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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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