Difference between revisions of "ITK Registration Optimization"
From NAMIC Wiki
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*# '''DTI: components of the diffusion tensor''' [[DTI-non-rigid]] (Sylvain) | *# '''DTI: components of the diffusion tensor''' [[DTI-non-rigid]] (Sylvain) | ||
− | == | + | == Hardware Platform Requirements and Use Cases == |
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*# Intel quad-core Xeon processors (?) | *# Intel quad-core Xeon processors (?) | ||
*# 6 CPU Sun, Solaris 8 (SPL: vision) | *# 6 CPU Sun, Solaris 8 (SPL: vision) |
Revision as of 13:18, 30 March 2007
Home < ITK Registration OptimizationContents
Goals
There are two components to this research
- Identify registration algorithms that are suitable for non-rigid registration problems that are indemic to NA-MIC
- 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
- Intersubject mapping example data set (Kilian)
- fMRI to hi-res brain morphology mapping example data set (Steve Pieper)
- DTI: components of the diffusion tensor DTI-non-rigid (Sylvain)
Hardware Platform Requirements and Use Cases
- Requirements
- Shared memory
- Single and multi-core machines
- Single and multi-processor machines
- AMD and Intel - Windows, Linux, and SunOS
- Use-cases
- Intel Core2Duo
- 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)
Data
Workplan
- Quantify current performance and bottlenecks
- Identify timing tools (cross platform, multi-threaded)
- For each use-case
- Centralized data and provide easy access
- Identify relevant registration algorithm(s)
- Develop traditional ITK-style implementations
- Develop timing tests using implementations and data
- Across use-cases
- Identify ITK classes/functions common to implementations (e.g., interpolation/resampling)
- Develop timing tests specific to these common sub-classes
- Compute performance on multiple platforms
Progress Highlights
- Quantify current performance and bottlenecks
Related Pages
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