Systems & Networks · Computer Science & AI
Distributed & Parallel Computing
The newest papers in Distributed & Parallel Computing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
507 papers, from 4 September 2026 to 5 November 2026: 507 arXiv preprints.
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Newest papers, page 12
Entanglement-Capability-Aware Finite-Bond Circuit Cutting for Distributed Quantum Computing
Distributed quantum computing distributes workloads that exceed a single processor's capacity across resource-constrained components.sGLP: Simulating People with Stochastic Agents
Grassroots platforms are designed to be owned, operated and governed by their participants, and thus are constituted from person-to-person smartphone apps that are run by the participants.KedgeFlow: A Programmable Control Plane for Enterprise AI Agents
Enterprise AI agents can execute altered versions of approved actions, concurrently reuse the same permission, or retry operations whose responses were lost.Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance
Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time.Conflict-Aware Autonomous Resource Orchestration for 5G Edge Slicing Using Multi-Agent Systems and LLMs
5G edge network slicing must reconcile two coupled requirements: understanding high-level operator intent and reallocating scarce computing and bandwidth resources under dynamic contention.BSIPipe: Bidirectional Sequence-Level Interleaved Pipeline Parallelism for Long-Sequence Large Language Model Training
Pipeline parallelism has become a critical technique for training large language models.Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under…Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks
Multi-source semantic information fusion enables edge networks to support global inference using observations collected by distributed terminals.Sustainable Cloud-Fog-Edge Systems for Smart Buildings: Algorithmic Adaptation under Memory Constraints and Dynamic Orchestration Strategies
The escalating environmental footprint of the Information and Communication Technol- ogy (ICT) sectorwith global Data Center electric- ity usage potentially reaching 3% by 2030 and the imperative…Taking Tokenization off the Host for Agentic LLM Serving
Agentic workloads resend long contexts that barely change between calls.Adaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy
Long-context serving runs out of KV memory before the hardware reaches its useful throughput.Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins
Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent management to improve safety and reduce congestion.Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs
The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the user.Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning
The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks.When More Memory Fails: Hidden Feasibility Holes in Dynamic Tensor Rematerialization
Dynamic Tensor Rematerialization (DTR) trains neural networks under a memory budget by evicting tensors and recomputing them on demand.PageWeaver: KV-Guided Query Unions for Sparse Attention
Dynamic sparse attention limits the KV pages selected by each query, but a small support does not necessarily yield efficient GPU work.NosRacer: Dynamic Detection of Race Conditions in On-Device Network Operating Systems
Commercial on-device network operating systems (NOSes) run complex control planes in production routers and switches, where configuration update tasks are executed by multiple loosely coupled…Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it - giving a much simpler method…Deploying RDMA for Distributed Inference and Training At Scale
RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing TCP.Expert Coupling in MoE Pretraining: Reducing All-to-All Overhead with Correlated Placement and Token Shuffling
Mixture-of-Experts (MoE) layers replace the feed-forward block of a Transformer with E expert networks, and each token is routed to k of these experts.