Parallelizing Locally-Weighted Regression.

Abstract

This paper focuses on a nonparametric regression technique known as locally-weighted regression or LOESS, LOESS is a computationally intensive technique which makes it naturally amenable to exploiting high performance computers. In this paper, we explore domain decomposition techniques for LOESS and study the performance of our algorithm on an Intel Paragon XP/S A4 machine. We study both speedup and efficiency as a function of the number of nodes. Certain segments of the LOESS computation are shown to be fruitfully parallelized while others are essentially sequential and cannot be parallelized effectively.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1994
Accession Number
ADA288294

Entities

People

  • Edward Wegman
  • Julia C. Fauntleroy

Organizations

  • George Mason University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Acoustic Arrays
  • Algorithms
  • Classification
  • Computations
  • Computers
  • Efficiency
  • Environment
  • Estimators
  • Information Operations
  • Information Science
  • Military Research
  • New York
  • Parallel Computing
  • Regression Analysis
  • Statistical Analysis
  • Statistics

Fields of Study

  • Computer science

Readers

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