cs.DC · 2026-08-26 · No. 96

Distributed, Parallel, and Cluster Computing, 2026-08-26.

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

01 — The papers

7 entries
  1. 01

    Asynchronous Verifiable Information Dispersal with Low Space and Communication Complexity

    Thomas Locher, Yvonne-Anne Pignolet

    cs.DC · cs.CR · cs.IT

    The primary goal of a distributed storage system is to ensure that clients can both write and read data in a reliable and consistent manner, even in the presence of failures. While existing asynchronous verifiable information dispersal (AVID) protocols achieve optimal space complexity for storage and communication complexity for data retrieval in a Byzantine setting, the crucial operations of data dispersal and node recovery have received...

    arxiv.org/abs/2608.24636 · PDF

  2. 02

    Scalable datacenter replication with mostly-synchronous consensus on hardware

    Davide Rovelli, Philipp Berdesinski, Rodrigo Otoni, Patrick Eugster

    cs.DC

    Consistent replication of data among distributed processes -- a task involving the well-known consensus problem -- is notoriously expensive and hard to scale, affecting especially datacenter services with stringent performance requirements. To mitigate this problem, we introduce scalable replication in-hardware ( scarHW ): a network card design that improves throughput and latency of consistent replication even when increasing the number of...

    arxiv.org/abs/2608.24622 · PDF

  3. 03

    SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

    Hao Wu, Kin Whye Chew, Yizhan Han, Han Li, Jingxian Wang

    cs.DC · cs.LG

    Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereby avoiding large-scale data downloads to ground servers. However, training convergence is significantly slowed by severe non-IID data, specifically label imbalance, as each satellite observes different geographic regions with distinct labels. This imbalance extends training duration and...

    arxiv.org/abs/2608.24516 · PDF

  4. 04

    Mixed-Precision SEM-Based CFD Simulations on GPUs: A Taylor-Green Vortex case

    Yanxiang Chen, Manuel Münsch, Roman Iakymchuk

    cs.DC · cs.SE

    Mixed precision is a promising approach for reducing the computational cost and energy consumption of Computation Fluid Dynamics (CFD) simulations, but its effectiveness depends strongly on where precision is reduced within the full simulation pipeline. In this work, we study Taylor-Green vortex case using Neko, a matrix-free CFD solver based on the spectral element method (SEM). Profiling shows that the fluid time step is not dominated by...

    arxiv.org/abs/2608.24348 · PDF

  5. 05

    An HPC Approach to Accelerate Tensor Decompositions

    Markus Hellgren, Erna Begovic Kovac, Hans O. Karlsson, Roman Iakymchuk

    cs.DC · cs.MS

    Quantum systems grow in complexity so rapidly that even modest models become difficult to simulate, creating a strong need for methods that can handle high-dimensional data, also known as tensors. In this work, we investigate a novel Jacobi-type tensor algorithm for tensor decomposition and develop a CUDA-based algorithm that supports tensors of arbitrary order on a single GPU. We test the implementation on NVIDIA H100 GPUs and show that the...

    arxiv.org/abs/2608.24307 · PDF

  6. 06

    pigzpp: Fast, Parallel, Portable Compression for the Whole Stack

    Thamme Gowda

    cs.DC

    pigz is a widely deployed parallel gzip utility, but its process-global mutable state means that it was not designed as a reentrant, directly embeddable library. pigzpp is a from-scratch C++23 rewrite that turns the design into a thread-safe library with one accelerated DEFLATE core exposed to C++, Python, WebAssembly, Go, and Rust. As application-level conveniences built on that same core, it also provides multi-entry ZIP archives and a fast...

    arxiv.org/abs/2608.24153 · PDF

  7. 07

    A Few Shared Random Bits Suffice for Constant-Round Almost Stable Matching

    Yijun Chang, Kushagra Chatterjee

    cs.DC

    We show that almost stable matching can be solved in constant distributed rounds on general bipartite graphs $G=(V,E)$ using only a few shared random bits. Specifically, in the $\congest$ model, we compute a matching whose expected number of blocking pairs is at most $\varepsilon |E|$ in $O\left(\frac{\log(1/\varepsilon)}{\varepsilon^4}\right)$ rounds using $O\left(\log(1/\varepsilon)\right)$ shared random bits. Thus, for every constant...

    arxiv.org/abs/2608.24102 · PDF

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