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 16
About the Influence of Workflow Topology on Task Intensity Prediction through Graph Learning
Efficient resource provisioning for large-scale workflows on cloud infrastructures is a critical performance engineering challenge.EPR Count for Runtime Prediction in Distributed Quantum Computing
EPR-pair consumption is commonly used as a communication-cost objective in distributed quantum computing, but minimizing EPR cost does not necessarily minimize distributed execution time.Efficient Expert-Parallel Communication on PCIe-Connected Consumer GPUs
Expert parallelism (EP) enables inference of large Mixture-of-Experts (MoE) models by placing their experts across multiple GPUs, but requires substantial communication between GPUs at every MoE…Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization
Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications.Local Relaxation Hierarchies for Quantum Ground State Energies: Convergence Guarantees and Message Passing Algorithms
Convex relaxation hierarchies provide lower bounds to the ground state energy of quantum many-body systems that can be computed in polynomial time on a classical computer, at any fixed hierarchy…Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization
While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive memory requirements.Leto: Fast In-Place Recovery for LLM Training on Surviving Hardware
Hardware-operable failures (HOFs) interrupt large language model (LLM) training but permit recovery on the same hardware without reset, repair, or replacement.Progressive-Resolution Secure Aggregation for Federated Learning
Secure aggregation lets a server recover an aggregate of client updates without observing any individual update, but conventional protocols fix the aggregate precision when clients upload.Replay the Curvature: Accurate and Scalable NVFP4 Quantization for Large Language Model Inference
Large language models make weight storage and memory traffic major inference costs, motivating low-precision formats that represent each weight with only a few bits.Federated Clustering with Unknown Local and Global Cluster Cardinalities
Federated clustering methods that do not require the global number of clusters $K$ still assume that each client knows its local number $K_g$.Purlin: Separating Orchestration from the Datapath of Collectives
Distributed inference depends on GPU collective communication that must keep pace with evolving hardware and specialized workloads.CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning
Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental mismatches under heterogeneous client data: a…vSkipper: Translating Dynamic Layer Skipping into LLM Serving Gains
Dynamic layer skipping reduces LLM computation by allowing each token to execute only a subset of the model's layers.ARGOS: Reinforcement Learning-Driven Multidimensional Elasticity for Service Orchestration in the Computing Continuum
Data-intensive services in the Computing Continuum must balance analytics quality, resource usage, and cost across heterogeneous nodes with limited and uneven capacity.Encoding and Node Choices in Transversal Fault-Tolerant Distributed Quantum Computations: An Initial Study
We compare and study different Bivariate-Bicycle (BB) encodings and node choices for distributed quantum operations such as transversal non-local CNOTs.DScale: Scaling Block-Diffusion Speculative Decoding with Adaptive Verification
Growing large language model applications demand efficient inference.SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving
No single way of parallelizing attention serves large language models well under all loads.Byzantine Causal Reliable Broadcast with Constant Metadata Overhead
Asynchronous Byzantine Reliable Broadcast (BRB) is a fundamental primitive that guarantees agreement and validity in distributed systems subject to Byzantine faults, but it lacks ordering guarantees.RLX: A Unified Multi-Backend Tensor Compiler and Distributed Runtime in Rust
Production machine learning (ML) stacks often split graph compilation and kernel execution across different layers and languages, making backend behavior, deployment guarantees, and performance…Janus: Evidence-Before-Effect Sagas and Offline-Verifiable Provenance for Agentic LLMs
Agentic large language models (LLMs) now move money through tools, yet the record of what they did is usually a trace their own process emits beside the effect.