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.
|Number of pages||18|
|Journal||Journal of Supercomputing|
|Publication status||Published - 2022 Feb|
Bibliographical noteFunding Information:
This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1008716). We would like to thank Editage (www.editage.co.kr) for English language editing.
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture