cs.DC · 2026-07-30 · No. 69

Distributed, Parallel, and Cluster Computing, 2026-07-30.

8 new papers in cs.DC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

8 entries

01

InferScale: GPU-Native KV Injection for Personalized LLM Serving

Peter Li, Prashant Pandey

cs.DC · cs.LG

Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histories, that is shared across a user's many requests. Production memory systems (e.g., Mem0, MemGPT, and Zep) retrieve a relevant subset of this memory and inject it into the prompt, forcing the serving engine to repeatedly prefill the same content. As the retrieval budget grows, time-to-first-token...

arxiv.org/abs/2607.27090 · PDF
02

Hybrid Workflow Composition for Extreme-Scale Data Processing: A Case Study on the HL-LHC (Extended Version)

Alan Malta Rodrigues, Douglas Thain

cs.DC · cs.SE

High-Throughput Computing (HTC) environments tailored for high-concurrency resource efficiency require sophisticated orchestration to manage petabyte-scale data across heterogeneous resources. A critical but often overlooked challenge is workflow composition: the strategic grouping of tasksets within a Directed Acyclic Graph (DAG) to mitigate execution overhead while maximizing resource utilization. This paper presents a novel simulation...

arxiv.org/abs/2607.26877 · PDF
03

Mind the Gap: The Disconnect Between Synthetic and Natural Edge Weights in Parallel Single-Source Shortest Path

Marco D'Antonio, Thai Son Mai, Hans Vandierendonck

cs.DC

Scientific research works often evaluate Parallel Single-Source Shortest Path (SSSP) algorithms using synthetic, uniformly distributed edge weights. However, real-world graphs exhibit very different, often heavy-tailed, weight distributions. This creates a disconnect between how algorithms are evaluated and their real-world performance, since most SSSP implementations inherently rely on the weight distribution for parameter tuning and work...

arxiv.org/abs/2607.26821 · PDF
04

Nix to the Rescue for a Reproducible HPC-AI Software Stack

Wenke Du, Jean-Marc Gratien, Raphael Gayno, Bruno Raffin

cs.DC

Reproducibility in HPC remains difficult under the constraints of production supercomputers: no root access, limited internet, and software stacks that increasingly span C/C++, Fortran, Python, MPI, and GPU runtimes. Traditional approaches based on environment modules and Conda require manual intervention to locate dependencies, leak system libraries into builds, and fail to compose across projects. Containers help with deployment but do not...

arxiv.org/abs/2607.26688 · PDF
05

ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform

Xiaoxiao Jiang, Suyi Li, Sheng Yao, Tianyu Feng, Lingyun Yang, Dapeng Nie, Haoran Yang, Wei Wang

cs.DC · cs.AI

Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently. Existing platforms typically deploy each workflow as an opaque GPU function, provisioning, placing, and scaling all constituent models in the workflow together. This monolithic design obscures workflow structure, inflates scaling overhead, forces users to manage low-level GPU...

arxiv.org/abs/2607.26566 · PDF
06

Safety-Gated Autoscaling: A Multi-Layered Defense Architecture for Kubernetes Vertical Resource Optimization

Azra Karakaya, Erva Şengül, Ahmet Kaplan

cs.DC

Kubernetes is the standard platform for orchestrating containerized applications, yet resource management remains difficult. To stay safe, engineers over-provision CPU and memory, leaving reserved but unused capacity that is the main source of wasted cost. The built-in Horizontal and Vertical Pod Autoscalers are reactive: they act only after a threshold is crossed, which causes lag, over-provisioning, and can mask software defects by granting...

arxiv.org/abs/2607.26503 · PDF
07

DualDecoder: Accelerate Long Context LLM Inference by Predictive Prefetch

Zuning Liang, Zhiyi Yao, Qi Chen, Yuedong Xu, Hao Dai, Zhiqiang Ding, Tongkai Yang, Jinlong Hou, Yuan Cheng

cs.DC

Long-context inference is becoming a fundamental capability for modern LLM serving, especially driven by emerging agentic applications. Yet it faces a severe memory wall that the KV cache scales proportionally with increasing context length and request concurrency. Existing sparse KV cache methods offload most KV entries to host memory and retrieve only the critical KV entries needed by each decoding step. However, they commonly introduce...

arxiv.org/abs/2607.26475 · PDF
08

StrataCL: Fabric-Native Communication Library for Production Supernodes

Tiancheng Hu, Jin Qin, Yuzheng Wang, Ke Liu, TangShengsheng Li, Sheng Wang, Zhongzhe Hu, Tianlun Hu, Wei Wang, Lijun...

cs.DC

Modern distributed AI workloads run across hundreds of accelerators, making communication a major bottleneck. Existing communication libraries remain largely buffer-centric because user and communication buffers are managed separately, causing redundant data copies or costly user-buffer registration. This paper presents StrataCL, a zero-redundancy and fabric-native communication library for production supernodes. StrataCL introduces...

arxiv.org/abs/2607.26444 · PDF

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