cs.DC · 2026-08-17 · No. 87

Distributed, Parallel, and Cluster Computing, 2026-08-17.

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

01 — The papers

4 entries
  1. 01

    Validating LLM-Modernized Scientific Software Through Differential Fault Injection

    Evan Coleman, Yuzhong Shen, Masha Sosonkina, Peng Xu

    cs.DC · cs.SE

    Large language model (LLM) agents are increasingly used to modernize the legacy Fortran underlying production scientific software, but validation of these transformations emphasizes nominal executions and may not test whether a modernization preserves the original code's response to faults, perturbations, and reduced precision. We present a differential fault-injection validation method: a harness instruments the shared self-consistent-field...

    arxiv.org/abs/2608.14527 · PDF

  2. 02

    Large-scale workflow placement in serverless computing using integer nonlinear programming

    Joshua Adamek, Natalie Carl, Trever Schirmer, Moritz Heinlein, David Bermbach, Sergio Lucia

    cs.DC

    Serverless edge computing has become a powerful cloud framework that enables the execution of large workflows without the need for the user to manage the underlying servers and edge devices. In this work, we address the challenge of deploying these workflows on a large number of different existing servers and edge devices such that monetary costs for the users and workflow evaluation times are minimized. To this end, the workflow and cloud...

    arxiv.org/abs/2608.14427 · PDF

  3. 03

    Could Model Partitioning Make Federated Learning More Sustainable?

    Tobias Frohlich, Tiffany Vlaar, Lauritz Thamsen

    cs.DC

    As federated learning (FL) extends from distributed machine learning between low-power devices to cross-silo scenarios involving edge servers and data centres, its carbon footprint has become a growing concern. Addressing this, methods for sustainable FL align training with low-carbon energy availability or low grid demand and reduce the energy consumption of clients powered by high-carbon sources by decreasing the size of their models. We...

    arxiv.org/abs/2608.14242 · PDF

  4. 04

    Balancing Workload Performance and Slurm Stress: Four Nextflow Deployment Strategies

    Nil Tianchen Mu, William Dizon, Glen Otero, Torey Battelle

    cs.DC

    Wide Nextflow fan-outs on shared Slurm clusters can submit tens of thousands of short tasks. Deployment choices - individual jobs, arrays, or nested schedulers within allocations - affect both workflow turnaround and RPC volume, a shared cost that can degrade scheduler responsiveness. Existing studies compare whole workflow systems, while per-task queueing metrics do not span architectures that dispatch inside an existing allocation. We...

    arxiv.org/abs/2608.13824 · PDF

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