cs.DC · 2026-06-08 · No. 17

Distributed, Parallel, and Cluster Computing, 2026-06-08.

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

    Twelve quick tips for designing AI-driven HPC workflows

    Jamie J. Alnasir

    cs.DC · cs.AI · cs.LG · cs.SE

    High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance. However, the pervasive integration of artificial intelligence (AI) and foundation models into scientific research has introduced a fundamentally new computational paradigm. AI-driven workflows are characteristically iterative, data-driven, and...

    arxiv.org/abs/2606.07491 · PDF

  2. 02

    Clairvoyant: Predictive SJF Scheduling to Mitigate Head-of-Line Blocking in Serial LLM Backends

    Aravind Sundaresan

    cs.DC

    Serial LLM inference backends -- such as Ollama -- process requests one at a time under FCFS admission, causing Head-of-Line Blocking (HOLB) under mixed workloads at high utilisation: short factual queries can be delayed by minutes behind long generation jobs. While cloud-scale deployments mitigate HOLB via continuous batching (vLLM, Orca), these solutions require tens of GB of VRAM for concurrent KV-caches -- infeasible for...

    arxiv.org/abs/2606.07248 · PDF

  3. 03

    Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions

    Bablu Kumar, Anshul Verma, Rajkumar Buyya

    cs.DC

    Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions...

    arxiv.org/abs/2606.07046 · PDF

  4. 04

    PCCL: Process Group-Aware Scalable and Generic Collective Algorithm Synthesizer

    William Won, Kartik Lakhotia, Madhu Kumar, Sudarshan Srinivasan, Tushar Krishna

    cs.DC

    Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many compute devices, which requires frequent collective communications to synchronize activations and parameter updates. Such collective communications have become a major bottleneck. While the performance of the collective algorithm depends on the physical network...

    arxiv.org/abs/2606.07019 · PDF

  5. 05

    Communication Strategy Selection for Multi-GPU 3D FDTD with Convolutional Perfectly Matched Boundary Layers

    Victory C. Obieke

    cs.DC · math-ph

    In this paper we describe a communication-strategy study for multi-GPU three-dimensional finite-difference time-domain computation with convolutional perfectly matched layer boundary conditions using CUDA. The metrics used to determine the most effective implementation include runtime, throughput in millions of output points per second, strong-scaling efficiency, CPML overhead, host-staged versus direct GPU-to-GPU exchange speedup, and...

    arxiv.org/abs/2606.06910 · PDF

  6. 06

    Terastal: Layer-Variant-based Scheduling for Real-Time Multi-DNN Workloads on Heterogeneous Accelerators

    Sing-Yao Wu, Fengshuo Song, Eli Bozorgzadeh

    cs.DC · cs.AR · cs.LG

    Heterogeneous DNN accelerators improve soft real-time multi-DNN execution by mapping each layer to its preferred accelerator to reduce latency. However, under skewed workloads, large layer-latency differences across accelerators limit scheduling flexibility and increase deadline misses. To address this challenge, we introduce layer variants, customized layer implementations that reduce latency gaps on non-preferred accelerators. We then...

    arxiv.org/abs/2606.06818 · PDF

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