Difference between revisions of "ITK Registration Optimization"

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# Quantify current performance and bottlenecks
 
# Quantify current performance and bottlenecks
 
## Identify timing tools
 
## Identify timing tools
−
##* We have chosen [http://www.cs.uoregon.edu/research/tau/home.php| TAU] for the performance quantification tool
+
##* We have chosen [http://www.cs.uoregon.edu/research/tau/home.php TAU] for the performance quantification tool
 
##* Summary of select performance quantification tools is available here
 
##* Summary of select performance quantification tools is available here
−
 
  
 
= Performance Measurement =
 
= Performance Measurement =

Revision as of 13:53, 7 January 2007

Home < 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.

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

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
    1. Identify timing tools
      • We have chosen TAU for the performance quantification tool
      • Summary of select performance quantification tools is available here

Performance Measurement

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