, int planeMethod, int nStarts, float newnessWeight, float brightnessWeight)
79 {
80  cl::Program retval;
81  cl_int err = 0;
82  VECTOR_CLASS<cl::Device> devices;
83  try
84  {
85  QString define = "-D MAX_PLANES=%1 -D N_PLANES=%2 -D METHOD=%3 -D PLANE_METHOD=%4 -D MAX_MULTISTART_STARTS=%5 -D NEWNESS_FACTOR=%6 -D BRIGHTNESS_FACTOR=%7";
86  define = define.arg(nMaxPlanes).arg(nPlanes).arg(method).arg(planeMethod).arg(nStarts).arg(newnessWeight).arg(brightnessWeight);
87 
88  int programID = mOulContex->createProgramFromString(program_src, "-I " + std::string(kernelPath) + " " + define.toStdString());
89  retval = mOulContex->getProgram(programID);
90 
91  } catch (cl::Error &error)
92  {
93  reportError("Could not build a OpenCL program. Reason: "+QString(error.what()));
94  reportError(qstring_cast(oul::getCLErrorString(error.err())));
95  }
96  return retval;
97 }
98 
99 bool VNNclAlgorithm::initializeFrameBlocks(frameBlock_t* framePointers, int numBlocks, ProcessedUSInputDataPtr inputFrames)
100 {
101  // Compute the size of each frame in bytes
102  Eigen::Array3i dims = inputFrames->getDimensions();
103  size_t frameSize = dims[0] * dims[1];
104  size_t numFrames = dims[2];
105  report(QString("Input dims: (%1, %2, %3)").arg(dims[0]).arg(dims[1]).arg(dims[2]));
106 
107  // Find out how many frames needs to be in each block
108  size_t framesPerBlock = numFrames / numBlocks;
109  report(QString("Frames: %1, Blocks: %2, Frames per block: %3").arg(numFrames).arg(numBlocks).arg(framesPerBlock));
110 
111  // Some blocks will need to contain one extra frame
112  // (numFrames and numBlocks is probably not evenly divisible)
113  size_t numBigBlocks = numFrames % numBlocks;
114 
115  // Allocate the big blocks
116  report(QString("Allocating %1 big blocks outside of OpenCL").arg(numBigBlocks));
117  for (unsigned int block = 0; block < numBigBlocks; block++)
118  {
119  framePointers[block].length = (1 + framesPerBlock) * frameSize;
120  framePointers[block].data = new unsigned char[framePointers[block].length];
121  }
122 
123  // Then the small ones
124  report(QString("Allocating %1 small blocks outside of OpenCL").arg(numBlocks - numBigBlocks));
125  for (int block = numBigBlocks; block < numBlocks; block++)
126  {
127  framePointers[block].length = (framesPerBlock) * frameSize;
128  framePointers[block].data = new unsigned char[framePointers[block].length];
129  }
130 
131  // Now fill them
132  unsigned int frame = 0;
133  for (int block = 0; block < numBlocks; block++)
134  {
135  for (unsigned int frameInThisBlock = 0; frameInThisBlock < framePointers[block].length / frameSize; frameInThisBlock++)
136  {
137  memcpy(&(framePointers[block].data[frameInThisBlock * frameSize]), inputFrames->getFrame(frame), frameSize);
138  frame++;
139  }
140  }
141  return true;
142 }
143 
144 bool VNNclAlgorithm::reconstruct(ProcessedUSInputDataPtr input, vtkImageDataPtr outputData, float radius, int nClosePlanes)
145 {
146  mMeasurementNames.clear();
147 
148  int numBlocks = 10; // FIXME? needs to be the same as the number of input bscans to the voxel_method kernel
149 
150  // Split input US into blocks
151  // Splits and copies data from the processed input in the way the kernel will processes it, which is per frameBlock
152  frameBlock_t* inputBlocks = new frameBlock_t[numBlocks];
153  size_t nPlanes_numberOfInputImages = input->getDimensions()[2];
154  this->initializeFrameBlocks(inputBlocks, numBlocks, input);
155 
156  // Allocate CL memory for each frame block
157  VECTOR_CLASS<cl::Buffer> clBlocks;
158  report("Allocating OpenCL input block buffers");
159  for (int i = 0; i < numBlocks; i++)
160  {
161  //TODO why does the context suddenly contain a "dummy" device?
