Parallelization of Classification Algorithms For Medical Imaging on a Cluster Computing System 指導教授 : 梁廷宇 老師 系所 : 碩光通一甲 姓名 : 吳秉謙 學號 :

Introduction Single Program Multiple Data (SPMD) parallel implementation of image classification algorithms on a cluster comprised of personal computers 2 different Application Programming Interfaces (APls) 3 Image Classification Algorithms

The current trend in parallel processing is towards low cost highly available cluster computers constructed from networking individual systems. Such small-scale clusters offer many advantages: 1.inexpensive to assemble 2.easy to maintain 2.easy to maintain 3.extremely fault-tolerant 3.extremely fault-tolerant 4. surprisingly powerful and relatively easy to 4. surprisingly powerful and relatively easy to program program

Image Classification System Goals 1.The processing a continuous stream of image data 2. 2.Data re-configurable during run-time for varying size images and image sets 3. 3.process re-configurable so that individual processes and processor nodes could be added, deleted, or modified to reflect changes in the processing environment 4. 4.process re-configurable so that different classifications operations could be performed 5. 5.conducive to software modification, extension and rapid application development

Data Scheduling Each Worker process is given an approximately equal size portion of the image to operate on A Worker’s image partition is distributed by the Manager to the Worker in row-by-row fashion, through individual messages.

Object-oriented design

For MPI and Paradise 8 PC platforms (Pentium 166MHz systems) 32 MB running Microsoft Windows NT 25Mbs ATM switch and lOMbs Ethernet

Context-Independent Image Classifier Performance Nearest Mean Maximum Likelihood K Nearest Neighbors

Nearest Mean

Maximum Likelihood

K Nearest Neighbors

Sample Images

Conclusions We have taken 3 algorithms (Nearest Mean ； Maximum Likelihood ; K Nearest Neighbors )and parallelized them using both message passing(MPI) and virtual shared memory (Paradise) communications on a small-scale cluster. Both MPI and Paradise generate an overhead but can still be effectively used if the algorithm computational burden is large enough to compensate for additional communications cost. Paradise far out performed MPI for the classifiers developed, under the hardware and software suite utilized. The small-scale cluster also proved to be reliable and highly effective environment.

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