29 lines
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29 lines
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<!doctype html><html lang=en dir=auto data-theme=auto><head><meta charset=utf-8><meta http-equiv=X-UA-Compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta name=robots content="index, follow"><title>HPDC 2025 Digest | Publish Assistant</title><meta name=keywords content="hpc,distributed-systems,networking,storage,scheduling,cloud-hpc,performance"><meta name=description content="10 papers selected.
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Parameterized Algorithms for Non-uniform All-to-all
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Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar
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TL;DR — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.
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Why notable — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.
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→ Read paper
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DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge
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Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001"><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/hpdc-2025/><link crossorigin=anonymous href=/vincent/publish-assistant/assets/css/stylesheet.d72f07832e13c592b3edba91680bfe70f01daac396179bcace0ac36e8e0494c6.css integrity="sha256-1y8Hgy4TxZKz7bqRaAv+cPAdqsOWF5vKzgrDbo4ElMY=" rel="preload stylesheet" as=style><link rel=icon href=https://pub.sqrt.fr/vincent/publish-assistant/favicon.ico><link rel=icon type=image/png sizes=16x16 href=https://pub.sqrt.fr/vincent/publish-assistant/favicon-16x16.png><link rel=icon type=image/png sizes=32x32 href=https://pub.sqrt.fr/vincent/publish-assistant/favicon-32x32.png><link rel=apple-touch-icon href=https://pub.sqrt.fr/vincent/publish-assistant/apple-touch-icon.png><link rel=mask-icon href=https://pub.sqrt.fr/vincent/publish-assistant/safari-pinned-tab.svg><meta name=theme-color content="#2e2e33"><meta name=msapplication-TileColor content="#2e2e33"><link rel=alternate hreflang=en href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/hpdc-2025/><noscript><style>#theme-toggle,.top-link{display:none}</style><style>@media(prefers-color-scheme:dark){:root{--theme:rgb(29, 30, 32);--entry:rgb(46, 46, 51);--primary:rgb(218, 218, 219);--secondary:rgb(155, 156, 157);--tertiary:rgb(65, 66, 68);--content:rgb(196, 196, 197);--code-block-bg:rgb(46, 46, 51);--code-bg:rgb(55, 56, 62);--border:rgb(51, 51, 51);color-scheme:dark}.list{background:var(--theme)}.toc{background:var(--entry)}}</style></noscript><script>localStorage.getItem("pref-theme")==="dark"?document.querySelector("html").dataset.theme="dark":localStorage.getItem("pref-theme")==="light"?document.querySelector("html").dataset.theme="light":window.matchMedia("(prefers-color-scheme: dark)").matches?document.querySelector("html").dataset.theme="dark":document.querySelector("html").dataset.theme="light"</script><meta property="og:url" content="https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/hpdc-2025/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="HPDC 2025 Digest"><meta property="og:description" content="10 papers selected.
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Parameterized Algorithms for Non-uniform All-to-all Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar
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TL;DR — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.
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Why notable — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.
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→ Read paper DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001"><meta property="og:locale" content="en_us"><meta property="og:type" content="article"><meta property="article:section" content="cloud-edge"><meta property="article:published_time" content="2025-07-20T00:00:00+00:00"><meta property="article:modified_time" content="2025-07-20T00:00:00+00:00"><meta property="article:tag" content="Hpc"><meta property="article:tag" content="Distributed-Systems"><meta property="article:tag" content="Networking"><meta property="article:tag" content="Storage"><meta property="article:tag" content="Scheduling"><meta property="article:tag" content="Cloud-Hpc"><meta name=twitter:card content="summary"><meta name=twitter:title content="HPDC 2025 Digest"><meta name=twitter:description content="10 papers selected.
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Parameterized Algorithms for Non-uniform All-to-all Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar
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TL;DR — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.
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Why notable — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.
