cs.DC · 2026-08-13 · No. 83

Distributed, Parallel, and Cluster Computing, 2026-08-13.

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

01 — The papers

4 entries
  1. 01

    RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning

    Yibo Shen, Xudong Han, Xiaowei Zhu, Gen Li, Zhenxuan Pan

    cs.DC

    Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks. Optimizing either alone can shift the bottleneck to the other. In MoE RL, rollout-time routing replay exposes every sample's sequence length and layer-wise expert demand...

    arxiv.org/abs/2608.12146 · PDF

  2. 02

    Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control

    Josef Liyanjun Chen

    cs.DC · cs.AI · cs.OS

    LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect. We ask when this control path exposes enough concurrent work for GPU execution, and what changes when a GPU-computed route decision remains on device. We formalize the ready-cohort boundary using fixed-partition share F, exact offline share P*, local upper bound U, and online achieved...

    arxiv.org/abs/2608.12123 · PDF

  3. 03

    Evaluating OpenMP Offloading for Intra-node Multi-GPU Programming across NVIDIA, AMD, and Intel Architectures: A 3D Heat Transfer Case Study

    Ezhilmathi Krishnasamy

    cs.DC

    Currently, most supercomputers are equipped with GPUs from manufacturers such as NVIDIA, AMD, or Intel, which provide substantial parallelism and high throughput. It is common for a single compute node (intra-node) to host multiple GPUs, typically four or more. Therefore, effectively leveraging all these GPUs within a single compute node is essential for applications in scientific and engineering domains. However, several factors must be...

    arxiv.org/abs/2608.11882 · PDF

  4. 04

    User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling

    Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon, Praveen Kumar Donta

    cs.DC · cs.AI · cs.LG · cs.NI · cs.PF

    Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. We propose a collaborative distributed inference system combining dedicated infrastructure with resources contributed by service users. Dedicated resources provide baseline capacity for maintaining quality of service (QoS), while volunteered resources absorb increasing demand without proportional...

    arxiv.org/abs/2608.11840 · PDF

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