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新论文 · v1arXiv:2611.16189

SBA: A Practical Memory Performance Denial-of-Service Attack

Amin Mamandipoor, Connor Rudy Sullivan, Cole Ridge Strickler, Mohammad Alian, Heechul Yun

原文摘要

Modern cloud platforms use memory bandwidth regulation to isolate co-located workloads, implicitly assuming that high aggregate bandwidth consumption implies high interference. We show that this assumption is fundamentally flawed. We present the Single-Bank Attack (SBA), a practical memory denial-of-service attack that induces extreme cross-core slowdowns---over three orders of magnitude on memory-intensive victims (e.g., LLM decode)---while consuming less than 1 GB/s of memory bandwidth. SBA exploits the gap between peak and worst-case DRAM throughput by concentrating memory write operations on a single bank, forcing persistent row conflicts and frequent write draining, thereby collapsing the memory service rate. Across Intel and AMD systems, including DDR5- and HBM2E-based platforms, SBA consistently causes orders-of-magnitude slowdowns under realistic multi-tenant settings, revealing a fundamental limitation of bandwidth-centric memory isolation. We further show that this localized contention propagates system-wide: single-bank congestion saturates memory controller queues and induces back-pressure into shared cache and coherence structures, ultimately throttling memory request injection across cores. Consequently, severe interference can arise even at low aggregate bandwidth, evading existing bandwidth-based QoS mechanisms such as Intel RDT MBA. To address this limitation, we propose IQ-MemGuard, a lightweight, software-based memory bandwidth regulator that detects sustained contention using microarchitectural signals and selectively throttles pathological request injection. Our implementation on an Intel platform effectively mitigates SBA, whereas Intel's hardware QoS mechanism fails completely.

cs.AR

版本更新 · v2arXiv:2609.03079

LeanStream: A Speculate-and-Refine Streaming Framework for Efficient on-Device LLM Inference

Renyuan Liu, Yuyang Leng, Kaiyan Liu, Yuzhou Zhong, Shaohan Hu, Chun-Fu, Chen, Peijun Zhao 等 10 位作者

原文摘要

On-device LLM inference is attractive for privacy and responsiveness, but remains challenging on mobile and embedded devices because model weights far exceed available DRAM. Prior systems exploit activation sparsity and offload weights to SSD or flash storage, but face a fundamental systems trade-off: accurate sparse execution decisions require the latest context, whereas efficient computation-I/O overlap requires early prediction. As a result, existing designs either serialize execution or incur redundant weight fetches, extra computation, and large cache overheads. We present LeanStream, a streaming speculate-and-refine framework for efficient on-device LLM inference. LeanStream progressively refines computation, loading, and cache-retention priorities using partial GPU results, enabling fine-grained overlap between GPU execution and storage I/O. We implement LeanStream on both mobile and embedded platforms. Compared with prior on-device LLM inference systems, LeanStream reduces memory usage by 4.8$\times$ to 7.5$\times$ at the best throughput achieved by prior work, while further improving token generation throughput by 1.6$\times$ to 2.1$\times$. Source code is available at https://github.com/RenyuanLIU/LeanStream.

cs.LG