cs.DC · 2026-08-11 · No. 81

Distributed, Parallel, and Cluster Computing, 2026-08-11.

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

    Defining Decentralization: An Ontological Perspective

    Jakub Kacper Szeląg, Aydin Abadi, Mohammad Naseri

    cs.DC · cs.AI · cs.LG · cs.LO · eess.SY

    Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of...

    arxiv.org/abs/2608.09748 · PDF

  2. 02

    SpSYRK: Half the Work in Distributed Sparse Matrix Multiplication

    Thomas McFarland, Julian Bellavita, Giulia Guidi

    cs.DC

    The symmetric rank-$k$ update (SYRK), $\C = \A\A^\top$, computes the dot product between each pair of rows of $\A$, producing the Gram matrix $\C$. Its sparse variant underpins similarity search in machine learning, graph analytics, and genomics, including Jaccard similarity on datasets too large for a single node. Yet, despite the symmetry in its inputs and outputs, existing distributed sparse matrix multiplication algorithms, such as Sparse...

    arxiv.org/abs/2608.09713 · PDF

  3. 03

    A Preliminary Study on Simultaneous Coscheduling for Discrete GPU vs. Fused GPU

    Poorna Gunathilaka, Nabayan Chaudhury, Kirshanthan Sundararajah, Wu-chun Feng

    cs.DC

    CPU-GPU coscheduling enables simultaneous execution of an application across both processing units, but its efficiency depends on workload partitioning and memory architecture. This preliminary study evaluates coscheduling on the NVIDIA GH200 Superchip compared to a discrete H100 PCIe platform. Using sparse conjugate gradient (CG) as a case study, we assess various work divisions across three memory-management paradigms: explicit copy,...

    arxiv.org/abs/2608.09647 · PDF

  4. 04

    $\tilde{\text{O}}$ptimal Distributed Maximum Flow Approximation in Undirected Planar Graphs

    Yaseen Abd-Elhaleem, Michal Dory, Oren Weimann

    cs.DC · cs.DS

    Persistent efforts in recent years have been devoted to devising distributed algorithms for fundamental optimization problems in planar graphs. In particular, for Single-Source Shortest-Paths, there is an $\tilde O(D^2)$-rounds exact algorithm [Li, Parter STOC'19] for directed planar graphs, and an $\tilde {O}(D)$-rounds $(1+o(1))$-approximation algorithm [Rozhon, Grunau, Haeupler, Zuzic, Li STOC'22] for undirected planar graphs (where $D$ is...

    arxiv.org/abs/2608.09500 · PDF

  5. 05

    How Accurately Can the Energy Use of Spark Applications Be Estimated Based on Resource Utilisation?

    Youssef Moawad, Kathleen West, Vasilis Bountris, Philipp Thamm, Yehia Elkhatib, Lauritz Thamsen

    cs.DC

    Distributed batch data processing applications are widely executed on cloud-based resources where restricted user access to node-level hardware energy counters hinders transparent sustainability accounting. Energy and carbon attribution methodologies therefore depend on power models and available resource utilisation traces, yet the accuracy of these estimates has to be validated while direct counters are available. In this work, we use...

    arxiv.org/abs/2608.09359 · PDF

  6. 06

    Beyond the Limits: Flexible and Congestion-Aware Cluster Scheduling for the Cloud

    Oliver Larsson, Thijs Metsch, Cristian Klein, Erik Elmroth

    cs.DC

    Workload scheduling in cloud environments often relies on simplistic assumptions about application resource needs and hardware utilization. Overlooking application-level performance objectives and hardware resource contention that leads to inefficient resource usage and degraded performance. This paper addresses two key limitations of current approaches. First, unnecessarily strict enforcement of service level objectives (SLOs) often leads to...

    arxiv.org/abs/2608.09308 · PDF

  7. 07

    UnionSparse: An Index-Efficient Sparsity Framework for Low-Bit Sparse LLM Inference on Edge

    Tianhao Jiang, Hang Gu, Teng Wang, Qianyu Cheng, ZhenDong Zheng, Cheng Tang, Qiyue Su, Wenqi Lou, Lei Gong, Chao...

    cs.DC

    Edge LLM inference combines sparsity and low-bit quantization to meet device memory, latency, and power limits. Yet quantization shrinks weight payloads without proportionally reducing sparse metadata, so index traffic and nonzero extraction become critical SpMM bottlenecks. We introduce the Payload-to-Metadata Ratio (PMR) and show that improving PMR raises effective compute intensity in decoding. We present UnionSparse, an index-efficient...

    arxiv.org/abs/2608.09291 · PDF

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