SAC: Sharing-Aware Caching in Multi-Chip GPUs
Shiqing Zhang, Mahmood Naderan-Tahan, Magnus Jahre, Lieven Eeckhout
Abstract
Bandwidth non-uniformity in multi-chip GPUs poses a major design challenge for its last-level cache (LLC) architecture. Whereas a memory-side LLC caches data from the local memory partition while being accessible by all chips, an SM-side LLC is private to a chip while caching data from all memory partitions. We find that some workloads prefer a memory-side LLC while others prefer an SM-side LLC, and this preference solely depends on which organization maximizes the effective LLC bandwidth. In contrast to prior work which optimizes bandwidth beyond the LLC, we make the observation that the effective bandwidth ahead of the LLC is critical to end-to-end application performance. We propose Sharing-Aware Caching (SAC) to adopt either a memory-side or SM-side LLC organization by dynamically reconfiguring the routing policies in the intra-chip interconnection network and LLC controllers. SAC is driven by a simple and lightweight analytical model that predicts the impact of data sharing across chips on the effective LLC bandwidth. SAC improves average performance by 76% and 12% (and up to 157% and 49%) compared to a memory-side and SM-side LLC, respectively. We demonstrate significant performance improvements across the design space and across workloads.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext acf5bb4c-a9df-487a-8a45-ac6d912ae46bCited by top-tier papers3
- Barre Chord: Efficient Virtual Memory Translation for Multi-Chip-Module GPUsYuan Feng, Seonjin Na, Hyesoon Kim, Hyeran JeonISCA 2024 · 20 citations
- Photon: A Fine-grained Sampled Simulation Methodology for GPU WorkloadsChangxi Liu, Yifan Sun, Trevor E. CarlsonMICRO 2023 · 10 citations
- SMILE: LLC-based Shared Memory Expansion to Improve GPU Thread Level ParallelismTianyu Guo, Xuanteng Huang, Kan Wu, Xianwei Zhang et al.DAC 2024 · 3 citations
Builds on6
- Accel-Sim: An Extensible Simulation Framework for Validated GPU ModelingMahmoud Khairy, Zhesheng Shen, Tor M. Aamodt, Timothy G. RogersISCA 2020 · 366 citations
- Griffin: Hardware-Software Support for Efficient Page Migration in Multi-GPU SystemsTrinayan Baruah, Yifan Sun, Ali Tolga Dinçer, Saiful A. Mojumder et al.HPCA 2020 · 50 citations
- Locality-Centric Data and Threadblock Management for Massive GPUsMahmoud Khairy, Vadim Nikiforov, David W. Nellans, Timothy G. RogersMICRO 2020 · 38 citations
- HMG: Extending Cache Coherence Protocols Across Modern Hierarchical Multi-GPU SystemsXiaowei Ren, Daniel Lustig, Evgeny Bolotin, Aamer Jaleel et al.HPCA 2020 · 38 citations
- GPS: A Global Publish-Subscribe Model for Multi-GPU Memory ManagementHarini Muthukrishnan, Daniel Lustig, David W. Nellans, Thomas F. WenischMICRO 2021 · 23 citations
Related papers
- NUBA: Non-Uniform Bandwidth GPUsXia Zhao, Magnus Jahre, Yuhua Tang, Guangda Zhang et al.ASPLOS 2023 · 17 citations
- Selective Replication in Memory-Side GPU CachesXia Zhao, Magnus Jahre, Lieven EeckhoutMICRO 2020 · 15 citations
- Leveraging Chiplet-Locality for Efficient Memory Mapping in Multi-Chip Module GPUsJunhyeok Park, Sungbin Jang, Osang Kwon, Yongho Lee et al.MICRO 2025 · 7 citations
- Analyzing and Leveraging Decoupled L1 Caches in GPUsMohamed Assem Ibrahim, Onur Kayiran, Yasuko Eckert, Gabriel H. Loh et al.HPCA 2021 · 30 citations
- Predictable sharing of last-level cache partitions for multi-core safety-critical systemsZhuanhao Wu, Hiren D. PatelDAC 2022 · 6 citations
