creating a parallel program to compute statistical information victoria sensano maui scientific...
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Creating a Parallel Programto Compute
Statistical Information
Victoria Sensano
Maui Scientific Research Center
Research Supervisor: Douglas Hope
The Goal
To Automate the process of rating images
Where to start?
Understand how to define information in an image.
Benefits
•To test imaging systems
•Design and optimize imaging systems
How does one define information in an image
Based on the amount of information in the image Information is computed using ensembles of object and image scenes Based on how well one can associate an image with its object scene
Images from : http://hubblesite.org/gallery
Object Ensemble of
Spiral Galaxies
Given Image Scene
Associate
“Statistical Comparison”
Object and Image Ensembles
Object Ensemble Image Ensemble
Image provided by Boeing
Important Question to Address...
How many object and image scenes in the ensembles are required to make a good comparison?
Constraints:
• Limited memory
Solution:
• Break the problem up into pieces then combine
My Project
•Create a parallel program
- adapt existing single processor code to run in a parallel environment
•Create two Matlab programs
- divide the frequencies between processors
- combine frequencies to form information maps
•Evaluate performance within parallel environment
- Verify results obtained by the parallel program
How Programs Work Together
Distributes frequency values
Sets up parallel environment
Computes the information
Combines results
Matlab Program 1
Parallel Program
Existing Matlab Programs
Matlab Program 2
Three existing external programs used to create image and object ensembles
Distributes frequency valuesMatlab Program 1
Parallel Program
Existing Matlab Programs
Matlab Program 2
Three existing external Program used to create image and object ensembles
Matlab Program 1 -- Divide the Workload
•Distributes the work load by allowing the user to select the amount of processors to be used.
•This program divides the frequency values between the amount of processors selected for computation.
Select Amount of Processors
Frequency values between 1 and 100
Frequency values between 101 and 200
3 Processors
Frequency values between 201 and 300
Processor 1
Processor 2
Processor 3
Distributes frequency values
Sets up parallel environment
Matlab Program 1
Parallel Program
Existing Matlab Programs
Matlab Program 2
Three existing external Program used to create image and object ensembles
Parallel ProgramSets up the parallel environment using MPI and C to run the existing Matlab programs on the Huinalu Cluster at the Maui High Performance Computing Center.
Sends a message to each processor to
1) Open files that contain frequency values
2) Begins to open existing Matlab Program
Processor sends acknowledgment of receiving the message
Each node begins to compute information at its assigned frequency values
Distributes frequency values
Sets up parallel environment
Computes the information
Combines results
Matlab Program 1
Parallel Program
Existing Matlab Programs
Matlab Program 2
Three existing external Program used to create image and object ensembles
Matlab Program 2 -- Combine Results Combines results forming information maps Information Map - is a combination of
frequencies within its Fourier DomainSNR 5
SNR 10
SNR 15
SNR 20
Aperture 4cm Aperture 3cm
(4) Red color = more information at low frequencies (0) Blue color = less information at high frequencies
Evaluating Performance within the Parallel Environment
Pros:
• Faster Results than using a single processor to do computations
Cons:
Data Dependencies - use of the same storage location
Testing:
1) Design
2) Efficiency
Approaching its limit
Conclusion Project Achievements:
• Used MPI and C to run existing Matlab programs in a parallel environment
• Created Matlab programs to distribute workload over the processors and combine results
• Confirmed that Matlab programs performed as expected
• Evaluated the parallel program for its design and efficiency
Future Goals of Researchers:
• Use parallel implementation to compute information using large ensembles
• Numerically confirm theoretical predictions
• Use information to characterize the quality of an image
AcknowledgementsNational Science Foundation
Center for Adaptive Optics
Malika Bell, Lisa Hunter, Liz Esperanza and the CfAO instructors
Maui Scientific and Research Center
Douglas Hope and Stuart Jefferies
Maui Community College
Mark Hoffman and Wallette Pallegrino
Maui Economic and Development Board
Isla Yap and Leslie Wilkins
Satellite images : http://www.spaceimaging.com/default.htm
Funding provided through a Research Experiences for Undergraduates (REU) Supplement to the Center for AdaptiveOptics, a National Science Foundation Science and Technology Center (STC), AST-987683