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Wayne Young

dblp:181/5845 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 77% Indexing and storage engines · 23%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization › query execution › hardware-accelerated query processing
GPU-accelerated query processing
0.212016
Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration · SIGMOD Conference 2016
Indexing and storage engines
column store
0.112016
Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration · SIGMOD Conference 2016
GPUs and heterogeneous computing
GPU computing
0.112016
Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration · SIGMOD Conference 2016

Methods — techniques the papers use, named apart from their topics

GPU kernels · 0.5
YearPublicationVenuePosition
2016 Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration
abstract
In this paper, we show how we use Nvidia GPUs and host CPU cores for faster query processing in a DB2 database using BLU Acceleration (DB2's column store technology). Moreover, we show the benefits and problems of using hardware accelerators (more specifically GPUs) in a real commercial Relational Database Management System(RDBMS).We investigate the effect of off-loading specific database operations to a GPU, and show how doing so results in a significant performance improvement. We then demonstrate that for some queries, using just CPU to perform the entire operation is more beneficial. While we use some of Nvidia's fast kernels for operations like sort, we have also developed our own high performance kernels for operations such as group by and aggregation. Finally, we show how we use a dynamic design that can make use of optimizer metadata to intelligently choose a GPU kernel to run. For the first time in the literature, we use benchmarks representative of customer environments to gauge the performance of our prototype, the results of which show that we can get a speed increase upwards of 2x, using a realistic set of queries.
Sina Meraji, Berni Schiefer, Lan Pham, Lee Chu, Peter Kokosielis, Adam J. Storm, Wayne Young, Chang Ge 0002, Geoffrey Ng, Kajan Kanagaratnam
SIGMOD Conference7