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→ Read paper DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001"><script type=application/ld+json>{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Edge and Cloud Systems","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/"},{"@type":"ListItem","position":2,"name":"Digests","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/"},{"@type":"ListItem","position":3,"name":"HPDC 2025 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/hpdc-2025/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"HPDC 2025 Digest","name":"HPDC 2025 Digest","description":"10 papers selected.\nParameterized Algorithms for Non-uniform All-to-all Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar\nTL;DR — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.\nWhy notable — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.\n→ Read paper DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001\n","keywords":["hpc","distributed-systems","networking","storage","scheduling","cloud-hpc","performance"],"articleBody":"10 papers selected.\nParameterized Algorithms for Non-uniform All-to-all Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar\nTL;DR — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.\nWhy notable — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.\n→ Read paper DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001\nTL;DR — Offloads key-value store operations to Data Processing Units (DPUs) over RDMA to reduce CPU overhead and tail latency in edge deployments.\nWhy notable — DPU offloading is an emerging paradigm for network-attached smart NICs in HPC clusters; this work provides concrete performance analysis showing when and how much offloading helps, informing future RDMA-based storage designs.\n→ Read paper TSUE: A Two-Stage Data Update Method for an Erasure Coded Cluster File System Zheng Wei, Jing Xing, Yida Gu, Wenjing Huang 0002 et al.\nTL;DR — Proposes a two-stage update scheme for erasure-coded parallel file systems that decouples the logging and parity-update phases to cut write amplification and I/O latency.\nWhy notable — Erasure coding is increasingly used in large-scale HPC storage to replace replication, but update overhead remains a bottleneck; TSUE addresses a core pain point for Lustre- and GPFS-class parallel file systems.\n→ Read paper LegoIndex: A Scalable and Modular Indexing Framework for Efficient Analysis of Extreme-Scale Particle Data Chang Guo, Ning Yan 0002, Lipeng Wan 0001, Zhichao Cao 0002\nTL;DR — Presents a composable, multi-level indexing framework for particle simulation datasets that enables efficient query processing at extreme scale without requiring full dataset scans.\nWhy notable — Scientific particle simulations at exascale generate data volumes that overwhelm traditional post-processing pipelines; LegoIndex’s modular design allows it to be adapted across different storage backends and query patterns commonly seen in DOE workloads.\n→ Read paper IPComp: Interpolation Based Progressive Lossy Compression for Scientific Applications Zhuoxun Yang, Sheng Di, Longtao Zhang, Ruoyu Li et al.\nTL;DR — Introduces interpolation-driven progressive lossy compression that lets users trade accuracy for compression ratio at query time rather than at write time, without re-compressing stored data.\nWhy notable — Progressive reconstruction is a long-sought capability for HPC I/O; IPComp achieves it with competitive compression ratios and builds on the widely used SZ/ZFP lineage, making adoption in existing scientific workflows straightforward.\n→ Read paper Advancing Scientific Data Compression via Cross-Field Prediction Youyuan Liu, Wenqi Jia 0003, Taolue Yang, Bo Jiang et al.\nTL;DR — Exploits correlations between different physical fields in multi-field scientific datasets to improve lossy compression ratios beyond what single-field methods can achieve.\nWhy notable — Multi-field simulations (climate, combustion, fusion) dominate HPC storage consumption; cross-field prediction represents a principled, generally applicable step change in compression efficiency for these workloads.\n→ Read paper Flux Emulator: First Insights into Optimizing Scheduling for Exascale HPC W. Jay Ashworth, Ian Lumsden, Jim Garlick, Mark Grondona et al.\nTL;DR — Presents an emulation infrastructure for the Flux workload manager that enables scheduling algorithm evaluation at exascale node counts without requiring access to a full exascale machine.\nWhy notable — Validating schedulers at exascale is otherwise infeasible before systems exist; Flux Emulator directly supports the scheduling research needed to maximize utilization of Frontier- and Aurora-class systems.\n→ Read paper HYPERF: End-to-End Autotuning Framework for High-Performance Computing Juseong Park, Yongwon Shin, Junghyun Lee, Junseo Lee et al.\nTL;DR — Delivers an end-to-end autotuning framework that jointly optimizes compiler flags, runtime parameters, and problem-specific configurations for HPC applications through structured search.\nWhy notable — Manual tuning of HPC codes for new architectures is expensive and error-prone; HYPERF’s end-to-end scope distinguishes it from prior tools that target only one layer of the software stack, offering broader applicability across the HPC software ecosystem.