cs.DC · 2026-06-15 · No. 24
Distributed, Parallel, and Cluster Computing, 2026-06-15.
10 new papers in cs.DC. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
10 entries-
01
parRSB: Exascale Spectral Element Mesh Partitioning
Thilina Ratnayaka, Paul Fischer
cs.DC
We introduce parRSB - a parallel, highly scalable graph partitioner for spectral element meshes that produce high quality partitions. parRSB is based on Recursive Spectral Bisection (RSB) algorithm implemented on the dual graph of the input mesh. RSB uses the Fiedler vector, which is the eigenvector associated with the smallest non-zero eigenvalue of the Laplacian matrix of the dual graph for making partitioning decisions and tries to...
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02
Simple-IT: Practical Low-Latency Signature-Free BFT Consensus
Qianyu Yu, Juan Villacis, Giuliano Losa, Zhuolun Xiang, Xuechao Wang
cs.DC
Recent advances in quantum computing pose a looming threat to most current Byzantine fault-tolerant (BFT) consensus protocols, which rely on quantum-vulnerable public-key signature schemes such as Ed25519 and BLS12-381. Instead of switching to much more expensive post-quantum secure signature schemes, an alternative is to use signature-free protocols, which rely only on cheap, post-quantum secure authenticated channels. In this paper, we ask...
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03
Simulation-Based Performance Evaluation of Sharded Blockchain Architectures
Om Amit Gandhi, Ioan Raicu
cs.DC
Public blockchains continue to struggle with scalability because improving throughput is not as simple as increasing block size or reducing block interval. Larger blocks increase validation and transmission cost, while shorter intervals raise the likelihood of propagation delays, forks, and stale blocks. These limits motivate sharding, where transaction processing is divided across multiple parallel shard groups. In this work, we present a...
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04
PLAIground: SLO-Driven Runtime Model Selection for Compound AI Systems in the Edge-Cloud-Space Continuum
Milos Gravara, Cynthia Marcelino, Andrija Stanisic, Stefan Nastic
cs.DC · cs.AI
Applications in the 3D Computing Continuum, which unifies edge, cloud, and space, require combining multiple AI tasks such as object detection, time-series analytics, and natural language processing into Compound AI systems. These systems must satisfy stringent Service Level Objectives (SLOs) on accuracy, latency, and cost. A key mechanism for maintaining SLO compliance of Compound AI systems is runtime model selection, where AI models are...
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05
Design Methodology and Performance Trade-offs Management for Distributed and Compound AI Systems
Milos Gravara, Andrija Stanisic, Stefan Nastic
cs.DC · cs.AI
Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost. The prevailing model-centric approaches select a monolithic model at design time and apply identical computation regardless of input difficulty, cannot decompose tasks across specialized components, and have knowledge that is fixed at training time. During runtime, this can lead to performance degradation and increasing...
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06
On the Limits of Causal Observation in Shared-Memory Systems
Gilde Valeria Rodríguez, Armando Castañeda, Miguel Piña
cs.DC
Determining whether one concurrent operation completed before another began is a fundamental prerequisite for reasoning about the correctness of concurrent systems. We formalize this challenge as the Causal Observability Problem (COP): assign timestamps to the observable boundary events of a concurrent execution, invocations and responses, that faithfully reflect real-time operation order. A solution is complete if it never misses a genuine...
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07
Extreme-Scale Atomistic Simulation of Real-Temperature Magnetic Skyrmion Dynamics by Coupled Spin-Lattice Modeling
Pin Chen, Cheng-bing Chen, Hai Liu, Yuewen Huang, Kangyou Zhong, Hai-Jun Zhao, Liu-Liu Han, Guixin Guo, Jiang Li,...
cs.DC · cond-mat.mtrl-sci
Real-temperature topological magnetic dynamics in functional materials is governed by coupled lattice and spin evolution, yet remains inaccessible to predictive simulation at device-relevant scales. As a flagship example, thermally driven helix-to-skyrmion transformation in FeGe requires atomistic resolution, explicit lattice motion, and micrometer-scale domains to resolve device-scale topological texture formation. We combine a...
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08
VaultxGPU: GPU-Accelerated Blockchain Consensus
Samuel Taiwo Fatunmbi, Om Amit Gandhi, Luke Logan
cs.DC
Blockchain consensus mechanisms based on Proof-of-Work consume significant energy, with Bitcoin alone estimated at approximately 150 TWh per year. Proof-of-Space reduces this cost by replacing repeated computation with storage, but plot generation remains bottlenecked by CPU hashing throughput. Prior work on VaultX demonstrated a high-performance CPU-based Proof-of-Space plotter using multi-threaded Blake3 hashing, achieving plotting speeds 4...
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09
STREAM: Multi-Tier LLM Inference Middleware with Dual-Channel HPC Token Streaming
Anas Nassar, Steve Mohr, Leonard Apanasevich, Himanshu Sharma
cs.DC · cs.AI
Researchers and practitioners working with large language models face a fragmented landscape: local models are free and private but hardware limits the model size and context windows a researcher can use; institutional HPC centers offer powerful GPU resources at no marginal cost and keep data within institutional boundaries, but operate behind firewalls and are designed for batch jobs rather than interactive use; commercial cloud APIs provide...
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10
Lattice Aggregation in Distributed Verification under Crash and Byzantine Failures
Gilde Valeria Rodríguez, Borzoo Bonakdarpour, Armando Castañeda, Sergio Rajsbaum
cs.DC
We introduce c-Lattice Aggregation, a fault-tolerant reconstruction problem for distributed verification under crash and Byzantine failures. In our setting, n asynchronous processes supervise a concurrent execution I: each process holds a local sample, and must collaboratively reconstruct I from partial, potentially overlapping observations. A protocol solves c-Lattice Aggregation if at least c correct processes output the complete execution...
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