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

被引：7

作者：

Arefyeva, Iya ^{[1
]}

Broneske, David ^{[1
]}

Campero, Gabriel ^{[1
]}

Pinnecke, Marcus ^{[1
]}

Saake, Gunter ^{[1
]}

机构：

[1] Univ Magdeburg, Univ Pl 2, D-39106 Magdeburg, Germany

来源：

BEYOND DATABASES, ARCHITECTURES AND STRUCTURES: FACING THE CHALLENGES OF DATA PROLIFERATION AND GROWING VARIETY | 2018年 / 928卷

关键词：

Cross-device query processing; GPU memory management; Divide-and-conquer; Mapped memory; Unified Virtual Addressing; Unified Memory; GPU;

D O I：

10.1007/978-3-319-99987-6_10

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：128 / 142

页数：15

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