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Distributed & Parallel Computing
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507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Hybrid Classical-Quantum Solutions to Accelerate the Adoption of Quantum Computing
Despite the promise and potential of quantum computing, most of the current day developments of quantum computing have several limitations for relevant practical use.Evaluating Inference Compute for Generative AI: A Framework for Enterprise Workloads
LLM deployment is shifting from single-turn completion to agentic trajectories in which a model plans, calls tools, reads results and reasons at test time before acting.T-CCL: Resource Efficient and Performant Collective Communication using Tensor Memory Accelerator
Large transformer-based models increasingly depend on multi-GPU execution, which requires frequent collective communication among GPUs.Cascadia: Resident 975B MoE Inference on Eleven AI PCs
Mixture-of-experts models make nearly trillion-parameter capacity accessible with sparse per-token computation, provided that the serving system can distribute the weights and coordinate their…Memory-Efficient Expert Routing for Distributed MoE Training
As Mixture-of-Experts (MoE) models scale toward hundreds of experts and higher top-$k$ routing, memory efficiency in distributed training becomes a critical bottleneck.Evaluating Behavioral Context for Interpretable IAM Policy Risk Scoring in Cloud Environments
IAM policy analysis typically emphasizes the authorization capabilities encoded in a policy, but security analyst review priority may also depend on the behavioral and environmental context…Byzantine-Tolerant Causal Unicast with Constant Message Space Overhead
Causal message ordering provides essential semantics for distributed applications, yet ensuring it within an asynchronous system subject to Byzantine failures presents fundamental theoretical and…Static Bootstrap Placement for Encrypted Language Model Decoding
Language models increasingly serve prompts that carry private data, and secure inference under homomorphic encryption lets a client outsource the computation without revealing the prompt.From Overloaded to Guaranteed: High-Throughput Multi-SLO Enforcement for LoRA-Assisted On-Premise LLM Deployment
As Large Language Models (LLMs) become essential in privacy-sensitive sectors like hospitals and government agencies, the on-premise LLM servers offer a cost-effective and secure alternative to…Min-Max Uniform Circle Formation by Asynchronous Mobile Robots
Given a set of point robots $\mathcal{R}$ in the Euclidean plane and a target circle $\mathbf C$ enclosing all robot positions, the \textsc{Min-Max Uniform Circle Formation (MMUCF)} problem requires…Characterizing Parallelism Strategies in LLM Inference: Fundamental Compute-Communication Trade-offs
Large Language Model (LLM) inference has become the dominant workload in modern AI systems, requiring serving infrastructures to maximize throughput while meeting strict latency Service-Level…Measuring and Reducing Cross-Vendor Mismatch in Language Models
Running the same language model on different graphics processing unit (GPU) vendors can produce different logits, even when the model weights and inputs are the same.Rethinking Streaming-Perception Evaluation on Heterogeneous Edge Platforms
Multi-camera streaming perception is increasingly deployed on heterogeneous edge platforms shared with co-resident workloads, yet accelerator placement is often evaluated using isolated…When the Cross-Silo Federation Goes Offline: Continual Learning for Site Onboarding with Limited Unlabeled Data
An organization often holds too little labeled data to train a model that generalizes, and the records that would supply the rest sit with organizations that cannot release them.Structural Conditions for Distributed Quantum Advantage
Circuit cutting runs a quantum computation on processors smaller than the circuit, at the price of classical knitting whose cost grows exponentially with the number of cut gates.JASPER: Special Session on Joint Reliability And Security Assessment of SPlit Computing for Edge Robustness
Split Computing (SC) enables efficient deployment of Deep Neural Networks (DNNs) by partitioning inference between edge devices and cloud servers.Budget-Constrained Fault-Tolerant Mutual Visibility for Autonomous Robots under the Mobility Fault Model
We investigate the mutual visibility problem for a swarm of $n\ge3$ autonomous mobile robots under the budget-constrained mobility fault model.PhaseGate: Phase-Aware CPU Retrieval Scheduling for On-Device LLMs on Unified Memory
On-device assistants run GPU-based LLM inference alongside CPU retrieval on unified-memory systems.AID: A Framework for AI Infrastructure Dynamics
A useful model of AI inference infrastructure must specify the system state, the information available to an observer, and the decisions the model is intended to support.When Robots Crash: Optimal Asynchronous Gathering at Weber Meeting Nodes
We study the \textit{optimal gathering} problem over a finite set of designated \textit{meeting nodes} for \textit{asynchronous, anonymous,} and \textit{oblivious} mobile robots on an infinite grid…