cs.DC · 2026-07-18 · No. 57

Distributed, Parallel, and Cluster Computing, 2026-07-18.

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

    Memory-Exhaustion Attack on the Blocklace Byzantine-Repelling Conflict-Free Replicated Data Type

    Erick Lavoie

    cs.DC

    The blocklace is a directed acyclic graph encoding the causal relationship between authenticated updates produced by participating nodes. Compared to previous approaches, it adds restrictions on what can be replicated: a new update and its causal history is replicated locally if and only if either 1) it reveals a new node behaving arbitrarily (byzantine), or 2) it was signed by a node that still appears to be correct and the new updates...

    arxiv.org/abs/2607.15185 · PDF

  2. 02

    Don't Predict, Prioritize: Rethinking GPU Reliability Assessment

    Difeng Ma, Changhua Pei, Yuanwei Lu, Quan Zhou, Zexin Wang, Yibo Zhu, Daxin Jiang, Dan Pei, Jingjing Li, Gaogang Xie

    cs.DC

    The reliability of Graphics Processing Units (GPUs) is a criticalbottleneck for modern large-scale AI infrastructure, where a sin-gle node failure can disrupt synchronous training jobs and causesignificant financial losses. While predictive maintenance is widelyused in other hardware domains, we demonstrate that accuratelypredicting the exact timing of GPU failures is inherently difficult.Through an in-depth analysis of telemetry data from a...

    arxiv.org/abs/2607.15115 · PDF

  3. 03

    Ground-Side Mission Plan Compilation with Policy-as-Code Guardrails for Cloud-Native Satellite Platforms

    Hsiu-Chi Tsai, Chia-Tung Chung

    cs.DC

    Onboard cloud-native runtimes for satellites are emerging on multiple tracks (ORCHIDE, Axiom Space's AxDCU-1, Kepler's Jetson nodes), but each assumes that the workflow artifacts it executes arrive from the ground. ORCHIDE's architecture document D3.1 states explicitly that "only the Deferred Phase is part of the ORCHIDE scope," and no open-source ground-side toolchain has been released by the consortium. We present Satellite Mission...

    arxiv.org/abs/2607.14798 · PDF

  4. 04

    EdgeFaaS: A Function-based Framework for Edge Computing

    Neha Vadnere, Yu-Ting Wang, Yitao Chen, Sreehari Sadesh, Ming Zhao

    cs.DC · cs.AI

    Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution. This paper proposes EdgeFaaS, a novel function-based edge computing framework to enable edge applications to effectively utilize heterogeneous...

    arxiv.org/abs/2607.14489 · PDF

  5. 05

    Settling The Round Complexity of Byzantine Agreement Against a Full-Information, Adaptive Adversary

    Yuval Efron

    cs.DC

    We prove that every randomized synchronous Byzantine Agreement protocol in the full-information, strongly adaptive adversary model, secure against $t$ corrupt parties, has worst-case expected round complexity \[ Ω\!\left(\frac{t^2}{n\log(n+1)}\right). \] This improves upon the seminal $Ω(\frac{t}{\sqrt{n\log n}})$ bound of [Bar-Joseph, Ben-Or 98]. Our result matches the recent upper bound of $O\left(\min\left\{\frac{t^2\log...

    arxiv.org/abs/2607.14413 · PDF

  6. 06

    DRIFT: Direct Reduced Fourier Transforms for Distributed Spectral Neural Operators

    Sana Taghipour Anvari, David Kaeli

    cs.DC

    Fourier Neural Operators (FNOs) learn solution operators for partial differential equations and offer orders of magnitude speedup over traditional numerical solvers at inference time, which makes them attractive surrogates for high-resolution computational physics. Scaling FNOs to high-resolution spatial grids requires distributing the data across GPUs, but the distributed FFT at the core of each spectral layer requires multiple dense...

    arxiv.org/abs/2607.14394 · PDF

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