cs.DC · 2026-09-03 · No. 104

Distributed, Parallel, and Cluster Computing, 2026-09-03.

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
  1. 01

    AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference

    Yida Zhang, Zhiyong Gao, Shuaibing Yue, Jie Li, Rui Wang

    cs.DC

    Deploying Large Language Models (LLMs) on edge devices typically relies on model compression or split inference. However, compression degrades reasoning capabilities, while split inference suffers from severe Wide Area Network (WAN) communication bottlenecks. Edge-cloud speculative decoding emerges as a promising alternative, leveraging an edge small model to draft tokens for cloud verification. Yet, over volatile WANs, inevitable prediction...

    arxiv.org/abs/2609.02514 · PDF

  2. 02

    Federated Learning on the American Science Cloud using APPFL

    Zilinghan Li, Abhijit Chunduru, Harinarayan Krishnan, Eric Chagnon, Peter Nugent, Kibaek Kim, Ravi Madduri

    cs.DC

    The American Science Cloud (AmSC), established under the Genesis Mission of the U.S. Department of Energy (DOE), aims to integrate DOE high-performance computing systems, experimental facilities, and data resources into a single, coordinated, AI-driven discovery platform. AmSC's early services focus on curated artifacts, such as gated inference access to hosted models, experiment tracking, and function execution across computing facilities....

    arxiv.org/abs/2609.02238 · PDF

  3. 03

    Towards Global Federated Genome-Wide Association Meta-Analysis Using GA4GH TES

    Abhijit Chunduru, Matthew Joel, Zilinghan Li, Ravi Madduri

    cs.DC

    Genome-wide association studies (GWAS) gain statistical power from large, ancestrally diverse cohorts, but privacy regulations and data-residency constraints often prevent genomic data from being centrally pooled across institutional or national borders. We present a privacy-preserving federated GWAS meta-analysis pipeline built on the APPFL framework, in which each site computes local GWAS summary statistics and transmits only aggregate...

    arxiv.org/abs/2609.02227 · PDF

  4. 04

    MeanField Surrogate Modeling for Scalable Runtime Scheduling of Concurrent Heterogeneous AI Inference on Shared GPUs

    Youssef Ennouri, Soonhoi Ha

    cs.DC · cs.AI

    Deploying heterogeneous AI models concurrently on a shared GPU introduces resource contention that complicates runtime scheduling. While surrogate models avoid costly online benchmarking, their profiling requirements typically grow combinatorially with the number of co-running models, limiting scalability. We propose a MeanField surrogate that predicts per-model performance from local configuration and aggregate GPU state rather than...

    arxiv.org/abs/2609.02109 · PDF

  5. 05

    RT-HiSS: Ray Tracing Accelerated High Dimensional Vector Similarity Searches

    Revanth Reddy Munugala, Michael Gowanlock

    cs.DC · cs.DB

    Recent GPU generations include special-purpose ray tracing (RT) cores for graphics applications. While RT cores are primarily used for rendering, recent works show they can be leveraged for general-purpose tasks, including similarity searches. However, existing approaches do not support datasets exceeding three dimensions. In this work, we propose RT-HiSS, the first exact GPU RT-core-based similarity search algorithm for high-dimensional...

    arxiv.org/abs/2609.01975 · PDF

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.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.