162  cl::Buffer buffer = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, inputBlocks[i].length, inputBlocks[i].data, "block buffer "+QString::number(i).toStdString());
163  clBlocks.push_back(buffer);
164  }
165  // Allocate output memory
166  int *outputDims = outputData->GetDimensions();
167 
168  size_t outputVolumeSize = outputDims[0] * outputDims[1] * outputDims[2] * sizeof(unsigned char);
169 
170  report(QString("Allocating CL output buffer, size %1").arg(outputVolumeSize));
171 
172  cl_ulong globalMemUse = 10 * inputBlocks[0].length + outputVolumeSize + sizeof(float) * 16 * nPlanes_numberOfInputImages + sizeof(cl_uchar) * input->getDimensions()[0] * input->getDimensions()[1];
173  if(isUsingTooMuchMemory(outputVolumeSize, inputBlocks[0].length, globalMemUse))
174  return false;
175 
176  cl::Buffer outputBuffer = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_WRITE_ONLY, outputVolumeSize, NULL, "output volume buffer");
177 
178  // Fill the plane matrices
179  float *planeMatrices = new float[16 * nPlanes_numberOfInputImages]; //4x4 (matrix) = 16
180  this->fillPlaneMatrices(planeMatrices, input);
181 
182  cl::Buffer clPlaneMatrices = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, nPlanes_numberOfInputImages * sizeof(float) * 16, planeMatrices, "plane matrices buffer");
183 
184  // US Probe mask
185  cl::Buffer clMask = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
186  sizeof(cl_uchar) * input->getMask()->GetDimensions()[0] * input->getMask()->GetDimensions()[1],
187  input->getMask()->GetScalarPointer(), "mask buffer");
188 
189  double *out_spacing = outputData->GetSpacing();
190  float spacings[2];
191  float f_out_spacings[3];
192  f_out_spacings[0] = out_spacing[0];
193  f_out_spacings[1] = out_spacing[1];
194  f_out_spacings[2] = out_spacing[2];
195 
196 
197  spacings[0] = input->getSpacing()[0];
198  spacings[1] = input->getSpacing()[1];
199 
200  //TODO why 4? because float4 is used??
201  size_t planes_eqs_size = sizeof(cl_float)*4*nPlanes_numberOfInputImages;
202 
203  // Find the optimal local work size
204  size_t local_work_size;
205  unsigned int deviceNumber = 0;
206  cl::Device device = mOulContex->getDevice(deviceNumber);
207  mKernel.getWorkGroupInfo(device, CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE, &local_work_size);
208 
209  size_t close_planes_size = this->calculateSpaceNeededForClosePlanes(mKernel, device, local_work_size, nPlanes_numberOfInputImages, nClosePlanes);
210 
211  this->setKernelArguments(
212  mKernel,
213  outputDims[0],
214  outputDims[1],
215  outputDims[2],
216  f_out_spacings[0],
217  f_out_spacings[1],
218  f_out_spacings[2],
219  input->getDimensions()[0],
220  input->getDimensions()[1],
221  spacings[0],
222  spacings[1],
223  clBlocks,
224  outputBuffer,
225  clPlaneMatrices,
226  clMask,
227  planes_eqs_size,
228  close_planes_size,
229  radius);
230 
231  report(QString("Using %1 as local workgroup size").arg(local_work_size));
232 
233  // We will divide the work into cubes of CUBE_DIM^3 voxels. The global work size is the total number of voxels divided by that.
234  int cube_dim = 4;
235  int cube_dim_pow3 = cube_dim * cube_dim * cube_dim;
236  // Global work items:
237  size_t global_work_size = (((outputDims[0] + cube_dim) * (outputDims[1] + cube_dim) * (outputDims[2] + cube_dim)) / cube_dim_pow3); // = number of cubes = number of kernels to run
238 
239  // Round global_work_size up to nearest multiple of local_work_size
240  if (global_work_size % local_work_size)
241  global_work_size = ((global_work_size / local_work_size) + 1) * local_work_size; // ceil(...)
