ICGMM: CXL-enabled Memory Expansion with Intelligent Caching Using Gaussian Mixture Model
<|reference_start|>ICGMM: CXL-enabled Memory Expansion with Intelligent Caching Using Gaussian Mixture Model: Compute Express Link (CXL) emerges as a solution for wide gap between computational speed and data communication rates among host and multiple devices. It fosters a unified and coherent memory space between host and CXL storage devices such as such as Solid-state drive (SSD) for memory expansion, with a corresponding DRAM implemented as the device cache. However, this introduces challenges such as substantial cache miss penalties, sub-optimal caching due to data access granularity mismatch between the DRAM "cache" and SSD "memory", and inefficient hardware cache management. To address these issues, we propose a novel solution, named ICGMM, which optimizes caching and eviction directly on hardware, employing a Gaussian Mixture Model (GMM)-based approach. We prototype our solution on an FPGA board, which demonstrates a noteworthy improvement compared to the classic Least Recently Used (LRU) cache strategy. We observe a decrease in the cache miss rate ranging from 0.32% to 6.14%, leading to a substantial 16.23% to 39.14% reduction in the average SSD access latency. Furthermore, when compared to the state-of-the-art Long Short-Term Memory (LSTM)-based cache policies, our GMM algorithm on FPGA showcases an impressive latency reduction of over 10,000 times. Remarkably, this is achieved while demanding much fewer hardware resources.<|reference_end|>
@article{chen2024icgmm:,
    title={ICGMM: CXL-enabled Memory Expansion with Intelligent Caching Using
  Gaussian Mixture Model},
    author={Hanqiu Chen, Yitu Wang, Luis Vitorio Cargnini, Mohammadreza
  Soltaniyeh, Dongyang Li, Gongjin Sun, Pradeep Subedi, Andrew Chang, Yiran
  Chen and Cong Hao},
    journal={arXiv preprint arXiv:2408.05614},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05614},
    primaryClass={cs.AR cs.ET cs.SY eess.SY}
}