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  1. Proceedings of the 11th International Workshop on Data Management on New Hardware (DaMoN'15)
  2. Toward GPUs being mainstream in analytic processing: An initial argument using simple scan-aggregate queries
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Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies
Beyond the Wall: Near-Data Processing for Databases
Scaling the Memory Power Wall With DRAM-Aware Data Management
NUMA obliviousness through memory mapping
By their fruits shall ye know them: A Data Analyst's Perspective on Massively Parallel System Design
TLB misses: The Missing Issue of Adaptive Radix Tree?
Applying HTM to an OLTP System: No Free Lunch
The Serial Safety Net: Efficient Concurrency Control on Modern Hardware
Efficient Lightweight Compression Alongside Fast Scans
Energy-Efficient Query Processing on Embedded CPU-GPU Architectures
Toward GPUs being mainstream in analytic processing: An initial argument using simple scan-aggregate queries
Ultra-Fast Similarity Search Using Ternary Content Addressable Memory

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Toward GPUs being mainstream in analytic processing: An initial argument using simple scan-aggregate queries

Content Provider ACM Digital Library
Author Patel, Jignesh M. Wood, David A. Li, Yinan Power, Jason Hill, Mark D.
Abstract There have been a number of research proposals to use discrete graphics processing units (GPUs) to accelerate database operations. Although many of these works show up to an order of magnitude performance improvement, discrete GPUs are not commonly used in modern database systems. However, there is now a proliferation of integrated GPUs which are on the same silicon die as the conventional CPU. With the advent of new programming models like heterogeneous system architecture, these integrated GPUs are considered first-class compute units, with transparent access to CPU virtual addresses and very low overhead for computation offloading. We show that integrated GPUs significantly reduce the overheads of using GPUs in a database environment. Specifically, an integrated GPU is 3x faster than a discrete GPU even though the discrete GPU has 4x the computational capability. Therefore, we develop high performance scan and aggregate algorithms for the integrated GPU. We show that the integrated GPU can outperform a four-core CPU with SIMD extensions by an average of 30% (up to 3:2x) and provides an average of 45% reduction in energy on 16 TPC-H queries.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781450336383
DOI 10.1145/2771937.2771941
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-05-31
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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