Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81886
Title: In-cache query co-processing on coupled CPU-GPU architectures
Authors: He, Jiong
Zhang, Shuhao
He, Bingsheng
Keywords: Memory architecture
Query processing
Issue Date: 2014
Source: He, J., Zhang, S., & He, B. (2014). In-cache query co-processing on coupled CPU-GPU architectures. Proceedings of the VLDB Endowment, 8(4), 329-340. doi: 10.14778/2735496.2735497
Series/Report no.: Proceedings of the VLDB Endowment
Abstract: Recently, there have been some emerging processor designs that the CPU and the GPU (Graphics Processing Unit) are integrated in a single chip and share Last Level Cache (LLC). However, the main memory bandwidth of such coupled CPU-GPU architectures can be much lower than that of a discrete GPU. As a result, current GPU query co-processing paradigms can severely suffer from memory stalls. In this paper, we propose a novel in-cache query co-processing paradigm for main memory On-Line Analytical Processing (OLAP) databases on coupled CPU-GPU architectures. Specifically, we adapt CPU-assisted prefetching to minimize cache misses in GPU query co-processing and CPU-assisted decompression to improve query execution performance. Furthermore, we develop a cost model guided adaptation mechanism for distributing the workload of prefetching, decompression, and query execution between CPU and GPU. We implement a system prototype and evaluate it on two recent AMD APUs A8 and A10. The experimental results show that 1) in-cache query co-processing can effectively improve the performance of the state-of-the-art GPU co-processing paradigm by up to 30% and 33% on A8 and A10, respectively, and 2) our workload distribution adaption mechanism can significantly improve the query performance by up to 36% and 40% on A8 and A10, respectively.
URI: https://hdl.handle.net/10356/81886
http://hdl.handle.net/10220/39709
ISSN: 21508097
DOI: 10.14778/2735496.2735497
Schools: School of Computer Engineering 
Rights: © 2014 VLDB Endowment. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles from this volume were invited to present their results at the 41st International Conference on Very Large Data Bases, August 31st - September 4th 2015, Kohala Coast, Hawaii.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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