242 
243  unsigned int queueNumber = 0;
244  cl::CommandQueue queue = mOulContex->getQueue(queueNumber);
245  this->measureAndExecuteKernel(queue, mKernel, global_work_size, local_work_size, mKernelMeasurementName);
246  this->measureAndReadBuffer(queue, outputBuffer, outputVolumeSize, outputData->GetScalarPointer(), "vnncl_read_buffer");
247  setDeepModified(outputData);
248  // Cleaning up
249  report(QString("Done, freeing GPU memory"));
250  this->freeFrameBlocks(inputBlocks, numBlocks);
251  delete[] inputBlocks;
252 
253  inputBlocks = NULL;
254 
255  return true;
256 }
257 
259 {
260  std::vector<TimedPosition> vecPosition = input->getFrames();
261 
262  // Sanity check on the number of frames
263  if (input->getDimensions()[2] != vecPosition.end() - vecPosition.begin())
264  {
265  reportError(QString("Number of frames %1 != %2 dimension 2 of US input").arg(input->getDimensions()[2]).arg(vecPosition.end() - vecPosition.begin()));
266  return;
267  }
268 
269  int i = 0;
270  for (std::vector<TimedPosition>::iterator it = vecPosition.begin(); it != vecPosition.end(); ++it)
271  {
272  Transform3D pos = it->mPos;
273 
274  // Now store the result in the output
275  for (int j = 0; j < 16; j++)
276  {
277  planeMatrices[i++] = pos(j / 4, j % 4);
278  }
279  }
280 }
281 
283 {
284  if(on)
285  mRuntime->enable();
286  else
287  mRuntime->disable();
288 }
289 
291 {
292  double totalExecutionTime = -1;
293  std::set<std::string>::iterator it;
294  for(it = mMeasurementNames.begin(); it != mMeasurementNames.end(); ++it)
295  {
296  oul::RuntimeMeasurement measurement = mRuntime->getTiming(*it);
297  totalExecutionTime += measurement.getSum();
298  }
299  return totalExecutionTime;
300 }
301 
303 {
304 // double kernelExecutionTime = -1;
305  return mRuntime->getTiming(mKernelMeasurementName).getSum();
306 
307 }
308 
309 void VNNclAlgorithm::freeFrameBlocks(frameBlock_t *framePointers, int numBlocks)
310 {
311  for (int i = 0; i < numBlocks; i++)
312  {
313  delete[] framePointers[i].data;
314  }
315 }
316 
317 void VNNclAlgorithm::setKernelArguments(
318  cl::Kernel kernel,
319  int volume_xsize,
320  int volume_ysize,
321  int volume_zsize,
322  float volume_xspacing,
323  float volume_yspacing,
324  float volume_zspacing,
325  int in_xsize,
326  int in_ysize,
327  float in_xspacing,
328  float in_yspacing,
329  std::vector<cl::Buffer>& blocks,
330  cl::Buffer out_volume,
331  cl::Buffer plane_matrices,
332  cl::Buffer mask,
333  size_t plane_eqs_size,
334  size_t close_planes_size,
335  float radius)
336 {
337  int arg = 0;
338  kernel.setArg(arg++, volume_xsize);
339  kernel.setArg(arg++, volume_ysize);
340  kernel.setArg(arg++, volume_zsize);
341  kernel.setArg(arg++, volume_xspacing);
342  kernel.setArg(arg++, volume_yspacing);
343  kernel.setArg(arg++, volume_zspacing);
344  kernel.setArg(arg++, in_xsize);
345  kernel.setArg(arg++, in_ysize);
346  kernel.setArg(arg++, in_xspacing);
347  kernel.setArg(arg++, in_yspacing);
348  for (int i = 0; i < blocks.size(); i++)
349  {
350  kernel.setArg(arg++, blocks[i]);
351  }
352  kernel.setArg(arg++, out_volume);
353  kernel.setArg(arg++, plane_matrices);
354  kernel.setArg(arg++, mask);
355  kernel.setArg<cl::LocalSpaceArg>(arg++, cl::__local(plane_eqs_size));
356  kernel.setArg<cl::LocalSpaceArg>(arg++, cl::__local(close_planes_size));
357  kernel.setArg(arg++, radius);
358 }
359 
360 size_t VNNclAlgorithm::calculateSpaceNeededForClosePlanes(cl::Kernel kernel, cl::Device device, size_t local_work_size, size_t nPlanes_numberOfInputImages, int nClosePlanes)
361 {
362  // Find out how much local memory the device has
363  size_t dev_local_mem_size;
364  dev_local_mem_size = device.getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
365 
366  // Find the maximum work group size
367  size_t max_work_size;
368  kernel.getWorkGroupInfo(device, CL_KERNEL_WORK_GROUP_SIZE, &max_work_size);
369 
370  // Now find the largest multiple of the preferred work group size that will fit into local mem
371  size_t constant_local_mem = sizeof(cl_float) * 4 * nPlanes_numberOfInputImages;
372 
373  size_t varying_local_mem = (sizeof(cl_float) + sizeof(cl_short) + sizeof(cl_uchar) + sizeof(cl_uchar)) * (nClosePlanes + 1); //see _close_plane struct in kernels.cl
374  report(QString("Device has %1 bytes of local memory").arg(dev_local_mem_size));
375  dev_local_mem_size -= constant_local_mem + 128; //Hmmm? 128?
376 
377  // How many work items can the local mem support?
378  size_t maxItems = dev_local_mem_size / varying_local_mem;
379  // And what is the biggest multiple of local_work_size that fits into that?
380  int multiple = maxItems / local_work_size;
381 
382  if(multiple == 0)
383  {
384  // If the maximum amount of work items is smaller than the preferred multiple, we end up here.
