Adaptive granularity based last-level cache prefetching method with edram prefetch buffer for graph processing applications

Sae Gyeol Choi, Jeong Geun Kim, Shin Dug Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The emergence of big data processing and machine learning has triggered the exponen-tial growth of the working set sizes of applications. In addition, several modern applications are memory intensive with irregular memory access patterns. Therefore, we propose the concept of adaptive granularities to develop a prefetching methodology for analyzing memory access patterns based on a wider granularity concept that entails both cache lines and page granularity. The proposed prefetching module resides in the last-level cache (LLC) to handle the large working set of memory-intensive workloads. Additionally, to support memory access streams with variable in-tervals, we introduced an embedded-DRAM based LLC prefetch buffer that consists of three granularity-based prefetch engines and an access history table. By adaptively changing the granularity window for analyzing memory streams, the proposed model can swiftly and appropriately determine the stride of memory addresses to generate hidden delta chains from irregular memory access patterns. The proposed model achieves 18% and 15% improvements in terms of energy consumption and execution time compared to global history buffer and best offset prefetchers, respectively. In addition, our model reduced the total execution time and energy consumption by approximately 6% and 2.3%, compared to those of the Markov prefetcher and variable-length delta prefetcher.

Original languageEnglish
Article number991
Pages (from-to)1-24
Number of pages24
JournalApplied Sciences (Switzerland)
Issue number3
Publication statusPublished - 2021 Feb 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea funded by the Korean government (MSIT) (NRF-2019R1A2C1008716).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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