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  1. Proceedings of the 11th International Workshop on Data Management on New Hardware (DaMoN'15)
  2. Energy-Efficient Query Processing on Embedded CPU-GPU Architectures
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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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Energy-Efficient Query Processing on Embedded CPU-GPU Architectures

Content Provider ACM Digital Library
Author Lau, Chiew Tong Cheng, Xuntao He, Bingsheng
Abstract Energy efficiency is a major design and optimization factor for query co-processing of databases in embedded devices. Recently, GPUs of new-generation embedded devices have evolved with the programmability and computational capability for general-purpose applications. Such CPU-GPU architectures offer us opportunities to revisit GPU query co-processing in embedded environments for energy efficiency. In this paper, we experimentally evaluate and analyze the performance and energy consumption of a GPU query co-processor on such hybrid embedded architectures. Specifically, we study four major database operators as micro-benchmarks and evaluate TPC-H queries on CARMA, which has a quad-core ARM Cortex-A9 CPU and a NVIDIA Quadro 1000M GPU. We observe that the CPU delivers both better performance and lower energy consumption than the GPU for simple operators such as selection and aggregation. However, the GPU outperforms the CPU for sort and hash join in terms of both performance and energy consumption. We further show that CPU-GPU query co-processing can be an effective means of energy-efficient query co-processing in embedded systems with proper tuning and optimizations.
Starting Page 1
Ending Page 7
Page Count 7
File Format PDF
ISBN 9781450336383
DOI 10.1145/2771937.2771939
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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