Trimma: Trimming Metadata Storage and Latency for Hybrid Memory Systems
<|reference_start|>Trimma: Trimming Metadata Storage and Latency for Hybrid Memory Systems: Hybrid main memory systems combine both performance and capacity advantages from heterogeneous memory technologies. With larger capacities, higher associativities, and finer granularities, hybrid memory systems currently exhibit significant metadata storage and lookup overheads for flexibly remapping data blocks between the two memory tiers. To alleviate the inefficiencies of existing designs, we propose Trimma, the combination of a multi-level metadata structure and an efficient metadata cache design. Trimma uses a multi-level metadata table to only track truly necessary address remap entries. The saved memory space is effectively utilized as extra DRAM cache capacity to improve performance. Trimma also uses separate formats to store the entries with non-identity and identity address mappings. This improves the overall remap cache hit rate, further boosting the performance. Trimma is transparent to software and compatible with various types of hybrid memory systems. When evaluated on a representative hybrid memory system with HBM3 and DDR5, Trimma achieves up to 1.68$\times$ and on average 1.33$\times$ speedup benefits, compared to state-of-the-art hybrid memory designs. These results show that Trimma effectively addresses metadata management overheads, especially for future scalable large-scale hybrid memory architectures.<|reference_end|>
@article{li2024trimma:,
    title={Trimma: Trimming Metadata Storage and Latency for Hybrid Memory Systems},
    author={Yiwei Li, Boyu Tian, Mingyu Gao},
    journal={arXiv preprint arXiv:2402.16343},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16343},
    primaryClass={cs.AR}
}