research-article

By their fruits shall ye know them: A Data Analyst's Perspective on Massively Parallel System Design

Authors:

Mike StonebrakerAuthors Info & Claims

DaMoN'15: Proceedings of the 11th International Workshop on Data Management on New Hardware

Article No.: 5, Pages 1 - 6

https://doi.org/10.1145/2771937.2771944

Published: 31 May 2015 Publication History

Abstract

Increasingly parallel systems promise a remedy for the current stagnation of single-core performance. However, the battle to find the most appropriate architecture for the resulting massively parallel systems is still ongoing. Currently, there are two active contenders: Massively Parallel Single Instruction Multiple Threads (SIMT) systems such as GPGPUs and Many Core Single Instruction Multiple Data (SIMD) systems such as Intel's Xeon Phi. While the former is more versatile, the latter is an efficient, time-tested technology with a clear migration path. In this study, we provide a data management perspective to the debate: we study the implementation and performance of a set of common data management operations on an SIMT device (an Nvidia GTX 780) and compare it to a Many Core SIMD system (an Intel Xeon Phi). We interpret the results to pinpoint architectural decisions and tradeoffs that lead to suboptimal performance and point out potential areas for improvement in the next generation of these devices.

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Cited By

Breβ SKöcher BFunke HZeuch SRabl TMarkl V(2018)Generating custom code for efficient query execution on heterogeneous processorsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0512-y27:6(797-822)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s00778-018-0512-y

Index Terms

By their fruits shall ye know them: A Data Analyst's Perspective on Massively Parallel System Design
1. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information retrieval
    1. Document representation

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cover image ACM Conferences

DaMoN'15: Proceedings of the 11th International Workshop on Data Management on New Hardware

May 2015

100 pages

ISBN:9781450336383

DOI:10.1145/2771937

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGMOD: ACM Special Interest Group on Management of Data

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Published: 31 May 2015

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SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

VIC, Melbourne, Australia

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DaMoN'15 Paper Acceptance Rate 12 of 16 submissions, 75%;

Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

Breβ SKöcher BFunke HZeuch SRabl TMarkl V(2018)Generating custom code for efficient query execution on heterogeneous processorsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0512-y27:6(797-822)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s00778-018-0512-y

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