Empirical evaluation across multiple GPU-accelerated DBMSes-Reference-Cited by-同舟云学术

Empirical evaluation across multiple GPU-accelerated DBMSes

Published:2020-06-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 16th International Workshop on Data Management on New Hardware
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Author:

Chu Hawon¹,Kim Seounghyun¹,Lee Joo-Young¹,Suh Young-Kyoon¹

Affiliation:

1. Kyungpook National University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3399666.3399907

Reference17 articles.

1. BlazingDB Inc. 2020. BlazingSQL: High Performance SQL Engine on RAPIDS AI. URL: https://blazingsql.com/. BlazingDB Inc. 2020. BlazingSQL: High Performance SQL Engine on RAPIDS AI. URL: https://blazingsql.com/.

2. The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS

3. Why it is time for a HyPE

4. Periklis Chrysogelos Panagiotis Sioulas and Anastasia Ailamaki. 2019. Hardware-conscious query processing in GPU-accelerated analytical engines. In CIDR. Periklis Chrysogelos Panagiotis Sioulas and Anastasia Ailamaki. 2019. Hardware-conscious query processing in GPU-accelerated analytical engines. In CIDR.

5. Emily Furst Mark Oskin and Bill Howe. 2017. Profiling a GPU Database Implementation: A Holistic View of GPU Resource Utilization on TPC-H Queries. In DAMON. Emily Furst Mark Oskin and Bill Howe. 2017. Profiling a GPU Database Implementation: A Holistic View of GPU Resource Utilization on TPC-H Queries. In DAMON.

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