cs.DC · 2026-08-03 · No. 73
Distributed, Parallel, and Cluster Computing, 2026-08-03.
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
SLIM: Saturation-Aware Lightweight Performance Modeling for LLM Serving
Pol G. Recasens, Ferran Agullo, Yue Zhu, Chen Wang, Jordi Torres, Josep Ll. Berral
cs.DC · cs.PF
Large language model (LLM) serving commonly increases batch size to improve throughput, but performance eventually reaches a deployment-dependent plateau beyond which larger batches provide marginal gains while increasing latency and GPU memory consumption. Previous studies have attributed this behavior to HBM/DRAM bandwidth limitations, but the underlying causes have primarily been supported by conceptual arguments or high-level performance...
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02
System-Wide Termination in Distributed Betweenness Centrality Computation
Siamak Abdi, Lucia Cavallaro, Giuseppe Di Fatta
cs.DC
Computing betweenness centrality on large networks is inherently expensive, as it requires aggregating shortest-path dependencies across all pairs of vertices and becomes increasingly difficult to scale as network size grows. Scalable distributed algorithms can facilitate such computations, particularly when centralised processing is not feasible, and message exchanges must be carefully controlled, for example, in bandwidth-limited or very...
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03
CARA: Exact Local Repair with Fresh One-Action Certification for Cloud Consolidation
Xiyang Zhang, Yuanhe Tian, Hongzhi Wang
cs.DC
Simulator-based placement pipelines may inspect many repairs but deploy only when several reliability criteria improve together. Reusing search scenes to test the selected action invalidates nominal evidence, while scalarization can trade away the weakest criterion. We introduce Certificate-Aligned Recomposition (CARA), an incumbent-anchored pipeline that separates adaptive proposal generation from a one-use deployment decision. In a bounded...
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04
Allocation Tracking and Parameter Checking for Parallel Programming Models using Contracts
Yussur Mustafa Oraji, Christian Bischof
cs.DC
Correctness checking tools for High-Performance Computing programs are typically limited to specific parallel programming models such as MPI or OpenSHMEM. The CoVer framework previously addressed this by introducing a generic, contract-based approach that decoupled API requirements from the core tool. However, CoVer's effectiveness remains bounded by the expressiveness of its underlying contract language, restricting the types of errors it...
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05
Knox: Fortifying Smart Spaces With Safety Guarantees
Rishabh Menezes, Jadon T. Schuler, Kaimeng Zhu, Oliver Rogalski, Indranil Gupta
cs.DC
Internet of Things (IoT) devices in smart spaces and buildings are an emerging class of distributed systems with critical safety requirements. This paper presents Knox, the first system to enable safety checking in IoT-enabled smart spaces. Knox's contributions include (i) safety specifications: a new language for safety clauses in such smart spaces, and (ii) static safety checking: two new algorithms for static verification of multiple...
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06
Rethinking AI Cloud Infrastructure for Agentic Serving Systems with the Aries Experimentation Framework
Leonid Kondrashov, Hongrui Liu, JooYoung Park, Boxi Zhou, Zonghao Liu, Chengzhi Lu, Riccardo Mancini, Esha Choukse,...
cs.DC
Autonomous agents challenge conventional LLM serving by coupling repeated inference with persistent context and sandboxed tool execution. We present Aries, a full-stack experimentation framework that separates task semantics from execution configurations, reconstructs cross-component agent trajectories with correlated system telemetry, and exposes stateful tool execution through a consistent interface across heterogeneous sandbox substrates....
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07
LayoutBench: Performance Benchmarking of Cloud Storage Layouts for Multimedia Data
Debopam Sanyal, Hongjie Chen, Alexey Tumanov, Joshua Kimball
cs.DC · cs.DB · cs.LG
Modern multimedia machine learning workloads increasingly store large-scale datasets in cloud object storage services such as AWS S3. How these samples are physically organized in storage (i.e.,storage layout) directly affects how quickly and cheaply they can be retrieved. Yet the benchmarks used to guide storage decisions today focus on database engines and query processing, and none systematically evaluates how different storage layouts...
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08
DeltaServe: Host-Agnostic Co-Serving of Inference and Fine-Tuning for LLMs
Jiaxuan Chen, Jianshu She, Ye Yuan, Rajat Ghosh, Karan Gupta, Qirong Ho, Xue Liu, Oana Balmau
cs.DC · cs.LG
LLM serving systems are provisioned for peak load to meet strict latency targets, leaving substantial GPU compute idle whenever traffic falls below peak. We present DeltaServe, a host-agnostic co-serving design that converts this idle inference capacity into LoRA fine-tuning throughput while preserving inference service-level objectives (SLOs). DeltaServe integrates with existing inference engines through a compact hook interface that...
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