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Adjacent LSTM-Based Page Scheduling for Hybrid DRAM/NVM Memory Systems

Authors Manolis Katsaragakis, Konstantinos Stavrakakis, Dimosthenis Masouros, Lazaros Papadopoulos, Dimitrios Soudris

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Author Details

Manolis Katsaragakis
  • Microprocessors and Digital Systems Laboratory, National Technical University of Athens, Greece
Konstantinos Stavrakakis
  • Microprocessors and Digital Systems Laboratory, National Technical University of Athens, Greece
Dimosthenis Masouros
  • Microprocessors and Digital Systems Laboratory, National Technical University of Athens, Greece
Lazaros Papadopoulos
  • Microprocessors and Digital Systems Laboratory, National Technical University of Athens, Greece
Dimitrios Soudris
  • Microprocessors and Digital Systems Laboratory, National Technical University of Athens, Greece

Funding

This work has been partially funded by EU Horizon 2020 program under grant agreement No 101015922 AI@EDGE (https://aiatedge.eu/).

Cite AsGet BibTex

Manolis Katsaragakis, Konstantinos Stavrakakis, Dimosthenis Masouros, Lazaros Papadopoulos, and Dimitrios Soudris. Adjacent LSTM-Based Page Scheduling for Hybrid DRAM/NVM Memory Systems. In 14th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 12th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2023). Open Access Series in Informatics (OASIcs), Volume 107, pp. 7:1-7:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.PARMA-DITAM.2023.7

Abstract

Recent advances in memory technologies have led to the rapid growth of hybrid systems that combine traditional DRAM and Non Volatile Memory (NVM) technologies, as the latter provide lower cost per byte, low leakage power and larger capacities than DRAM, while they can guarantee comparable access latency. Such kind of heterogeneous memory systems impose new challenges in terms of page placement and migration among the alternative technologies of the heterogeneous memory system. In this paper, we present a novel approach for efficient page placement on heterogeneous DRAM/NVM systems. We design an adjacent LSTM-based approach for page placement, which strongly relies on page accesses prediction, while sharing knowledge among pages with behavioral similarity. The proposed approach leads up to 65.5% optimized performance compared to existing approaches, while achieving near-optimal results and saving 20.2% energy consumption on average. Moreover, we propose a new page replacement policy, namely clustered-LRU, achieving up to 8.1% optimized performance, compared to the default Least Recently Used (LRU) policy.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Heterogeneous (hybrid) systems
  • Hardware → Emerging tools and methodologies
Keywords
  • Page Placement
  • Long Short-Term Memory
  • LSTM
  • Prediction
  • NVM
  • DRAM

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