Optimizing Memory-Resident Decision Support System Workloads for Cache Memories
Trancoso, Pedro P.M.
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/81936
Description
Title
Optimizing Memory-Resident Decision Support System Workloads for Cache Memories
Author(s)
Trancoso, Pedro P.M.
Issue Date
1998
Doctoral Committee Chair(s)
Torrellas, Josep
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
In the second part of this work cache optimizations are proposed for two database system components: algorithms and query optimizer. In the former, blocking and prefetching are applied to database algorithms. In the latter the first public domain cache-oriented query optimizer is proposed. This optimizer chooses the ordering of operations and implementation of those operations using the number of cache misses and the number of instructions as the metric. In an evaluation of the proposed optimizations using a real architecture, some complex queries show performance improvement over the existing optimizations. One query from a standard benchmark achieves 29% improvement while the average for five queries is 13%. While this improvement is moderate, these optimizations are implemented in the database system without any changes to the hardware. Therefore the proposed optimizations provide improvement at no additional cost. A sensitivity test showed that the improvement provided by the proposed optimizations is independent of changes to the cache configuration like cache size, line size, and miss penalty. Finally, the prefetching optimization doubles the performance improvement from 13% to 28% in average for all queries.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.