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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Beyond the Bermuda Triangle of Contention: IOMMU Interfer...
Diogo Costa, Jose Martins, Sandro Pinto · 2025-08-27 · via cs.DC updates on arXiv.org

As Mixed Criticality Systems (MCSs) evolve, they increasingly integrate heterogeneous computing platforms, combining general-purpose processors with specialized accelerators such as AI engines, GPUs, and high-speed networking interfaces. This heterogeneity introduces challenges, as these accelerators and DMA-capable devices act as independent bus masters, directly accessing memory. Consequently, ensuring both security and timing predictability in such environments becomes critical. To address these concerns, the Input-Output Memory Management Unit (IOMMU) plays a key role in mediating and regulating memory access, preventing unauthorized transactions while enforcing isolation and access control policies. While prior work has explored IOMMU-related side-channel vulnerabilities from a security standpoint, its role in performance interference remains largely unexplored. Moreover, many of the same architectural properties that enable side-channel leakage, such as shared TLBs, caching effects, and translation overheads, can also introduce timing unpredictability. In this work, we analyze the contention effects within IOMMU structures using the Xilinx UltraScale+ ZCU104 platform, demonstrating how their shared nature introduce unpredictable delays. Our findings reveal that IOMMU-induced interference primarily affects small memory transactions, where translation overheads significantly impact execution time. Additionally, we hypothesize that contention effects arising from IOTLBs exhibit similar behavior across architectures due to shared caching principles, such as prefetching and hierarchical TLB structures. Notably, our experiments show that IOMMU interference can delay DMA transactions by up to 1.79x for lower-size transfers on the Arm SMMUv2 implementation.