cs.DC · 2026-08-05 · No. 75
Distributed, Parallel, and Cluster Computing, 2026-08-05.
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-
01
When Does Disaggregation Pay? Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference
Przemyslaw Forys, Haoran Wu, Can Xiao, Jiayi Nie, Tony Liu, Rika Antonova, Timothy Jones, Robert Mullins, Wayne Luk,...
cs.DC
Agentic inference now dominates the LLM inference landscape, requiring LLMs to actively engage in multi-turn interactions with tool-calling capabilities. This introduces a more complex workload for the underlying inference system: serving stages such as prefill and decode exhibit substantially different behaviors and demand distinct compute and memory-bandwidth capabilities. As a result, a single homogeneous GPU system now struggles to...
-
02
Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph
Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore Trani
cs.DC · cs.LG
This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms originally designed for static graphs. Built on the NVIDIA RAPIDS ecosystem, the framework enables the characterization and tracking of communities in snapshot-based dynamic graphs, either by Leiden greedy optimization with multi-GPU support via Dask-based workload distribution, or...
-
03
TAOT: Topology-Aware Optimal Transport for Dynamic Expert Replica Placement in MoE Training
Lingyun Zhang, Henghua Zhang, Shilei Gu, Kai Mo, Shuai Han, Shiyong Li, Yanpeng Wang, Dou Shen
cs.DC
Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs), yet its dynamic routing causes severe load imbalance in expert-parallel training. Existing dynamic-replica methods copy hot experts onto idle ranks to share computation, but they optimize load balance alone and ignore the cost of moving expert weights across a multi-node topology, so the resulting cross-node communication can outweigh the...
-
04
FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations
Ziwu Liu, Inês Pinto Gouveia, Rehana Yasmin, Paulo Esteves-Verissimo, Ali Shoker
cs.DC · cs.LG
Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or synchronized training inefficient and hard to scale. To address this, we propose FedRings, a decentralized framework that organizes satellites into ring-based communication structures. It uses a spatio-temporal routing strategy with link-aware communication scheduling...
-
05
LPV Control for Dynamic Power Capping in High-Performance Computing under Mixed Workloads
Mohamed Abdeldjalil Maziz, Kouds Halitim, Bogdan Robu, Sophie Cerf
cs.DC
Balancing energy consumption and performance remains a critical challenge in High Performance Computing (HPC) systems. While static power capping mechanisms such as Intel's Running Average Power Limit (RAPL) offer basic control capabilities, they lack the flexibility to adapt to dynamically varying workloads. This work studies dynamic power regulation for mixed workload scenarios. We investigate two feedback strategies: a gain scheduled...
This edition is part of The Daily Abstract — cs.DC archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.
#D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.