Visual Search Optimization

Prashanth Thinakaran, Diana Guttman, Mahmut Taylan Kandemir, Meenakshi Arunachalam, Rahul Khanna, Praveen Yedlapalli, Narayan Ranganathan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter presents an image-matching application that can take advantage of many-core architectures. Different parallelization strategies are explored that can take advantage of inter- and intraimage parallelism. The two main metrics that determine the application performance, tree creation time and search time, were studied in the context of scalability. Important insights obtained from a profiler-based analysis help identify the challenges in scalability of DB threads. The scalability with respect to increasing DBThreads with optimal KD-trees is shown to lead to 5.8× speedup in create time and 2.8× speedup in search time in the case of 120 threads when compared to single-threaded Xeon Phi performance.

Original languageEnglish (US)
Title of host publicationHigh Performance Parallelism Pearls
Subtitle of host publicationMulticore and Many-core Programming Approaches
PublisherElsevier Inc.
Pages191-209
Number of pages19
Volume2
ISBN (Electronic)9780128038901
ISBN (Print)9780128038192
DOIs
StatePublished - Jul 23 2015

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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    Thinakaran, P., Guttman, D., Kandemir, M. T., Arunachalam, M., Khanna, R., Yedlapalli, P., & Ranganathan, N. (2015). Visual Search Optimization. In High Performance Parallelism Pearls: Multicore and Many-core Programming Approaches (Vol. 2, pp. 191-209). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-803819-2.00021-5