arXiv subjects

Yanning Yang

Publications and source records attributed to Yanning Yang.

2 recordsLinked to original sources

arXiv2025

CHIME: A Case for Efficient Long-Context Attention-FC Disaggregated Inference with DIMM-PIM

Attention-FC Disaggregated (AFD) LLM inference systems offload memory-bound Attention operations to memory-rich accelerators (e.g., CPUs, HBM-PIM) while retaining compute-bound Fully-Connected (FC) operations on GPUs. In this paper, we first design a Disaggregated Roofline Model (DRM) to characterize AFD performance, revealing that system throughput is constrained by the accelerator's limiting factor: either memory bandwidth or capacity. We observe that prior AFD systems often overlook these constraints and fail to balance them, leading to resource underutilization or constrained throughput. Therefore, we propose CHIME, the first AFD system integrating DIMM-PIM, which is a case of the new accelerator that strikes the balance with scalable capacity and bandwidth. To address the synchronization challenges inherent to the distributed cooperating DRAM chips in DIMM-PIM, CHIME employs bubble-free pipelining and hybrid-grained re-layout for efficient attention computation. Furthermore, it maximizes cross-device resource utilization via rankset-granular communication-computation overlapping and alignment-predicting scheduling. Evaluations show CHIME achieves up to 5.15$\times$ speedup over state-of-the-art HBM-PIM solutions.

cs.AR↗

arXiv2024

Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability

Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to reduce resource wastage, but more frequent scaling also leads to more cold start overheads. This paper introduces Jiagu, which harmonizes efficiency with practicability through two novel techniques. First, pre-decision scheduling achieves accurate prediction while eliminating overheads by decoupling prediction and scheduling. Second, dual-staged scaling achieves frequent adjustment of instances with minimum overhead. We have implemented a prototype and evaluated it using real-world applications and traces from the public cloud platform. Our evaluation shows a 54.8% improvement in deployment density over commercial clouds (with Kubernetes) while maintaining QoS, and 81.0%--93.7% lower scheduling costs and a 57.4%--69.3% reduction in cold start latency compared to existing QoS-aware schedulers in research work.

cs.DC↗