cs.DC · 2026-08-30 · No. 100

Distributed, Parallel, and Cluster Computing, 2026-08-30.

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

    Consensus with Stochastic Broadcast

    Pierre Fraigniaud, Boaz Patt-Shamir, Sergio Rajsbaum

    cs.DC

    We study binary consensus in the \emph{stochastic broadcast model}, which assumes $n\geq 2$ processes communicating synchronously by message broadcasts. At each round, every process broadcasts a message to all the other processes. Each broadcast succeeds independently with some probability $p\in[0,1]$. If a broadcast succeeds, all processes receive the message, and if it fails, no process receives the message. The sender does not know whether...

    arxiv.org/abs/2608.27336 · PDF

  2. 02

    Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction

    Mayanka Chandrashekar, Xi Zhang, Ethan Seefried, Tirthankar Ghosal, John Gounley, Heidi Hanson

    cs.DC · cs.CV

    Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At scale, however, generating and handling massive numbers of patches on quickly introduces significant I/O and orchestration overhead, often dominating end-to-end performance. We present a decoupled, I/O-aware pipeline for large-scale WSI embedding extraction that...

    arxiv.org/abs/2608.27278 · PDF

  3. 03

    Sintr: Safe Interactive Transactions in the Presence of Byzantine Clients

    Austin T. Li, Daniel H. Lee, Lorenzo Alvisi, Natacha Crooks, Florian Suri-Payer

    cs.DC

    Byzantine fault-tolerant (BFT) systems are, in principle, an appealing foundation for transactional applications involving mutually distrustful participants. Yet their adoption has been hampered by two persistent stumbling blocks-performance and developer convenience-which are often in tension with one another. Recent systems show promising progress on both fronts by shifting to a client-centric architecture; clients execute transactions...

    arxiv.org/abs/2608.27091 · PDF

  4. 04

    Benchmarking Confidential Computing Performance on NVIDIA Blackwell GPUs

    Daniyal Khan, Amean Asad, Ansgar Grunseid

    cs.DC · cs.CR

    This paper measures the performance impact of running large language model inference and training inside a Trusted Execution Environment (TEE) on NVIDIA B200 GPUs, using Intel Trust Domain Extensions (TDX) confidential VMs together with NVIDIA Confidential Computing (CC) on Blackwell GPUs. The performance impact is derived from paired confidential versus non-confidential runs on a single physical host where the only variable is the GPU CC bit...

    arxiv.org/abs/2608.26575 · PDF

  5. 05

    Optimizing API Gateway Placement in Multi-Cloud Kubernetes

    Vinoth Punniyamoorthy, Murali Shankar Dulam, Aswathnarayan Muthukrishnan Kirubakaran, Akshay Deshpande, Nachiappan...

    cs.DC

    The use of API gateways within geographically distributed multi-cloud Kubernetes clusters poses a tradeoff between infrastructure cost, computational resources, and network latencies. We present an optimization formulation that addresses API gateway placement as a capacitated facility location problem that jointly determines which candidate clusters to activate, how many gateway replicas to deploy, and how regional traffic should be...

    arxiv.org/abs/2608.26573 · PDF

  6. 06

    VPP: Virtual Pipeline Parallelism for Efficient Chunked Prefill in Long-Context LLM Inference

    Yan Shi, Xiaochao Wang, Jingchun Gao, Jintao Luo, Xinyi Zhou, Feng Liu, Kui Luo, Xushi Li, Xinjie Guo, Liangjun Feng

    cs.DC

    Chunked prefill pipeline parallelism (CPP) is a key technique for LLM inference. However, equal-size chunks exhibit imbalanced latency, as later chunks attend longer prefix KV caches and incur higher attention costs, leading to pipeline bubbles. Existing approaches mitigate this imbalance through dynamic chunk resizing (Dynamic CPP, DCPP), but our measurements show that this trades scheduling overhead for load balancing, which becomes...

    arxiv.org/abs/2608.26523 · PDF

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