cs.DC · 2026-06-07 · No. 16

Distributed, Parallel, and Cluster Computing, 2026-06-07.

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

    CarbonSim: A Lifecycle-Aware Framework for Evaluating Carbon Tradeoffs in Hardware Upgrade Decisions

    Kartik Hans, Kaiwen Zhao, Stephen Lee

    cs.DC · cs.OS

    As the demand for information and communication technologies (ICT) continues to rise, the environmental impact of computing systems is becoming an increasingly critical concern. Although newer hardware often improves performance and energy efficiency, these gains do not always offset the carbon cost of premature replacement, particularly under low-utilization workloads or low-carbon electricity grids. We present CarbonSim, a lifecycle-aware...

    arxiv.org/abs/2606.06438 · PDF

  2. 02

    On GPU Implementation for Multi-Precision Integer Division

    Martin B. Marchioro, Aske N. Raahauge, Marc I. Løvenskjold, Cosmin E. Oancea, Stephen M. Watt

    cs.DC · cs.MS · cs.SC

    This paper presents the issues arising in implementing a fast integer division algorithm on general purpose GPUs. The algorithm uses a Newton iteration based on the shifted inverse operation, keeping all arithmetic in the integer domain and relying on data-parallel operators. The principal contribution is an efficient GPU/CUDA implementation for integer precisions from $2^{15}$ to $2^{18}$ -- sizes not supported by \cgbn{} division. We...

    arxiv.org/abs/2606.06386 · PDF

  3. 03

    Discrete Incremental Voting: New Bounds for General Graphs and Expanders

    Petra Berenbrink, Colin Cooper, Thorsten Götte, Lukas Hintze, Tomasz Radzik

    cs.DC

    We analyze the discrete incremental voting process (DIV) introduced by Cooper, Radzik, and Shiraga [OPODIS '23]. In this process, we consider a set $V$ of $n$ nodes connected in an undirected graph $G = (V, E)$ where each node has an integer opinion. In one step a randomly selected node interacts with its randomly selected neighbor and changes its opinion by $1$ in the direction of the neighbour's opinion. The process converges to a unique...

    arxiv.org/abs/2606.06381 · PDF

  4. 04

    LLM-Based Porting of Optimized C++ to CUDA Through Deoptimization and Reoptimization

    Daichi Mukunoki, Ryo Mikasa, Shunichiro Hayashi, Tetsuya Hoshino, Takahiro Katagiri

    cs.DC

    When porting high-performance computing (HPC) code from CPU to GPU, CPU-oriented optimizations may obstruct LLM-based CUDA translation. We design and evaluate a Deopt-Reopt workflow that first simplifies the input C++ code and then retranslates and reoptimizes it for CUDA, comparing it against direct translation (Direct) on twelve HPC kernels with two LLMs (gpt-oss-120b (O120) and qwen-3-235b-a22b-instruct-2507 (Q235)) in Single-shot (one...

    arxiv.org/abs/2606.06063 · PDF

  5. 05

    Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication

    Yijun Ma, Siyuan Shen, Tiancheng Chen, Akhil Langer, Jiri Kraus, Benjamin Glick, Craig Belusar, Jeff Hammond, Torsten Hoefler

    cs.DC

    NVSHMEM is NVIDIA's OpenSHMEM-based PGAS communication library for GPU clusters, enabling GPU-initiated, one-sided communication through symmetric memory. Despite its growing adoption, a system-level understanding of its design and behavior remains scattered across documentation, source code, and application experience. This paper presents a concise study of NVSHMEM's programming model, implementation, and performance characteristics,...

    arxiv.org/abs/2606.05951 · PDF

  6. 06

    Beyond Greedy Chunking: SLO-Aware Sliding-Window Scheduling for LLM Inference

    Yuansheng Chen, Yue Zhang, Xuan Mo, Weigang Wu, Jialun Li

    cs.DC

    With the rapid growth of interactive applications in large language model (LLM) online services, maintaining high system throughput while ensuring user-perceived latency has become a key issue in inference scheduling. Existing LLM service systems rely on coarse-grained output constraints, making it difficult to effectively handle resource contention among multiple requests, resulting in low resource utilization efficiency and limited support...

    arxiv.org/abs/2606.05933 · PDF

  7. 07

    IN2P3 Computing Center 2024 Workload Dataset

    Guillaume Cochard, Bertrand Simon

    cs.DC

    This paper provides and analyzes a dataset detailing the characteristics and execution data of all jobs submitted to the IN2P3 Computing Center (Villeurbanne, France), a national research and support unit of the CNRS, in 2024. The main additional value of this contribution compared to previously available datasets consists in the combination of an extended time interval considered, the inclusion of memory usage data and its recency, on top on...

    arxiv.org/abs/2606.05914 · PDF

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