Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries

Henrique Andrade; Tahsin Kurc; Alan Sussman; Joel Saltz

doi:10.1109/IPDPS.2003.1213184

Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries

Henrique Andrade
, Tahsin Kurc
, Alan Sussman
, Joel Saltz

Biomedical Informatics

University of Maryland, College Park

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Reuse is a powerful method for increasing system performance. In this paper, we examine functional decomposition for improving data and computation reuse and, therefore, overall query execution performance in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated by a query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using functional decomposition and projection primitives.

Original language	English
Title of host publication	Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	0769519261, 9780769519265
DOIs	https://doi.org/10.1109/IPDPS.2003.1213184
State	Published - 2003
Event	International Parallel and Distributed Processing Symposium, IPDPS 2003 - Nice, France Duration: Apr 22 2003 → Apr 26 2003

Publication series

Name	Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003

Conference

Conference	International Parallel and Distributed Processing Symposium, IPDPS 2003
Country/Territory	France
City	Nice
Period	04/22/03 → 04/26/03

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10.1109/IPDPS.2003.1213184

Cite this

Andrade, H., Kurc, T., Sussman, A., & Saltz, J. (2003). Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries. In Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003 Article 1213184 (Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPS.2003.1213184

Andrade, Henrique ; Kurc, Tahsin ; Sussman, Alan et al. / Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries. Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003. Institute of Electrical and Electronics Engineers Inc., 2003. (Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003).

@inproceedings{d2031d8cbc554e018baa32d9ccc57300,

title = "Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries",

abstract = "Reuse is a powerful method for increasing system performance. In this paper, we examine functional decomposition for improving data and computation reuse and, therefore, overall query execution performance in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated by a query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using functional decomposition and projection primitives.",

author = "Henrique Andrade and Tahsin Kurc and Alan Sussman and Joel Saltz",

note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; International Parallel and Distributed Processing Symposium, IPDPS 2003 ; Conference date: 22-04-2003 Through 26-04-2003",

year = "2003",

doi = "10.1109/IPDPS.2003.1213184",

language = "English",

series = "Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003",

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Andrade, H, Kurc, T, Sussman, A & Saltz, J 2003, Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries. in Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003., 1213184, Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003, Institute of Electrical and Electronics Engineers Inc., International Parallel and Distributed Processing Symposium, IPDPS 2003, Nice, France, 04/22/03. https://doi.org/10.1109/IPDPS.2003.1213184

Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries. / Andrade, Henrique; Kurc, Tahsin; Sussman, Alan et al.
Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003. Institute of Electrical and Electronics Engineers Inc., 2003. 1213184 (Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries

AU - Andrade, Henrique

AU - Kurc, Tahsin

AU - Sussman, Alan

AU - Saltz, Joel

PY - 2003

Y1 - 2003

N2 - Reuse is a powerful method for increasing system performance. In this paper, we examine functional decomposition for improving data and computation reuse and, therefore, overall query execution performance in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated by a query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using functional decomposition and projection primitives.

AB - Reuse is a powerful method for increasing system performance. In this paper, we examine functional decomposition for improving data and computation reuse and, therefore, overall query execution performance in the context of data analysis applications. Additionally, we look at the performance effects of using various projection primitives that make it possible to transform intermediate results generated by a query so that they can be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using functional decomposition and projection primitives.

UR - https://www.scopus.com/pages/publications/84947260150

U2 - 10.1109/IPDPS.2003.1213184

DO - 10.1109/IPDPS.2003.1213184

M3 - Conference contribution

AN - SCOPUS:84947260150

T3 - Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003

BT - Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Parallel and Distributed Processing Symposium, IPDPS 2003

Y2 - 22 April 2003 through 26 April 2003

ER -

Andrade H, Kurc T, Sussman A, Saltz J. Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries. In Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003. Institute of Electrical and Electronics Engineers Inc. 2003. 1213184. (Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003). doi: 10.1109/IPDPS.2003.1213184

Exploiting functional decomposition for efficient parallel processing of multiple data analysis queries

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