One size does not fit all: accelerating OLAP workloads with GPUs-Reference-Cited by-同舟云学术

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One size does not fit all: accelerating OLAP workloads with GPUs

Published:2020-07-31 Issue:4 Volume:38 Page:995-1037
ISSN:0926-8782
Container-title:Distributed and Parallel Databases
language:en
Short-container-title:Distrib Parallel Databases

Author:

Zhang Yansong,Zhang Yu,Lu Jiaheng,Wang Shan,Liu Zhuan,Han Ruichen

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Hardware and Architecture,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10619-020-07304-z.pdf

Reference36 articles.

1. Neumann, T., Leis, V.: Compiling database queries into machine code. IEEE Data Eng. Bull. 37(1), 3–11 (2014)

2. Open Source Analytical Database & SQL Engine. (2019). https://www.omnisci.com/platform/omniscidb

3. HGX-2 Fuses HPC and AI Computing Architectures. (2018). https://devblogs.nvidia.com/hgx-2-fuses-ai-computing/

4. Kinetica is the insight engine for the Extreme Data Economy. (2018). https://www.kinetica.com/

5. SQream DB is the GPU Data Warehouse for massive data. (2018). https://sqream.com/

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GPU-accelerated PostgreSQL for Scalable Management and Processing of Irregular Time-Series Data using SPI;2023 IEEE International Conference on Big Data (BigData);2023-12-15

2. Random Forests over normalized data in CPU-GPU DBMSes;Proceedings of the 19th International Workshop on Data Management on New Hardware;2023-06-18

3. Exploring Fine-Grained In-Memory Database Performance for Modern CPUs;IEEE Transactions on Parallel and Distributed Systems;2023

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