Dynamic recognition prefetch engine for DRAM-PCM hybrid main memory
Pages 1885 - 1902
Abstract
This research is to design an effective prefetching method required for hybrid main memory systems consisting of dynamic random-access memory (DRAM) and phase-change memory (PCM) components, which can be especially used for big data applications and massive-scale computing environment. Conventional prefetchers perform adequately for regular memory access patterns. However, graph processing applications show extremely irregular memory access characteristics, causing some difficulty in predicting accurate prefetching operation. Therefore, an effective dynamical prefetching algorithm based on the regression method is proposed in this study. We have designed an intelligent prefetch engine that can identify any dynamic accessing characteristics in memory accessing sequences. Specifically, it can select regular, linear, or polynomial regression predictive analysis based on the memory access sequence characteristics, and also dynamically determine the number of pages required for any selected prefetching. We also present a DRAM-PCM hybrid memory structure that can reduce the energy consumption and resolve the thermal issue that hampers conventional DRAM memory systems. Experimental results indicate that the performance can increase by around 40%, compared to that of conventional DRAM memory structures.
References
[1]
Hasso Plattner and Zeier A In-Memory Data Management 2011 Germany Spriger
[2]
Jiang T, Zhang Q, Hou R, Chai L, McKee SA, Jia Z, Sun N (2014) Understanding the behavior of in-memory computing workloads. In: IEEE International Symposium on Workload Characterization (IISWC), pp. 22–30
[3]
Yun Ji-Tae et al. Regression Prefetcher with Preprocessing for DRAM-PCM Hybrid Main Memory IEEE Comput Architect Lett 2018 17 2 163-166
[4]
Qureshi MK et al (2009) Scalable high performance main memory system using phase-change memory technology. In: Proc 36th Annu Int Symp Comput Archit, pp. 24–33
[5]
Yoon S-K et al. Optimized memory-disk integrated system with DRAM and nonvolatile memory IEEE Trans MultiScale Comput Syst 2016 2 2 83-93
[6]
Nesbit KJ and Smith JE (2004) Data cache prefetching using a global history buffer. In: Proc. Int Symp. High Perform. Comput Archit, pp. 96–105
[7]
Fu JWC et al (1992) Stride directed prefetching in scalar processors. In: Proc. IEEE/ACM Int. Symp. Microarchitecture, pp. 102–110
[8]
Nai L et al (2015) GraphBIG: understanding graph computing in the context of industrial solutions. In: Proc. Int. Conf. High Perform. Comput. Netw. Storage Anal., pp. 1–12
[9]
Somogyi S et al (2006) Spatial memory streaming. In: Proc Int Symp Comput Archit, pp. 252–263
[10]
Yuan LIANG, Jing-ling YUAN, and Min-cheng CHEN Prefetching algorithm of Sarsa learning based on space optimization Comput Sci 2019 46 3 327-331
[11]
Zhuang X and Lee H-HS (2003) A hardware-based cache pollution filtering mechanism for aggressive prefetches. In: Proc 32nd Int Conf Parallel Process, pp. 286–293
[12]
Kani CHEN and Zhezhen JIN Local polynomial regression analysis of clustered data Biometrika 2005 92 1 59-74
[13]
Ostertagov Eva Modelling using polynomial regression Proc Eng 2012 48 1 500-506
[14]
Mosteller F and Tukey JW Data analysis and regression: a second course in statistics 1977 Reading, MA Addison-Wesley
[15]
Gelman A and Hill J Data analysis using regression and multilevel/hierarchical models 2006 Cambridge Cambridge University Press
[16]
Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: ICML, pp. 161-168
[17]
Peters G and Wilkinson JH On the stability of Gauss-Jordan elimination with pivoting Commun ACM 1975 18 20-24
[18]
Renzo Angles, János Benjamin Antal, et al (2020) “The LDBC Social Network Benchmark (version 0.3.3)”, CoRR abs/2001.02299
[19]
Nai Lifeng, Xia Yinglong et al (2015) GraphBIG: understanding graph computing in the context of industrial solutions. In: SC ’15: Proceedings of the International Conference for High Performance Computing. Networking, Storage and Analysis
[20]
Luk C-K et al (2005) Pin: building customized program analysis tools with dynamic instrumentation. In: Proc. ACM SIGPLAN Conf. Program. Language Des. Implementation, pp. 190–200 (2005)
[21]
Chen S, Gibbons PB, and Nath S (2011) Rethinking database algorithms for phase change memory. In: Proc. CIDR, pp. 21–31
[22]
Park KH et al (2010) Mn-mate: Resource management of many cores with dram and non-volatile memories. In: Proc. 12th IEEE Int. Conf. HPCC, Sep., pp. 24–34
[23]
Qureshi M and Karidis J (2009) Enhancing lifetime and security of pcm-based main memory with start-gap wear leveling. In: Proc. 42nd Annu. IEEE/ACM Int. Symp. Microarchit., pp. 14–23
Index Terms
- Dynamic recognition prefetch engine for DRAM-PCM hybrid main memory
Index terms have been assigned to the content through auto-classification.
Recommendations
Designing Hybrid DRAM/PCM Main Memory Systems Utilizing Dual-Phase Compression
The last few years have witnessed the emergence of a promising new memory technology, namely Phase-Change Memory (PCM). Due to its inherent ability to scale deeply into the nanoscale regime and its low power consumption, PCM is increasingly viewed as an ...
Power management of hybrid DRAM/PRAM-based main memory
DAC '11: Proceedings of the 48th Design Automation ConferenceHybrid main memory consisting of DRAM and non-volatile memory is attractive since the non-volatile memory can give the advantage of low standby power while DRAM provides high performance and better active power. In this work, we address the power ...
CARAM: A Content-Aware Hybrid PCM/DRAM Main Memory System Framework
Network and Parallel ComputingAbstractThe emergence of Phase-Change Memory (PCM) provides opportunities for directly connecting persistent memory to main memory bus. While PCM achieves high read throughput and low standby power, the critical concerns are its poor write performance and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 February 2022
Accepted: 29 May 2021
Author Tags
Qualifiers
- Research-article
Funding Sources
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in