ITK Registration Optimization

Goals

There are two components to this research

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

Algorithmic Requirements and Use Cases

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

• Use-cases (Applications)
  1. Intersubject mapping example data set (Kilian)
  2. fMRI to hi-res brain morphology mapping example data set (Steve Pieper)
  3. DTI: components of the diffusion tensor DTI-non-rigid (Sylvain)

• Use-cases (Platforms)
  • Single and multi-core machines
  • Single and multi-processor machines
  • AMD and Intel - Windows, Linux, and SunOS
    • Intel quad-core Xeon processors (?)
    • 6 CPU Sun, Solaris 8 (SPL: vision)
    • 12 CPU Sun, Solaris 8 (SPL: forest and ocean)
    • 16 core Opteron (SPL: john, ringo, paul, george)
    • 16 core, Sun Fire, AMDOpteron (UNC: Styner)

• Related NA-MIC wiki pages
  • Non Rigid Registration
  • Slicer3:Performance_Analysis

Performance Requirements and Use Cases

• Requirements
  • Single and multi-core machines
  • Single and multi-processor machines
  • AMD and Intel - Windows, Linux, and SunOS

• Use-cases
  • <list specific machines here>

Data

• NA-MIC MIDAS collection for this project

Workplan

1. Quantify current performance and bottlenecks
   1. Identify timing tools (cross platform, multi-threaded)
   2. For each use-case
      1. Centralized data and provide easy access
      2. Identify relevant registration algorithm(s)
      3. Develop traditional ITK-style implementations
      4. Develop timing tests using implementations and data
   3. Across use-cases
      1. Identify ITK classes/functions common to implementations (e.g., interpolation/resampling)
      2. Develop timing tests specific to these common sub-classes
   4. Compute performance on multiple platforms

Progress Highlights

1. Quantify current performance and bottlenecks

User Contributions

• User:Barre/ITK Registration Optimization

Performance Measurement

• Intel's VTune for Linux ($)
• TAU
• Threadmon: Thread usage/blockage
• TotalView ($)
• PerfSuite (POSIX Threads)
• GProf work-around for multi-threaded apps
• References on multi-threaded profiling and code optimization
  • General C++ performance optimization
  • Multi-threaded performance measurement (pdf document)
  • Wikipedia - General performance analysis