Difference between revisions of "ITK Registration Optimization"

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## Compute performance on multiple platforms
 
## Compute performance on multiple platforms
  
−
= Progress Highlights =
+
= Benchmarks =
  
−
# Quantify current performance and bottlenecks
+
All tests cout two values
 +
* the time required
 +
* an measure of the error (0 = no error; 1 = 100% error)
 +
 
 +
Tests being developed and suggested parameter settings
 +
# LinearInterpTest <numThreads> <dimSize> <factor> [<outputImage>]
 +
#* NumThreads = 1, 2, 4, and #OfCoresIf>4
 +
#* DimSize = 100, 200 (i.e., 100^3 and 200^3 images)
 +
#* Factor = 1.5, 2, 3 (i.e., producing up to 600^3 images)
 +
#* = 24 tests (approx time on dual-core for all tests = 1.5 minutes)
 +
# BSplineInterpTest <numThreads> <dimSize> <factor> [<outputImage>]
 +
#* NumThreads = 1, 2, 4, and #OfCoresIf>4 (for every platform)
 +
#* DimSize = 100, 200 (meaning: 100^3 and 200^3 images)
 +
#* Factor = 1.5, 2, 3 (thereby producing up to 600^3 images)
 +
#* = 24 tests (approx time on dual-core for all tests = ??)
 +
# SincInterpTest <numThreads> <dimSize> <factor> [<outputImage>]
 +
# BSplineTransformLinearInterpTest <numThreads> <dimSize> <numNodesPerDim> [<outputImage>]
 +
# MeanReciprocalSquaredDifferenceMetricTest
 +
# MeanSquaresMetricTest
 +
# NormalizedCorreltationMetricTest
 +
# GradientDifferentMetricTest
 +
# MattesMutualInformationMetricTest
 +
# MutualInformationMetricTest
 +
# NormalizedMutualInformationMetricTest
 +
# MutualInformationHistogramMetricTest
 +
# NormaalizedMutualInformationHistogramMetricTest
  
 
= Related Pages =
 
= Related Pages =

Revision as of 13:27, 30 March 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
    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 (?)
    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

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

Benchmarks

All tests cout two values

  • the time required
  • an measure of the error (0 = no error; 1 = 100% error)

Tests being developed and suggested parameter settings

  1. LinearInterpTest <numThreads> <dimSize> <factor> [<outputImage>]
    • NumThreads = 1, 2, 4, and #OfCoresIf>4
    • DimSize = 100, 200 (i.e., 100^3 and 200^3 images)
    • Factor = 1.5, 2, 3 (i.e., producing up to 600^3 images)
    • = 24 tests (approx time on dual-core for all tests = 1.5 minutes)
  2. BSplineInterpTest <numThreads> <dimSize> <factor> [<outputImage>]
    • NumThreads = 1, 2, 4, and #OfCoresIf>4 (for every platform)
    • DimSize = 100, 200 (meaning: 100^3 and 200^3 images)
    • Factor = 1.5, 2, 3 (thereby producing up to 600^3 images)
    • = 24 tests (approx time on dual-core for all tests = ??)
  3. SincInterpTest <numThreads> <dimSize> <factor> [<outputImage>]
  4. BSplineTransformLinearInterpTest <numThreads> <dimSize> <numNodesPerDim> [<outputImage>]
  5. MeanReciprocalSquaredDifferenceMetricTest
  6. MeanSquaresMetricTest
  7. NormalizedCorreltationMetricTest
  8. GradientDifferentMetricTest
  9. MattesMutualInformationMetricTest
  10. MutualInformationMetricTest
  11. NormalizedMutualInformationMetricTest
  12. MutualInformationHistogramMetricTest
  13. NormaalizedMutualInformationHistogramMetricTest

Related Pages

Performance Measurement

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