cs.DC · 2026-09-09 · No. 110

Distributed, Parallel, and Cluster Computing, 2026-09-09.

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

    ContinuumBench: Benchmarking Joint Autoscaling and Placement Across Evaluation Regimes in the Cloud-Edge Continuum

    Lanpei Li, Antonino Vaccarella, Vincenzo Lomonaco, Massimo Coppola

    cs.DC

    Cloud-edge controllers coordinate service placement, replica scaling, and resource pre-warming to keep end-to-end latency within application deadlines. But evaluations often obscure the source of a reported gain: placement and scaling are studied separately; workload, connectivity, and calibration assumptions remain implicit; and metrics over completed tasks hide unfinished work. We present ContinuumBench, a benchmark that controls these...

    arxiv.org/abs/2609.08946 · PDF

  2. 02

    Exploring the Genesis Platform Capabilities to Accelerate Scientific Discovery in OPAL

    Daniel Rosendo, Renan Souza, Kelsey Carter, John Lagergren, Frédéric Suter, Shelaine L. Curd, David Weston, Rafael...

    cs.DC

    Autonomous, cross-facility science requires capabilities that no individual project should have to build for itself: managed execution for long-lived services, versioned distribution of models to remote compute systems, governed access to large language models, a shared substrate for experimental data, and end-to-end provenance. The U.S. Department of Energy Genesis Mission platform, delivered through the American Science Cloud, provides...

    arxiv.org/abs/2609.08844 · PDF

  3. 03

    Tools-CC-Bench: a Benchmark Suite for Collective Communication with Compression in HPC and AI Workloads

    Haozhe Fan, Wei Wang, Xingchen Liu, Man Liu, Xingjian Tian, Haoquan Long, Zedong Liu, Daran Sun, Jinwu Yang, Bo...

    cs.DC

    Distributed HPC and LLM workloads increasingly require efficient communication for scalability, yet growing data movement has become a major performance bottleneck. Communication compression can reduce this overhead and complement execution-level optimizations, but its benefits remain difficult to assess because existing benchmarks lack support for diverse backends, realistic datasets, application-specific accuracy metrics, and...

    arxiv.org/abs/2609.08739 · PDF

  4. 04

    Measuring Sustainability in Multi-Scale High-Performance Computing

    Carlos J Barrios, Frédéric Le Mouël, Yves Denneulin

    cs.DC

    The transition from traditional High Performance Computing (HPC) to the Computing Continuum emphasizes efficient resource management and sustainable practices across Multi-Scale hybrid architectures. This paper introduces a multidimensional metric framework to characterize these systems and guide deployment strategies for modern workloads. The framework combines Architectural Performance metrics (such as Throughput, Latency, Scalability),...

    arxiv.org/abs/2609.08688 · PDF

  5. 05

    Sample-Guided Exact Top-K Selection for Long-Context Sparse Attention

    Siran Liu, Yang Xue, Theo Tang, Changxu Shao, Qian Cheng, Haimeng Ren, Donghua Jiang, Haipeng Ming, Lehua Ding,...

    cs.DC

    Sparse attention bounds downstream attention work by retaining a fixed-size subset of indexed tokens, but its standalone exact Top-$K$ stage must still process materialized score rows whose length grows with context. Production radix selectors discover their first actionable boundary only after a complete-row pass, forcing another row-scale traversal before exact refinement. We observe that locating a compact upper tail requires substantially...

    arxiv.org/abs/2609.08450 · PDF

  6. 06

    A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware

    Maysam Khatib, Moysis Symeonides, Demetris Trihinas, George Pallis, Marios D. Dikaiakos

    cs.DC · cs.AI · cs.LG

    Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires balancing quality, latency, model footprint, and energy. This paper presents a controlled measurement study of self-hosted LLM inference across edge and near-edge deployment nodes: an NVIDIA Jetson AGX Orin and a near-edge server with CPU-only and GPU-enabled inference modes. We evaluate multiple...

    arxiv.org/abs/2609.08307 · PDF

  7. 07

    SemBridge: Compiling Consumer Observations into Cross-Stack Communication Plans

    Genlang Chen, Junyi Zhu, Yuanshan Lin

    cs.DC

    Distributed-tensor systems specify where values reside, while collective systems optimize how requested operations execute. At a boundary between vendor runtimes that cannot share a native communicator, neither abstraction states what a remote consumer must observe. SemBridge fills this gap by compiling graph and runtime facts into a typed contract for the consumer-visible result and its delivery obligations. The contract captures provenance,...

    arxiv.org/abs/2609.08231 · PDF

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