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Exploring Spark-SQL-Based Entity Resolution Using the Persistence Capability Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Memory Management Strategies in CPU/GPU Database Systems: A Survey

Authors : Iya Arefyeva, David Broneske, Gabriel Campero, Marcus Pinnecke, Gunter Saake

Published in: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety

Publisher: Springer International Publishing

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Abstract

GPU-accelerated in-memory database systems have gained a lot of popularity over the last several years. However, GPUs have limited memory capacity, and the data to process might not fit into the GPU memory entirely and cause a memory overflow. Fortunately, this problem has many possible solutions, like splitting the data and processing each portion separately, or storing the data in the main memory and transferring it to the GPU on demand. This paper provides a survey of four main techniques for managing GPU memory and their applications for query processing in cross-device powered database systems.

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Footnotes
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Metadata
Title
Memory Management Strategies in CPU/GPU Database Systems: A Survey
Authors
Iya Arefyeva
David Broneske
Gabriel Campero
Marcus Pinnecke
Gunter Saake
Copyright Year
2018
Book
Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety

Print ISBN: 978-3-319-99986-9

Electronic ISBN: 978-3-319-99987-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99987-6_10

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