ITK Registration Optimization

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Summary

Goals

There are two components to this research

  1. Identify registration algorithms that are suitable for non-rigid registration problems that are endemic to NA-MIC
  2. Develop implementations of those algorithms that take advantage of multi-core and multi-processor hardware

Steps involved

  1. Modify ITK's registration framework to support oriented images
  2. Modify ITK's registration framework to be thread safe
  3. Develop multi-threaded versions of select registration modules
  4. Make everything backward compatible with ITK's existing registration methods and framework
  5. Deliver in ITK

Target date for these deliverables: Jan 1, 2008

Planned follow-on work

  1. Implement helper-classes in ITK that combine common and effective registration components
    • e.g., b-spline deformable registration using LBFSGB optimizer and Mattes MI metric
  2. Deliver b-spline deformable registration using LBFSGB optimizer and Mattes MI metric as a multi-threaded Slicer modules

Target date for the follow-on work: Jan 15, 2008

Status and News

  1. Have developed mult-threaded registration metrics in ITK
    • Lead to the discovery that ITK's registration framework was not thread safe.
    • Making ITK's registration framework thread safe is conceptually a bug fix for ITK.
    • The incomplete implementation of oriented images in ITK has greatly extended the time and effort needed for this project.
      • Fixing this must be done in a manner that maintains ITK's backward compatibility.
      • This is a major effort involving approximately 50,000 lines of new code and over 400 new tests in ITK.
      • We have chosen to spend the time to integrate with ITK because it will serve the broader community, it will benefit from the support of the broader community, it will avoid having to incorporate another SVN checkout into Slicer's build process, and it will keep us from having to maintain and monitor separate dashboards for this effort.
  2. Weekly tcons, Monday, 10am
    • Luis Ibanez, Matt Turek, Stephen Aylward
  3. Active proposal to the ITK community:
  4. Project plan
  5. IJ article on oriented images and registration in ITK

Publications

  1. Aylward, Stephen; Jomier, Julien; Barre, Sebastien; Davis, Brad; Ibanez, Luis, "Optimizing ITK’s Registration Methods for Multi-processor, Shared-Memory Systems." MICCAI Open Source and Open Data Workshop, 2007 (Download PDF)

Quick Links

  1. Dashboard for this project
  2. Dashboard for BatchMake
  3. Batchboard (nightly experiment results) for this project
  4. BWH Neuroimaging Analysis Center (NAC), 2007-2008: Grid Enabled ITK

Algorithmic Requirements and Use Cases

  • Requirements
    1. relatively robust, with few parameters to tweak
    2. runs on grey scale images
    3. has already been published
    4. relatively fast (ideally speaking a few minutes for volume to volume).
    5. not patented
    6. can be implemented in ITK and parallelized.

Hardware Platform Requirements and Use Cases

  • Requirements
    1. Shared memory
    2. Single and multi-core machines
    3. Single and multi-processor machines
    4. AMD and Intel - Windows, Linux, and SunOS
  • Use-cases
    1. Intel Core2Duo
    2. Intel quad-core Xeon processors, Visual Studio 8, Windows Vista (Kitware: redwall)
    3. 6 CPU Sun, Solaris 8 (SPL: vision)
    4. 12 CPU Sun, Solaris 8 (SPL: forest and ocean)
    5. 16 core Opteron (SPL: john, ringo, paul, george)
    6. 16 core, Sun Fire, AMDOpteron (UNC: Styner)

Data

  • Now distributed with CVS

Workplan

Establish testing and reporting infrastructure

  1. Identify timing tools
    1. Cross platform and multi-threaded
    2. Timing and profiling
  2. Develop performance dashboard for collecting results
    1. Each test will report time and accuracy to a central server
    2. The performance of a test, over time, for a given platform can be viewed on one page
    3. The performance of a set of tests, at one point in time, for all platforms can be viewed on one page


Develop tests

  1. Develop modular tests
  2. Develop complete registration solutions for use cases


ITK Optimization

  • Target bottlenecks
    • Multi-thread metric calculation
      • Initial target is MattesMutualInformationImageToImageMetric
    • Optimize code
      • Sacrifice some memory and algorithm initialization speed to gain algorithm operation speed increases
      • Call multi-threaded functions when possible
  • Integrate metrics with transforms and interpolators for tailored performance

Example Results: MattesMutualInformationImageToImageMetric

Example of Optimizations Employed

  • GetValue
    • Added multi-threading to GetValue function
      • Partitions the samples - thereby distributes the computation of the transforms and interpolations across threads
      • Added the pre-computation of the FixedImageMarginalPDF for the sample to reduce the need for the thread mutex lock
        • Required the concept of an AdjustedFixedImageMarginalPDF that is updated when a fixed image voxel does not map into the moving image and thereby isn't valid for the current computations. By only updating when samples are missed, mutex lock to update a cross-thread data structure is needed less often.
      • Each thread now has its own copy of the joinPDF. After threads complete, jointPDFs from each thread are summed. This eliminates mutex from the main loop over samples.

Results

  • Speedup on a dual-core system is about 30% (reduction in computation time) when using linear transform and linear interpolation and about 45% when using bspline transform and bspline interpolation.

Performance Testing Results

GetValue Test at Identity Parameters

   // Print out a line with the test information
   std::cout << "GetValue2,";
   std::cout << metric->GetNameOfClass() << "," << interpolator->GetNameOfClass();
   std::cout << "," << transform->GetNameOfClass(); 

   // Make a time probe
   itk::TimeProbe timeProbe;

   // Run at the identity transform parameters.
   unsigned int numIters = 100;
   timeProbe.Start();
   for (unsigned int iter = 0; iter < numIters; iter++)
     {
     value = metric->GetValue( identityParameters );
     }
   timeProbe.Stop();

   // Print out the number of samples
   std::cout << "," << metric->GetNumberOfPixelsCounted();
   // Print out the time result.
   std::cout << "," << timeProbe.GetMeanTime()/numIters << std::endl;

GetValueAndDerivative Test at Identity Parameters

   // Print out a line with the test information
   std::cout << "GetValueAndDerivative2,";
   std::cout << metric->GetNameOfClass() << "," << interpolator->GetNameOfClass();
   std::cout << "," << transform->GetNameOfClass();

   // Make a time probe
   itk::TimeProbe timeProbe;

   // Evaluate at the identity transform;
   unsigned int numIters = 100;
   timeProbe.Start();
   for (unsigned int iter = 0; iter < numIters; iter++)
     {
     metric->GetValueAndDerivative( identityParameters, value, derivative );
     }
   timeProbe.Stop();

   // Print out the number of samples
   std::cout << "," << metric->GetNumberOfPixelsCounted();
   // Print out the time result.
   std::cout << "," << timeProbe.GetMeanTime()/numIters << std::endl;

Preliminary Results

January 5, 2008 - Note: "Opt" results are not using the OptLinearInterpolateImageFunction.

Events

Related Pages

Performance Measurement

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