DBMSs on a Modern Processor: Where Does Time Go?

@inproceedings{DBLP:conf/vldb/AilamakiDHW99,
  author    = {Anastassia Ailamaki and
               David J. DeWitt and
               Mark D. Hill and
               David A. Wood},
  editor    = {Malcolm P. Atkinson and
               Maria E. Orlowska and
               Patrick Valduriez and
               Stanley B. Zdonik and
               Michael L. Brodie},
  title     = {DBMSs on a Modern Processor: Where Does Time Go?},
  booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
               Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
               UK},
  publisher = {Morgan Kaufmann},
  year      = {1999},
  isbn      = {1-55860-615-7},
  pages     = {266-277},
  ee        = {db/conf/vldb/AilamakiDHW99.html},
  crossref  = {DBLP:conf/vldb/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Recent high-performance processors employ sophisticated techniques to overlap and simultaneously execute multiple computation and memory operations. Intuitively, these techniques should help database applications, which are becoming increasingly compute and memory bound. Unfortunately, recent studies report that faster processors do not improve database system performance to the same extent as scientific workloads. Recent work on database systems focusing on minimizing memory latencies, such as cache-conscious algorithms for sorting and data placement, is one step toward addressing this problem. However, to best design high performance DBMSs we must carefully evaluate and understand the processor and memory behavior of commercial DBMSs on today's hardware platforms.

In this paper we answer the question "Where does time go when a database system is executed on a modern computer platform?" We examine four commercial DBMSs running on an Intel Xeon and NT 4.0. We introduce a framework for analyzing query execution time on a DBMS running on a server with a modern processor and memory architecture. To focus on processor and memory interactions and exclude effects from the I/O subsystem, we use a memory resident database. Using simple queries we find that database developers should (a) optimize data placement for the second level of data cache, and not the first, (b) optimize instruction placement to reduce first-level instruction cache stalls, but (c) not expect the overall execution time to decrease significantly without addressing stalls related to subtle implementation issues (e.g., branch prediction).

Copyright © 1999 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Referenced by

1. Jun Rao, Kenneth A. Ross: Making B⁺-Trees Cache Conscious in Main Memory. SIGMOD Conference 2000: 475-486