paperbot · PL 论文追踪

全部论文 待读 收藏

P2 Towards an Adaptable Systems Architecture for Memory Tiering at Warehouse-Scale

迈向仓库级内存分层的可适配系统架构
ASPLOS Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 32023匹配度 1/10 · 论文属于系统架构与内存管理方向,研究仓库级计算中的内存分层与成本优化,与静态分析、LLM/Agent、浮点程序分析及贝叶斯程序分析均无直接交集。引用 69 inbox
Padmapriya Duraisamy, Wei Xu, Scott Hare, Ravi Rajwar, David Culler, Zhiyi Xu, Jianing Fan, Christopher Kennelly, Bill McCloskey, Danijela Mijailovic, Brian Morris, Chiranjit Mukherjee, Jingliang Ren, Greg Thelen, Paul Turner, Carlos Villavieja, Parthasarathy Ranganathan, Amin Vahdat
crossref: 2026-09-12

AI 速览

一句话:提出面向仓库级计算的可适配内存分层架构,用低成本慢速内存替代部分DRAM以降低内存成本。

快速DRAM在大规模计算环境中的基础设施支出占比越来越高,若不进行架构转变,这一趋势可能进一步恶化。论文提出通过用成本更低但速度较慢的存储介质替换部分常规DRAM,构建两层均可直接寻址并可缓存的内存分层系统,从而降低已部署内存成本。然而在高度多租户的仓库级计算环境中,这种设计面临诸多挑战:应用类型多样、规模巨大。因此,作者主张通用情况下采用对应用透明的解决方案,同时能够适配特定工作负载的需求。摘要未报告具体实现机制、定量结果或评估细节,主要贡献是提出该可适配系统架构的问题定位与设计方向。

问题
快速DRAM成本在大规模计算设施中日益主导,需要在不牺牲过多性能的前提下降低内存成本。
方法
用低成本慢速内存介质替代部分DRAM,构建两层直接可寻址并缓存的 tiered memory 系统,并强调应用透明且可适配特定工作负载。
评估
摘要未给出具体评估方式或实验结果,仅提出面向仓库级多租户场景的架构方向与设计约束。
原文摘要(Abstract)

Fast DRAM increasingly dominates infrastructure spend in large scale computing environments and this trend will likely worsen without an architectural shift. The cost of deployed memory can be reduced by replacing part of the conventional DRAM with lower cost albeit slower memory media, thus creating a tiered memory system where both tiers are directly addressable and cached. But, this poses numerous challenges in a highly multi-tenant warehouse-scale computing setting. The diversity and scale of its applications motivates an application-transparent solution in the general case, adaptable to specific workload demands.

链接与引用

DOI 原文 · PDF(开放获取) ·

BibTeX
@inproceedings{paperbot10075,
  title = {Towards an Adaptable Systems Architecture for Memory Tiering at Warehouse-Scale},
  author = {Padmapriya Duraisamy and Wei Xu and Scott Hare and Ravi Rajwar and David Culler and Zhiyi Xu and Jianing Fan and Christopher Kennelly and Bill McCloskey and Danijela Mijailovic and Brian Morris and Chiranjit Mukherjee and Jingliang Ren and Greg Thelen and Paul Turner and Carlos Villavieja and Parthasarathy Ranganathan and Amin Vahdat},
  booktitle = {ASPLOS},
  year = {2023},
  doi = {10.1145/3582016.3582031}
}