Data management systems on GPUs: promises and challenges

Authors:
Yi-Cheng Tu;Anand Kumar;Di Yu;Ran Rui;Ryan Wheeler
Affiliations:
University of South Florida, Tampa;University of South Florida, Tampa;University of South Florida, Tampa;University of South Florida, Tampa;University of South Florida, Tampa
Venue:
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Year:
2013

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Abstract

The past decade has witnessed the popularity of push-based data management systems, in which the query executor passively receives data from either remote data sources (e.g., sensors) or I/O processes that scan database tables/files from local storage. Unlike traditional relational database management system (RDBMS) architectures that are mostly I/O-bound, push-based database systems often become heavily computation-bound since the data arrival rate could be very high. In this paper, we argue that modern multi-core hardware, especially Graphics Processing Units (GPU), provide the most cost-effective computing platform to catch up with the large amount of data streamed into a push-based database system. Based on that, we will open discussions on how to design and implement a query processing engine for such systems that run on GPUs.