cs.DC · 2026-08-24 · No. 94

Distributed, Parallel, and Cluster Computing, 2026-08-24.

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

    GrAND: GPU-based Dynamic Graph Indexes for Approximate Nearest Neighbour Search

    Karthik Venkatasubba, Shivendra Deshpande, Shivram S, Jyothi Vedurada

    cs.DC · cs.DB

    Modern Approximate Nearest Neighbour Search (ANNS) applications operate over continuously evolving vector collections and require graph indexes that sustain high-throughput searches while incorporating insertions and deletions with high recall. However, most GPU graph indexes are static or provide limited update support. Updates require neighbour discovery, reverse-edge creation, pruning, and deletion-induced graph repair; executing these...

    arxiv.org/abs/2608.21163 · PDF

  2. 02

    HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization

    Jinghao Wang, Qiqi Gu, Chenpeng Wu, Jianguo Yao, Haibing Guan, Xijun Li

    cs.DC · cs.AI

    High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolves rapidly, developing efficient methods for automated GPU kernel generation and optimization has become increasingly important. Existing LLM-based methods typically optimize within a fixed implementation space, limiting either optimization flexibility or search efficiency. We propose...

    arxiv.org/abs/2608.21157 · PDF

  3. 03

    Integrating a Python Dynamical core into ICON

    Mauro Bianco, Till Ehrengruber, Enrique González Paredes, Andreas Jocksch, Christos Kotsalos, Ioannis Magkanaris,...

    cs.DC

    The transition of Earth-system models to exascale is often hindered by rigid, monolithic Fortran codebases and maintenance-heavy compiler directives. While high-level DSLs offer a solution, they frequently fail due to cumbersome integration. We present the integration of a Python-based ICON dynamical core into the original Fortran simulation code. Leveraging the GT4Py DSL and the Data-Centric (DaCe) optimization framework, we demonstrate that...

    arxiv.org/abs/2608.21150 · PDF

  4. 04

    AI Infrastructure in Space: How Far Can We Go?

    Qing Li, Qiyang Zhang, Daliang Xu, Tianze Huang, Dingge Zhang, Yihao Zhao, Xiaolong Huang, Jinfeng Wen, Xiameng Hu,...

    cs.DC

    Satellites are becoming programmable computing platforms capable of running increasingly demanding AI workloads. This shift raises a systems problem: how can AI services remain deployable, manageable, and recoverable after launch when compute capacity, connectivity, energy, and thermal headroom vary over orbital time? This paper develops a systems vision for AI infrastructure in space. We define it as the systems layer that manages AI...

    arxiv.org/abs/2608.21034 · PDF

  5. 05

    PRICE: Pricing-based Resource Incentives for Quality-of-Result-aware Computing at the Edge

    Uwe Gropengießer, Sebastian Frenz, Max Mühlhäuser

    cs.DC

    Edge nodes are capacity-constrained by design, yet many edge workloads can trade result quality for resource efficiency at runtime. Existing edge pricing mechanisms largely treat requests as fixed-configuration submissions and rarely exploit per-request quality flexibility under overload. We present PRICE, an incentive mechanism that couples a utilization-dependent price signal to per-request quality selection. As utilization increases,...

    arxiv.org/abs/2608.20819 · PDF

  6. 06

    MEMPOWER: Efficient Power Management with Fine-grained Memory Analysis and Modeling for HPC Workloads

    Nanda Velugoti, Joseph Manzano, Andres Marquez, Nathan Tallent, Kyle Hale

    cs.DC

    Managing the energy consumption and power efficiency of parallel applications is a significant issue in both HPC environments and in the cloud. As emerging applications continue to push against the memory wall of modern machines, the growing imbalance between compute and data movement creates new opportunities to intelligently tune CPU power consumption. Unfortunately, existing frequency and voltage scaling techniques do not adequately...

    arxiv.org/abs/2608.20734 · PDF

  7. 07

    Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores

    Xiang Fu, Jixiang Ma, Xinpeng Zhang, Peng Zhao, Shuai Lu, Xu Tony Liu

    cs.DC · cs.CV

    Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and GPU hardware. Existing GPU convolution methods suffer from large memory overhead, poor cache utilization, limited effectiveness across kernel sizes, or numerical instability. This work extends the im2win paradigm -- a universal, memory-efficient convolution method with contiguous memory access for...

    arxiv.org/abs/2608.20725 · PDF

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