cs.DC · 2026-09-19 · No. 118
Distributed, Parallel, and Cluster Computing, 2026-09-19.
9 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
9 entries-
01
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Zhexiang Zhang, Minchen Yu, Yifan Sun, Xu Bai, Xingliang Yuan, Adel N. Toosi
cs.DC
Online image generation with Diffusion Transformers (DiTs) must meet latency service-level objectives (SLOs) while using GPU resources efficiently. Existing systems improve GPU utilization by batching multiple requests for joint execution. However, request-level batching offers limited control over batch size: batches may be too small to saturate GPU compute, while larger ones may violate latency SLOs. Globally coordinated scheduling...
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02
A Kubernetes-Native Request Router for Quality-Aware Inference Serving in the Computing Continuum
Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić, Ivana Podnar Žarko, Schahram Dustdar
cs.DC
We introduce Adaptive Score-based Routing Balancer (ASRB), a dynamic, score-based request routing mechanism for Kubernetes-based service deployments over the computing continuum. ASRB jointly considers infrastructure-level information, response time measurements, and application-level quality indicators, with a particular focus on serving Machine Learning (ML) workloads. For these workloads, ASRB balances requests over service instances...
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03
Accelerating Sharded Data Parallelism at Scale with Federated Learning
Gianluca Mittone, Marco Aldinucci
cs.DC · cs.AI · cs.PF
The symbiotic scaling of artificial intelligence models and high-performance computing systems continually creates algorithmic challenges in their convergence. Foundation models (FMs) are a crucial example, requiring months-long training on thousands of cutting-edge GPUs. Sharded data parallelism (DP) is the dominant strategy to accelerate such computations by splitting data and models across multiple GPUs. However, it incurs prohibitive...
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04
Distributed Edge Inference: an Experimental Study on Multiview Detection
Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke
cs.DC
Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely...
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05
P-GADMM: Parallel Group-Based ADMM for Asynchronous Optimization in Heterogeneous Edge Networks
Gaiguo Wei, Qingying Zhang, Heqiang Wang, Yu Zhang, Xiaoxiong Zhong
cs.DC
The Alternating Direction Method of Multipliers (ADMM) is widely used for distributed optimization, but its synchronous implementation can suffer from efficiency loss in heterogeneous edge networks, where fast clients or groups need to wait for slower ones before global updates can be completed. Existing group-based ADMM methods reduce communication overhead through grouping, but their grouping rules usually focus on data similarity or...
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06
VERA: Reinforcement Learning for Dynamic Memory Scaling of HPC Workloads in Kubernetes
Ade Pramono, Jie Ren, Ivy Peng
cs.DC
Memory over-provisioning results in resource underutilization when HPC workloads run on Kubernetes. The default Vertical Pod Autoscaler (VPA) cannot anticipate phase-driven memory spikes for first-run HPC jobs. In this work, we present a reinforcement learning (RL) recommender VERA that formulates vertical memory scaling as a Markov Decision Process and trains an agent on 3353 real Prometheus traces. Evaluated on a live Google Kubernetes...
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07
The Life of a Token: from Words to Bits on the Wire
Davide Avesani, Pengwenlong Gu, Sotiris Skaperas, Stefano Secci
cs.DC · cs.LG · cs.NI · cs.PF
Large Language Models (LLMs) transform vast collections of unstructured text into semantic patterns used for language generation and reasoning tasks. Behind their ease of use lies a complex process: words become tokens, tokens become vectors, and vectors ultimately give rise to streams of bits that flow through High-Performance Computing (HPC) systems. As modern LLMs grow to billions or trillions of parameters, this path increasingly unfolds...
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08
Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs
Wonmi Choi, Sunjae Park, Dohyeok Kwon, Zhixiong Niu, Yeonho Yoo, Chuck Yoo, Gyeongsik Yang
cs.DC
Collaborative fine-tuning on edge devices adapts large language models to domain-specific data while keeping each device's data local. State-of-the-art (SOTA) collaborative fine-tuning techniques are largely designed for GPU-based edge devices and rely on pipeline parallelism (PP). However, many edge platforms, including IoT gateways, smart-home hubs, and in-vehicle computers, are primarily CPU-based. This paper reports that PP is ineffective...
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09
Safe Exploration of Arbitrary Dynamic Dangerous Networks
Caterina Feletti, Paola Flocchini, Giuseppe Prencipe, Nicola Santoro
cs.DC
Given a team of agents on the nodes of a graph-based network, the exploration problem requires each node to be visited by at least one agent. In the classical distributed setting of static networks, agents do not know the topology of the network; in the more recently investigated setting of dynamic networks, they may have prior knowledge about the graph class (e.g., trees, tori, rings) or other parameters (e.g., number of nodes). Gotoh et al....
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