cs.DC · 2026-06-29 · No. 38

Distributed, Parallel, and Cluster Computing, 2026-06-29.

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

    RAMSES: Secure high-performance computing for sensitive data

    Peter Heger, Lech Nieroda, Roland Pabel, Christoph Stollwerk, Stefan Borowski, Kamil Tokmakov, Michael Commer,...

    cs.DC · cs.CR

    Traditionally, the architecture of high-performance computing (HPC) systems is tailored for speed, while highly secure computer systems must sacrifice speed for security. However, a wide range of scientific domains, such as the life sciences, call for a combination of performance and security to allow processing sensitive data at scale. Here, we present RAMSES (Research Accelerator for Modeling and Simulation with Enhanced Security), an HPC...

    arxiv.org/abs/2606.27919 · PDF

  2. 02

    Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems

    Adrian P. Dieguez, Victor Conchello Vendrell, Alex Batlle, Vinnam Kim, Jordi Ros-Giralt, Harris Teague

    cs.DC · cs.AI · cs.LG

    Knowledge Distillation (KD) enables training smaller student models under the guidance of larger teacher models, and the widely adopted TRL library implements it. Yet, TRL treats both models symmetrically, missing opportunities to exploit their pronounced asymmetry in memory footprint, and communication requirements. This paper presents an HPC-aware methodology for KD that decouples teacher and student partitioning efficiently. Our approach...

    arxiv.org/abs/2606.27797 · PDF

  3. 03

    Lightweight Multi-Vehicle Collaborative Perception Acceleration with Fusion Position Adjustment

    Wenzhao Zhang, Shujun Han, Haixiao Gao, Mengying Sun, Bizhu Wang, Xiaodong Xu

    cs.DC

    Multi-vehicle collaborative perception (MvCP) is considered as a key technology to facilitate automated driving (AD), where real-time MvCP under limited resources is significant for reliable AD. In this paper, we formulate a lightweight acceleration scheme for intermediate-fusion (IF) MvCP, which can adapt to both situations of limited computation and communication resources. We provide a relaxed definition conditional additivity and analyze...

    arxiv.org/abs/2606.27750 · PDF

  4. 04

    How far does a random forest generalize from a 54-run LAMMPS+SPICA benchmark?

    Dennis Alves Pedersen, Paulo Henrique Leme Ramalho, Fábio Andrijauskas

    cs.DC

    Selecting near-optimal hybrid MPI+OpenMP configurations for molecular dynamics workloads on modern HPC clusters has traditionally required exhaustive empirical benchmarking, consuming allocation budget proportional to the number of configurations evaluated. This work investigates whether a cold-start Random Forest surrogate, trained once on a small, structured benchmark dataset, can reliably predict execution performance and recommend...

    arxiv.org/abs/2606.27695 · PDF

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