Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81700
Title: A Gaussian set sampling model for efficient shared cache profiling on multi-cores
Authors: Zhang, Y
Ling, ZW
Lv, MS
Guan, N 
Keywords: Gaussian distribution
Multi-core
Shared cache
Set sampling
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 115560-115567 How to cite?
Journal: IEEE access 
Abstract: The last level cache (LLC) has significant impact to system performance on modern multi-core processors. But as cache sizes reach several megabytes and more, the overhead of exploring performance on LLC greatly increases as well. To improve the efficiency of performance analysis, we propose a setsampling-based cache profiling model for the performance analysis on multi-core LLC. We first explore the memory access distributions on LLC by developing a low-overhead stress-application-based method. The results show that memory access distributions can be approximated by Gaussian distribution function. Based on this observation, a Gaussian-distribution-based set sampling model is proposed which can predict program performance with limited representative samples. We evaluate our model on a contemporary multicore machine and show that 1) the proposed method can precisely predict program performance on LLC under different contention intensities and 2) our method can achieve similar precision with less samples compared to widely adopted set sampling methods such as the random sampling and the continuous address sampling.
URI: http://hdl.handle.net/10397/81700
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2936439
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0
The following publication Y. Zhang, Z. Ling, M. Lv and N. Guan, "A Gaussian Set Sampling Model for Efficient Shared Cache Profiling on Multi-Cores," in IEEE Access, vol. 7, pp. 115560-115567, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2936439
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