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Efficient execution of multi-query data analysis batches using compiler optimization strategies

  • University of Maryland, College Park
  • Ohio State University

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

Abstract

This work investigates the leverage that can be obtained from compiler optimization techniques for efficient execution of multi-query workloads in data analysis applications. Our approach is to address multi-query optimization at the algorithmic level, by transforming a declarative specification of scientific data analysis queries into a high-level imperative program that can be made more efficient by applying compiler optimization techniques. These techniques - including loop fusion, common subexpression elimination and dead code elimination are employed to allow data and computation reuse across queries. We describe a preliminary experimental analysis on a real remote sensing application that analyzes very large quantities of satellite data. The results show our techniques achieve sizable reductions in the amount of computation and I/O necessary for executing query batches and in average execution times for the individual queries in a given batch.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsLawrence Rauchwerger
PublisherSpringer Verlag
Pages509-523
Number of pages15
ISBN (Print)9783540246442
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2958
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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