Efficient Execution of Multiple Query Workloads in Data Analysis Applications

Henrique Andrade; Tahsin Kurc; Alan Sussman; Joel Saltz

doi:10.1145/582034.582087

Efficient Execution of Multiple Query Workloads in Data Analysis Applications

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

6 Scopus citations

Abstract

Applications that analyze, mine, and visualize large datasets are considered an important class of applications in many areas of science, engineering, and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to clients. In this paper we present the design of a runtime system for executing multiple query workloads on a shared-memory machine. We describe experimental results using an application for browsing digitized microscopy images.

Original language	English
Title of host publication	Proceedings of the 2001 ACM/IEEE Conference on Supercomputing, SC 2001
Publisher	Association for Computing Machinery
Pages	53
Number of pages	1
ISBN (Electronic)	158113293X
DOIs	https://doi.org/10.1145/582034.582087
State	Published - Nov 10 2001
Event	2001 ACM/IEEE Conference on Supercomputing, SC 2001 - Denver, United States Duration: Nov 10 2001 → Nov 16 2001

Publication series

Name	Proceedings of the International Conference on Supercomputing

Conference

Conference	2001 ACM/IEEE Conference on Supercomputing, SC 2001
Country/Territory	United States
City	Denver
Period	11/10/01 → 11/16/01

Access to Document

10.1145/582034.582087

Cite this

@inproceedings{71ccdae8374a471994b7b48d277e556b,

title = "Efficient Execution of Multiple Query Workloads in Data Analysis Applications",

abstract = "Applications that analyze, mine, and visualize large datasets are considered an important class of applications in many areas of science, engineering, and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to clients. In this paper we present the design of a runtime system for executing multiple query workloads on a shared-memory machine. We describe experimental results using an application for browsing digitized microscopy images.",

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

note = "Publisher Copyright: {\textcopyright} 2001 ACM.; 2001 ACM/IEEE Conference on Supercomputing, SC 2001 ; Conference date: 10-11-2001 Through 16-11-2001",

year = "2001",

month = nov,

day = "10",

doi = "10.1145/582034.582087",

language = "English",

series = "Proceedings of the International Conference on Supercomputing",

publisher = "Association for Computing Machinery",

pages = "53",

booktitle = "Proceedings of the 2001 ACM/IEEE Conference on Supercomputing, SC 2001",

}

Andrade, H, Kurc, T, Sussman, A & Saltz, J 2001, Efficient Execution of Multiple Query Workloads in Data Analysis Applications. in Proceedings of the 2001 ACM/IEEE Conference on Supercomputing, SC 2001. Proceedings of the International Conference on Supercomputing, Association for Computing Machinery, pp. 53, 2001 ACM/IEEE Conference on Supercomputing, SC 2001, Denver, United States, 11/10/01. https://doi.org/10.1145/582034.582087

Efficient Execution of Multiple Query Workloads in Data Analysis Applications. / Andrade, Henrique; Kurc, Tahsin; Sussman, Alan et al.
Proceedings of the 2001 ACM/IEEE Conference on Supercomputing, SC 2001. Association for Computing Machinery, 2001. p. 53 (Proceedings of the International Conference on Supercomputing).

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

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AU - Kurc, Tahsin

AU - Sussman, Alan

AU - Saltz, Joel

PY - 2001/11/10

Y1 - 2001/11/10

N2 - Applications that analyze, mine, and visualize large datasets are considered an important class of applications in many areas of science, engineering, and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to clients. In this paper we present the design of a runtime system for executing multiple query workloads on a shared-memory machine. We describe experimental results using an application for browsing digitized microscopy images.

AB - Applications that analyze, mine, and visualize large datasets are considered an important class of applications in many areas of science, engineering, and business. Queries commonly executed in data analysis applications often involve user-defined processing of data and application-specific data structures. If data analysis is employed in a collaborative environment, the data server should execute multiple such queries simultaneously to minimize the response time to clients. In this paper we present the design of a runtime system for executing multiple query workloads on a shared-memory machine. We describe experimental results using an application for browsing digitized microscopy images.

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DO - 10.1145/582034.582087

M3 - Conference contribution

AN - SCOPUS:85129670221

T3 - Proceedings of the International Conference on Supercomputing

SP - 53

BT - Proceedings of the 2001 ACM/IEEE Conference on Supercomputing, SC 2001

PB - Association for Computing Machinery

T2 - 2001 ACM/IEEE Conference on Supercomputing, SC 2001

Y2 - 10 November 2001 through 16 November 2001

ER -

Efficient Execution of Multiple Query Workloads in Data Analysis Applications

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