cs.DC · 2026-07-21 · No. 60

Distributed, Parallel, and Cluster Computing, 2026-07-21.

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

01 — The papers

6 entries
  1. 01

    uSTM: A Lightweight and Efficient STM Supporting General Types and Deferred Aborts

    Zachary Kent, Guy Blelloch, André Costa

    cs.DC

    Software Transactional Memory (STM) systems allow developers to more easily exploit multicore architectures by wrapping arbitrary sequential code in transactions that are executed concurrently. In recent years, the performance of STM systems has approached that of hand-tuned data structures through techniques that avoid unnecessary aborts and exploit the semantics of underlying data structures. Despite achieving excellent performance, most...

    arxiv.org/abs/2607.18178 · PDF

  2. 02

    HyMCache: A KV Cache Framework for Multi-Turn LLM Serving with CXL-Hybrid Memory

    Hakbeom Jang, Inho Song, Sam H. Noh, Jongryool Kim

    cs.DC

    Long-context, multi-turn, and agentic LLM workloads increasingly reuse previously processed context, making KV-cache reuse essential for reducing redundant computation. However, this reuse shifts the bottleneck to the memory tier that stores and serves reusable KV states at cluster scale. GPU HBM and host DRAM are too costly to scale to TB-scale shared context capacity, motivating remote tiers built from lower-cost, higher-capacity media....

    arxiv.org/abs/2607.18141 · PDF

  3. 03

    ExpertPlex: A High-Goodput Disaggregated Serving System for MoE LLMs with Adaptive Persistent Kernels

    Bingyang Wu, Chao Jin, Zili Zhang, Xinming Wei, Yinmin Zhong, Ruidong Zhu, Chengxu Yang, Xin Jin, Yuliang Liu

    cs.DC

    LLMs scale Mixture-of-Experts (MoE) parameters for superior intelligence, but massive weights and dynamic computation impede efficient serving. Existing instance-level prefill-decode disaggregation isolates the phases on separate full-model replicas. As MoE weights grow, each instance may span tens to hundreds of GPUs, making resource allocation increasingly coarse. Configured prefill-to-decode ratios thus often mismatch demand,...

    arxiv.org/abs/2607.18002 · PDF

  4. 04

    AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning

    Bas Meuwissen, Vasileios Tsouvalas, Nirvana Meratnia

    cs.DC · cs.LG

    Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead. Parallel Split Learning (PSL) reduces client-side computation by keeping few model layers on each client and offloading the remaining computation to the server; however, clients must exchange intermediate activations and gradients with the server at every training step....

    arxiv.org/abs/2607.17913 · PDF

  5. 05

    Byzantine Fault-Tolerant Post-Quantum Distributed Quorum Signatures

    Quentin Kniep, Jakub Sliwinski, Roger Wattenhofer

    cs.DC

    Threshold, aggregate, and multi-signatures -- which we collectively call quorum signatures -- certify that a quorum of nodes endorsed a statement, with a certificate as small as a single signature. No constant-size post-quantum quorum signature is known: all candidates grow with the number of signers and are slow to aggregate, making quorum signatures the hardest obstacle to migrating byzantine fault-tolerant systems to post-quantum security....

    arxiv.org/abs/2607.17700 · PDF

  6. 06

    A Training-Memory Regression in MLA Sequence Parallelism: Why Megatron-Core Forbids Absorption, and LAGA -- a Communication-Efficient Fix

    Changzheng Ma

    cs.DC

    Multi-head Latent Attention (MLA) ships two implementations in Megatron-Core: an explicit form used for training and an absorbed form -- which slashes collective communication by gathering only the compressed latent -- that is fully implemented but hard-asserted out of training (the forward opens with "assert not (self.training and self.cache_mla_latents)"), allowed only in inference decode. The library documents no reason. We show the...

    arxiv.org/abs/2607.17644 · PDF

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