Performance Tuning on Multicore Systems for Feature Matching within Image Collections Xiaoxin Tang*, Steven Mills, David Eyers, Zhiyi Huang, Kai-Cheung Leung and Minyi Guo* Department of Computer Science University of Otago, New Zealand * Department of Computer Science Shanghai Jiao Tong University, China
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Performance Tuning on Multicore Systems for Feature Matching within Image Collections Xiaoxin Tang*, Steven Mills, David Eyers, Zhiyi Huang, Kai-Cheung.
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Performance Tuning on Multicore Systems for
Feature Matching within Image Collections
Xiaoxin Tang*, Steven Mills, David Eyers, Zhiyi Huang, Kai-Cheung Leung and Minyi Guo*
Department of Computer Science University of Otago, New Zealand
* Department of Computer ScienceShanghai Jiao Tong University, China
Contents
• Motivation• Our work• Evaluation• Conclusion
Contents
• Motivation• Our work• Evaluation• Conclusion
Similarity Search
• Definition:– To preprocess a database of N objects so that
given a query object, one can effectively determine its nearest neighbors in database.
• Applications:– pattern recognition, chemical similarity
analysis, and statistical classification, etc.
The problem – KNN Search
• K Nearest Neighbor Search:– Feature: an array of D elements
– Search: given a query feature fq, find k features in Fs so that they have the shortest distances to fq.
Our Case Study
• Feature Matching: a fundamental problem in many computer vision tasks– Use the SIFT algorithm to generate features for each image;– Use a k-Nearest Neighbors (k-NN) algorithm to find similar
features between images
Challenges
• Very time-consuming:– datasets become larger:
• hundreds or thousands of images;
– image resolution increases:• 2300×1500 pixels, or higher;
• New platforms: HPC turns to multi-/many-core age:
• AMD 16-core and 64-core machines.
Motivation
• Performance evaluation:– Find out common problems that may limit the
performance of feature matching on multi-/many-core platforms.
• Performance tuning:– Find general methods to solve the identified
• We have shown that performance tuning is demanding on modern multicore systems.
• We have comprehensively evaluated the impact of the three factors that have an influence on large-scale image feature matching.
• We have proposed a Divide-and-Merge algorithm that can greatly improve the speedup and scalability of feature matching algorithms on multicore machines.