cs.DC · 2026-08-08 · No. 78

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

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

01 — The papers

5 entries
  1. 01

    Routing LLM Inference to the Cleanest Grid in Real Time

    Aleks Bernhard, Arif Baran Yardimci

    cs.DC · cs.PF

    Large-language-model inference is a fast-growing electricity load whose marginal carbon intensity varies by more than an order of magnitude across grid regions and across the day, making request placement an attractive lever: no retraining, no hardware change. We report a live validation of carbon-aware inference routing on multi-region GPU testbeds driven by marginal operating emissions rate (MOER) signals, with three properties uncommon in...

    arxiv.org/abs/2608.06188 · PDF

  2. 02

    TensorCast: The Missing Tensor Management Layer in Large Language Model Infrastructure

    Yuhan Zhou, Yuchu Luo, Hao Nie, Wangrunze Lv, Yu Zhou, Yibo Zhu, Daxin Jiang, Chenren Xu

    cs.DC

    Modern LLM infrastructure increasingly manages tensors not only as computation data, but also as persistent states shared across distributed components. Existing systems optimize individual tensor management tasks, such as model weight loading, KV cache management, and checkpoint synchronization, by deeply integrating task-specific mechanisms with execution engines, networks, or storage backends. However, this specialization creates isolated...

    arxiv.org/abs/2608.06007 · PDF

  3. 03

    Operating Multi-Node Full Fine-Tuning on NVIDIA B300: A Field Report on Telemetry-Based Triage, Negative Results, and Operational Hardening

    Seon Ho Kim, Ui Jeong Jeon, Su Hyeon Kim, Min Tae Hwang

    cs.DC · cs.LG

    We report operational experience full-fine-tuning a 32.76B-parameter dense model (Qwen3-32B) on 16 x NVIDIA B300 (two nodes, FSDP / ZeRO-3) -- among the first published field accounts on this accelerator. We claim no new algorithm. The individual mechanisms we use are established practice; our contribution is the integrated field experience and a set of calibrated measurements on new hardware. Concretely we offer four practitioner artifacts....

    arxiv.org/abs/2608.05944 · PDF

  4. 04

    RepoOMP: Repository-Aware Hotspot OpenMP Parallelization via Dependency-Aware Context Reduction

    Yongjie Qian, Ke Gao, Zhibin Zhang, Shaohui Peng, Ling Li

    cs.DC

    OpenMP parallelization of hotspots in mature repositories remains difficult because loop safety and optimization payoff often depend on non-local evidence. Rule-based tools under-parallelize when legality is not locally provable, while agent-based approaches become unstable when retrieval misses decisive dependencies or includes irrelevant code. We present RepoOMP, a hybrid framework that recovers parallelization-relevant evidence before...

    arxiv.org/abs/2608.05855 · PDF

  5. 05

    Serverless platform driven CPU loadbalancing

    Abdul Rehman

    cs.DC

    Serverless platforms maintain a global view of function invocations and resource utilization, yet existing systems largely restrict CPU scheduling decisions to the operating system scheduler. This paper presents a serverless platform-driven CPU load balancing framework that enables the control plane to directly influence CPU scheduling through a custom Linux scheduler built on SchedExt(SCX). The proposed scheduler introduces configurable...

    arxiv.org/abs/2608.05633 · PDF

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