cs.DC · 2026-06-02 · No. 13

Distributed, Parallel, and Cluster Computing, 2026-06-02.

6 new papers in cs.DC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference

    Yafan Huang, Sheng Di, Guanpeng Li

    cs.DC · cs.AI

    Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decision-making. Yet, how soft errors propagate and affect LLM inference remains largely unexplored. To bridge this gap, we present a comprehensive study on error propagation in LLM inference, enabled by our proposed LLMFI, a...

    arxiv.org/abs/2606.02430 · PDF

  2. 02

    Strategies for Molecular Dynamics using Hybrid Systems: LAMMPS Use Case

    Paulo Henrique Leme Ramalho, Dennis Alves Pedersen, Fábio Andrijauskas

    cs.DC

    The complexity of biomolecular simulations has substantially increased the demand for High-Performance Computing (HPC) infrastructures, particularly in molecular dynamics and coarse-grained modeling. This work presents a systematic performance and scalability analysis of the LAMMPS simulator for coarse-grained biomolecular simulations, using the antimicrobial peptide Tritrpticin (PDB ID: 1D6X) as the experimental workload. Pure MPI and hybrid...

    arxiv.org/abs/2606.02319 · PDF

  3. 03

    EES-CND: Collaborative Neural Decision-Making for Drift-Aware Fault-Tolerant Edge-Cloud Service Placement

    Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed, Calin Curescu

    cs.DC

    The edge-cloud paradigm improves service delivery by orchestrating resources across edge nodes and cloud data centres. These environments consist of heterogeneous, interconnected computing nodes that cooperate to deliver continuous services. However, their scale and complexity increase vulnerability to failures from hardware malfunctions, software defects, and dynamic operating conditions. These failures can disrupt system configurations and...

    arxiv.org/abs/2606.02259 · PDF

  4. 04

    TAPAAL SMC: Statistical Model Checking of Stochastic Timed-Arc Petri Nets

    Tanguy Dubois, Kim G. Larsen, Jiri Srba

    cs.DC

    Timed-Arc Petri net (TAPN) is a timed extension of the classical Petri net model where tokens have their age and input arcs are associated with time intervals restricting the ages of tokens available for transition firing. Additionally, a TAPN can also contain place invariants constraining the ages of tokens in places, inhibitor arcs preventing a transition from firing and transport arcs that preserve token ages upon firing. This set of...

    arxiv.org/abs/2606.02007 · PDF

  5. 05

    Scaling LLM Inference Beyond Amdahl`s Limits via Eliminating Non-Scalable Overheads

    Alan Zhao, Cyril Y. He, Wei Xu

    cs.DC

    Deployers of online LLM services usually seek to maximize cluster-wide performance given a fixed number of GPUs. Tensor parallelism (TP) is necessary to fit modern models but scales sub-linearly as the TP degree t grows, due to cross-GPU communication and non-scalable runtime work, as predicted by Amdahl's Law. Conversely, increasing t improves memory efficiency and alleviates KV-cache contention and swapping. We identify and validate an...

    arxiv.org/abs/2606.01927 · PDF

  6. 06

    Boosting Multimodal Federated Learning via Chained Modality Optimization

    Zixin Zhang, Fan Qi, Shuai Li, Xiaoshan Yang, Changsheng Xu

    cs.DC · cs.AI

    Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative learning across decentralized clients with heterogeneous data and modality availability. However, most existing MMFL methods cast multimodal training as a joint optimization problem, overlooking a key bottleneck: modality competition, where dominant modalities suppress weaker ones and lead to suboptimal global models. To address this, we propose FedMChain, a...

    arxiv.org/abs/2606.01856 · PDF

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