cs.DC · 2026-06-10 · No. 19

Distributed, Parallel, and Cluster Computing, 2026-06-10.

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

01 — The papers

7 entries
  1. 01

    Piper: A Programmable Distributed Training System

    Megan Frisella, Shubham Tiwari, Andy Ruan, Yi Pan, Parker Gustafson, Mat Jacob, Gilbert Bernstein, Stephanie Wang

    cs.DC · cs.AI

    Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO. Deployed systems for foundation model pretraining often rely on human experts to manually design a high-level parallelism strategy then implement the corresponding low-level execution strategy, making it difficult to adapt the system to new strategies....

    arxiv.org/abs/2606.11169 · PDF

  2. 02

    Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation

    Jason Crop, Hayden Moore, Sudeep Pasricha

    cs.DC · cs.AR

    As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sustainable growth. The long-standing assumption that "cooler is better", i.e., lower CPU temperatures reduce power, does not fully hold for modern low-voltage CPUs, where inverse temperature dependence (ITD) drives higher supply voltages at lower temperatures. This creates a non-monotonic performance-per-watt curve...

    arxiv.org/abs/2606.11163 · PDF

  3. 03

    A Neurosymbolic Prolog Skill for LLM-Driven Service Placement

    Jacopo Massa, Giuseppe Bisicchia, Patrizio Dazzi, Antonio Brogi

    cs.DC

    Service placement in the cloud-edge continuum requires assigning application components to heterogeneous resources under multiple constraints, including latency, locality, and policy requirements. Existing approaches rely on optimisation models or heuristics that require explicit modelling, while neural methods lack transparency and formal guarantees. This work proposes a neuro-symbolic alternative based on a Prolog skill, a reusable...

    arxiv.org/abs/2606.11113 · PDF

  4. 04

    FairWave : A Fairness-Aware Asynchronous DAG-BFT Consensus

    Syariful Mujaddiq

    cs.DC

    Combining asynchronous Byzantine Fault Tolerant (BFT) consensus with Proof-of-Stake (PoS) creates a trilemma between Sybil resistance, reward distribution fairness, and protection against persistent plutocracy. Existing DAG-BFT approaches (Narwhal+Tusk, Bullshark, and Mysticeti) prioritize liveness over the fairness implications of stake-based selection, resulting in persistent longitudinal centralization.FairWave is a dual-channel DAG BFT...

    arxiv.org/abs/2606.10982 · PDF

  5. 05

    Dynamic Software Updates using CRDTs

    Seppe Wyns, Jim Bauwens, Elisa Gonzalez Boix

    cs.DC · cs.PL

    This paper investigates how Conflict-free Replicated Data Types (CRDTs) can be used for dynamic software updates of distributed applications. We propose to model application updates as a new App CRDT that stores the application code associated with a semantic version, which defines a total order of the code updates. The App CRDT works with an API-compatible message delivery middleware, which allows applications to continue working with...

    arxiv.org/abs/2606.10920 · PDF

  6. 06

    Generalizing LCL Complexity Gaps to Unbounded Degree via Monadic Second-Order Properties

    Chiara Piombi

    cs.DC · cs.CC · cs.FL

    The last decade of research on the LOCAL model has seen tremendous progress in understanding locally checkable labeling (LCL) problems, culminating in an almost complete classification of the possible complexities LCL problems can exhibit. In particular, on undirected trees, Chang and Pettie showed that there is no LCL problem with complexity between $ω(\log n)$ and $n^{o(1)}$ and Chang showed that, for every positive integer $k$, there is no...

    arxiv.org/abs/2606.10693 · PDF

  7. 07

    Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design

    Wenxin Wang, Yule Hou, Yu Ji, Peng Qu, Youhui Zhang

    cs.DC · cs.AI · cs.LG · cs.NE

    Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-concurrency workloads. We identify four key gaps in local MoE inference: reliance on capacity-reduced models (quantized, distilled, rerouted), inability to meet 30-second TTFT for long prefills (more than 12K), sub-baseline decode throughput (under 20 tokens/s), and poor concurrency under mixed...

    arxiv.org/abs/2606.10493 · PDF

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