385  // This means that the local memory use is so big we can't even fit into the preferred multiple, and
386  // have use a sub-optimal local work size.
387  local_work_size = std::min(max_work_size, maxItems);
388  }
389  else
390  {
391  // Otherwise, we make it fit into the local work size.
392  local_work_size = std::min(max_work_size, multiple * local_work_size);
393  }
394 
395  size_t close_planes_size = varying_local_mem*local_work_size;
396 
397  return close_planes_size;
398 }
399 
400 bool VNNclAlgorithm::isUsingTooMuchMemory(size_t outputVolumeSize, size_t inputBlocksLength, cl_ulong globalMemUse)
401 {
402  bool usingTooMuchMemory = false;
403 
404  unsigned int deviceNumber = 0;
405  cl_ulong maxAllocSize = mOulContex->getDevice(deviceNumber).getInfo<CL_DEVICE_MAX_MEM_ALLOC_SIZE>();
406  cl_ulong globalMemSize = mOulContex->getDevice(deviceNumber).getInfo<CL_DEVICE_GLOBAL_MEM_SIZE>();
407  if (maxAllocSize < outputVolumeSize)
408  {
409  reportError(QString("Output volume size too large! %1 > %2\n").arg(outputVolumeSize).arg(maxAllocSize));
410  usingTooMuchMemory = true;
411  }
412 
413  if (maxAllocSize < inputBlocksLength)
414  {
415  reportError(QString("Input blocks too large! %1 > %2\n").arg(inputBlocksLength).arg(maxAllocSize));
416  usingTooMuchMemory = true;
417  }
418 
419  if (globalMemSize < globalMemUse)
420  {
421  reportError(QString("Using too much global memory! %1 > %2").arg(globalMemUse).arg(globalMemSize));
422  usingTooMuchMemory = true;
423  }
424 
425  report(QString("Using %1 of %2 global memory").arg(globalMemUse).arg(globalMemSize));
426  return usingTooMuchMemory;
427 }
428 
429 void VNNclAlgorithm::measureAndExecuteKernel(cl::CommandQueue queue, cl::Kernel kernel, size_t global_work_size, size_t local_work_size, std::string measurementName)
430 {
431  this->startProfiling(measurementName, queue);
432  mOulContex->executeKernel(queue, kernel, global_work_size, local_work_size);
433  this->stopProfiling(measurementName, queue);
434 }
435 
436 void VNNclAlgorithm::measureAndReadBuffer(cl::CommandQueue queue, cl::Buffer outputBuffer, size_t outputVolumeSize, void *outputData, std::string measurementName)
437 {
438  this->startProfiling(measurementName, queue);
439  mOulContex->readBuffer(queue, outputBuffer, outputVolumeSize, outputData);
440  this->stopProfiling(measurementName, queue);
441 }
442 
443 void VNNclAlgorithm::startProfiling(std::string name, cl::CommandQueue queue) {
444  if(!mRuntime->isEnabled())
445  return;
446 
447  mRuntime->startCLTimer(name, queue);
448  mMeasurementNames.insert(name);
449 }
450 
451 void VNNclAlgorithm::stopProfiling(std::string name, cl::CommandQueue queue) {
452  if(!mRuntime->isEnabled())
453  return;
454 
455  mRuntime->stopCLTimer(name, queue);
456  mRuntime->printAll();
457 }
458 
459 
460 
461 } /* namespace cx */
QString qstring_cast(const T &val)
void reportError(QString msg)
Definition: cxLogger.cpp:92
virtual bool initCL(QString kernelFile, int nMaxPlanes, int nPlanes, int method, int planeMethod, int nStarts, float brightnessWeight, float newnessWeight)
Transform3D Transform3D
Transform3D is a representation of an affine 3D transform.
virtual void fillPlaneMatrices(float *planeMatrices, ProcessedUSInputDataPtr input)
virtual cl::Program buildCLProgram(std::string program_src, std::string kernelPath, int nMaxPlanes, int nPlanes, int method, int planeMethod, int nStarts, float brightnessWeight, float newnessWeight)
virtual bool reconstruct(ProcessedUSInputDataPtr input, vtkImageDataPtr outputData, float radius, int nClosePlanes)
virtual bool initializeFrameBlocks(frameBlock_t *framePointers, int numBlocks, ProcessedUSInputDataPtr inputFrames)
void setProfiling(bool on)
void report(QString msg)
Definition: cxLogger.cpp:90
void setDeepModified(vtkImageDataPtr image)
RealScalar length() const
boost::shared_ptr< class ProcessedUSInputData > ProcessedUSInputDataPtr
virtual void freeFrameBlocks(frameBlock_t *framePointers, int numBlocks)
vtkSmartPointer< class vtkImageData > vtkImageDataPtr