Research on OLAP query processing technology for asymmetric in-memory computing platform

ZHANG Yan-song; ZHANG Yu; ZHOU Xuan; WANG Shan

doi:10.3969/j.issn.1000-5641.2016.05.011

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Nov. 2016

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ZHANG Yan-song, ZHANG Yu, ZHOU Xuan, WANG Shan. Research on OLAP query processing technology for asymmetric in-memory computing platform[J]. Journal of East China Normal University (Natural Sciences), 2016, (5): 89-102. doi: 10.3969/j.issn.1000-5641.2016.05.011

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ZHANG Yan-song, ZHANG Yu, ZHOU Xuan, WANG Shan. Research on OLAP query processing technology for asymmetric in-memory computing platform[J]. Journal of East China Normal University (Natural Sciences), 2016, (5): 89-102. doi: 10.3969/j.issn.1000-5641.2016.05.011

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Research on OLAP query processing technology for asymmetric in-memory computing platform

doi: 10.3969/j.issn.1000-5641.2016.05.011

1.
DEKE Lab, Renmin University of China, Beijing 100872, China;
2.
School of Information, Renmin University of China, Beijing 100872, China;
3.
National Survey Research Center at Renmin University of China, Beijing 100872, China;4. National Satellite Meteorological Center, Beijing 100081, China

Received Date: 2016-06-27
Publish Date: 2016-09-25

Abstract

Abstract

This paper proposes an OLAP query processing technology for nowadays and future asymmetric in-memory computing platform. Asymmetric in-memorycomputing platform means that computer equips with different computing feature processors and different memory access devices so that the OLAP processing model needs to be optimized fordifferent computing features and implementation designs to enable the different processing stages to adapt to the characteristics of corresponding storage and computing hardware for higher hardware utilization and performance. This paper proposes the 3-stage OLAP computing model, which divides the traditional iterative processing model into computing intensive and data intensive workloads to be assigned to general purpose processor with full fledged functions and coprocessor with powerful parallel processing capacity. The data transmission overhead between different storage and computing devices is also minimized. The experimental results show that the 3-stage OLAP computing model based on workload partitioning can be adaptive to CPU-Phi asymmetric computing platform, the acceleration on OLAP query processing can be achieved by accelerating computing intensive workload by computing intensive hardware.
- in-memory computing asymmetric computing platform in-memory OLAP,

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References(1)

References

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