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2014 | OriginalPaper | Buchkapitel

GPU-Accelerated Database Systems: Survey and Open Challenges

verfasst von : Sebastian Breß, Max Heimel, Norbert Siegmund, Ladjel Bellatreche, Gunter Saake

Erschienen in: Transactions on Large-Scale Data- and Knowledge-Centered Systems XV

Verlag: Springer Berlin Heidelberg

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Abstract

The vast amount of processing power and memory bandwidth provided by modern graphics cards make them an interesting platform for data-intensive applications. Unsurprisingly, the database research community identified GPUs as effective co-processors for data processing several years ago. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this paper, we explore the design space of GPU-accelerated database management systems. Based on this survey, we present key properties, important trade-offs and typical challenges of GPU-aware database architectures, and identify major open challenges. Additionally, we survey existing GPU-accelerated DBMSs and classify their architectural properties. Then, we summarize typical optimizations implemented in GPU-accelerated DBMSs. Finally, we propose a reference architecture, indicating how GPU acceleration can be integrated in existing DBMSs.

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Nächstes Kapitel Compression Planner for Time Series Database with GPU Support
Fußnoten
1
Typically around 2–4 GB on mainstream cards and up to 16 GB on high-end devices.
 
2
We are aware that this features are included in OpenCL 2.0 but no OpenCL framework supports this features yet.
 
3
Some potential strategies include keeping the hot set of the data resident on the graphics card, or using the limited graphics card memory as a low-resolution data storage to quickly filter out non-matching data items [47].
 
4
Note that we deliberately excluded commercial systems such as Jedox [1] or Parstream [2], because they are neither available as open source nor have publications available that provide full architectural details.
 
9
Source code available at: http://​goo.​gl/​GHeUv.
 
12
Note that both systems still apply a block-oriented processing model. This is due to the nature of compilation-based strategies, as discussed in Sect. 3.
 
13
Since these models need to be able to estimate comparable operator runtimes across different devices, we and others [13] argue that dynamic cost models, which apply techniques from Machine Learning to adapt to the current hardware, are likely required here.
 
14
We are aware that some in-memory DBMSs can also store data row-oriented, such as HyPer [38]. However, in GDBMSs, row-oriented storage either increases the data volume to be transfered or requires a projection operation before the transfer. A row-oriented layout also makes it difficult to achieve optimal memory access patterns on a GPU.
 
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Metadaten
Titel
GPU-Accelerated Database Systems: Survey and Open Challenges
verfasst von
Sebastian Breß
Max Heimel
Norbert Siegmund
Ladjel Bellatreche
Gunter Saake
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Large-Scale Data- and Knowledge-Centered Systems XV

Print ISBN: 978-3-662-45760-3

Electronic ISBN: 978-3-662-45761-0

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-662-45761-0_1