Scalable Data Parallel Algorithms and Implementations for Vision.

Abstract

Our research is about designing, analyzing and implementing scalable parallel solutions to problems in intermediate- and high-level vision. This is a difficult problem as computations are heterogeneous, symbolic and geometric in nature and use complex data structures such as lists and graphs. We propose a realistic model of distributed memory parallel machines which accurately models the features of a parallel machine. This includes the costs of communication-latency, impact of communication patterns on network congestion, available bandwidth and time for synchronization. We analyze the computation communication and control characteristics and the memory requirements of the vision algorithms. Our parallel algorithm achieve load balancing by dynamic redistribution of the tasks. We show the results of our approach in parallelizing the line finding problem on IBM SP-2 and a perceptual grouping step on TMC CM-5.

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Document Details

Document Type
Technical Report
Publication Date
May 16, 1995
Accession Number
ADA295584

Entities

People

  • Ramakant Nevatia
  • Viktor K. Prasanna

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • California
  • Change Detection
  • Computations
  • Computer Architecture
  • Computers
  • Computing System Architectures
  • Detection
  • Digital Communications
  • Electrical Engineering
  • Feature Extraction
  • Machine Perception
  • Parallel Computing
  • Parallel Processing
  • Pattern Recognition
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.