By Topic

Improving In-memory Column-Store Database Predicate Evaluation Performance on Multi-core Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hong Min ; IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA ; Franke, H.

The ability to analyze a large volume of data for the purpose of business intelligence has led to various innovations in database technology. One example is the increased interest of using column-oriented data layout to address query performance in analytical and warehousing workloads. As system architectures move towards multi-core designs, it is important to address optimizing performance for these workloads on these platforms. In this paper we present SPHINX, an architecture that utilizes multi-core systems for search-based predicate evaluation operations in analytical query workloads against in-memory column store. We discuss the natural parallelism of predicate evaluations and various bottlenecks that impact search performance. We present several performance improvement techniques and apply a scan sharing technique based on cache reuse efficiency to further improve the performance. We demonstrate the performance benefits of our scan sharing scheduler over other scheduling approaches in a workload of mixed search queries.

Published in:

Computer Architecture and High Performance Computing (SBAC-PAD), 2010 22nd International Symposium on

Date of Conference:

27-30 Oct. 2010