\n→ Read paper Efficient and Cost-Effective HPC on the Cloud Aditya Bhosale, Laxmikant V. Kalé, Sara Kokkila Schumacher\nTL;DR — Demonstrates how Charm++-based adaptive runtime techniques—load balancing, dynamic over-decomposition, and message-driven execution—can recover near-on-premises HPC performance on cloud instances despite higher network variability.\nWhy notable — Cloud-HPC convergence is a major community priority as on-premises clusters face procurement delays; this paper provides a practitioner-oriented analysis of which runtime adaptations deliver the best performance-per-dollar on AWS and Azure.\n→ Read paper Bringing Differential Privacy to HPC: Privacy-Preserving Transformations of HPC Traces Ana Luisa Veroneze Solórzano, Rohan Basu Roy, Benjamin Schwaller, Sara Petra Walton et al.\nTL;DR — Applies differential privacy mechanisms to HPC job and performance traces, enabling centers to share workload data for research without exposing sensitive user or application information.\nWhy notable — Sharing HPC traces is critical for reproducible scheduling and performance research but is often blocked by privacy concerns; this work provides a rigorous, deployable solution that could unlock a significant new supply of public HPC datasets.\n→ Read paper ","wordCount":"812","inLanguage":"en","datePublished":"2025-07-20T00:00:00Z","dateModified":"2025-07-20T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/hpdc-2025/"},"publisher":{"@type":"Organization","name":"Publish Assistant","logo":{"@type":"ImageObject","url":"https://pub.sqrt.fr/vincent/publish-assistant/favicon.ico"}}}</script></head><body id=top><header class=header><nav class=header-nav><div class=logo><a href=https://pub.sqrt.fr/vincent/publish-assistant/ accesskey=h title="Publish Assistant (Alt + H)">Publish Assistant</a>
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<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-chevron-right"><polyline points="9 18 15 12 9 6"/></svg></nav><h1 class="post-title entry-hint-parent">HPDC 2025 Digest</h1><div class=post-meta><span title='2025-07-20 00:00:00 +0000 UTC'>July 20, 2025</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>10 papers selected.</p><hr><h3 id=parameterized-algorithms-for-non-uniform-all-to-all>Parameterized Algorithms for Non-uniform All-to-all<a hidden class=anchor aria-hidden=true href=#parameterized-algorithms-for-non-uniform-all-to-all>#</a></h3><p><em>Ke Fan, Jens Domke, Seydou Ba, Sidharth Kumar</em></p><p><strong>TL;DR</strong> — Introduces parameterized algorithms that adapt non-uniform all-to-all collective communication to heterogeneous network topologies, reducing message contention and improving throughput.</p><p><strong>Why notable</strong> — Non-uniform all-to-all is a performance bottleneck in many HPC applications; topology-aware parameterization directly benefits MPI implementations on dragonfly and fat-tree networks at scale.</p><p><a href=https://doi.org/10.1145/3731545.3731590>→ Read paper</a></p><hr><h3 id=dpu-kv-on-the-benefits-of-dpu-offloading-for-in-memory-key-value-stores-at-the-edge>DPU-KV: On the Benefits of DPU Offloading for In-Memory Key-Value Stores at the Edge<a hidden class=anchor aria-hidden=true href=#dpu-kv-on-the-benefits-of-dpu-offloading-for-in-memory-key-value-stores-at-the-edge>#</a></h3><p><em>Arjun Kashyap, Yuke Li 0003, Xiaoyi Lu 0001</em></p><p><strong>TL;DR</strong> — Offloads key-value store operations to Data Processing Units (DPUs) over RDMA to reduce CPU overhead and tail latency in edge deployments.</p><p><strong>Why notable</strong> — DPU offloading is an emerging paradigm for network-attached smart NICs in HPC clusters; this work provides concrete performance analysis showing when and how much offloading helps, informing future RDMA-based storage designs.</p><p><a href=https://doi.org/10.1145/3731545.3731571>→ Read paper</a></p><hr><h3 id=tsue-a-two-stage-data-update-method-for-an-erasure-coded-cluster-file-system>TSUE: A Two-Stage Data Update Method for an Erasure Coded Cluster File System<a hidden class=anchor aria-hidden=true href=#tsue-a-two-stage-data-update-method-for-an-erasure-coded-cluster-file-system>#</a></h3><p><em>Zheng Wei, Jing Xing, Yida Gu, Wenjing Huang 0002 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Proposes a two-stage update scheme for erasure-coded parallel file systems that decouples the logging and parity-update phases to cut write amplification and I/O latency.</p><p><strong>Why notable</strong> — Erasure coding is increasingly used in large-scale HPC storage to replace replication, but update overhead remains a bottleneck; TSUE addresses a core pain point for Lustre- and GPFS-class parallel file systems.</p><p><a href=https://doi.org/10.1145/3731545.3731577>→ Read paper</a></p><hr><h3 id=legoindex-a-scalable-and-modular-indexing-framework-for-efficient-analysis-of-extreme-scale-particle-data>LegoIndex: A Scalable and Modular Indexing Framework for Efficient Analysis of Extreme-Scale Particle Data<a hidden class=anchor aria-hidden=true href=#legoindex-a-scalable-and-modular-indexing-framework-for-efficient-analysis-of-extreme-scale-particle-data>#</a></h3><p><em>Chang Guo, Ning Yan 0002, Lipeng Wan 0001, Zhichao Cao 0002</em></p><p><strong>TL;DR</strong> — Presents a composable, multi-level indexing framework for particle simulation datasets that enables efficient query processing at extreme scale without requiring full dataset scans.</p><p><strong>Why notable</strong> — Scientific particle simulations at exascale generate data volumes that overwhelm traditional post-processing pipelines; LegoIndex’s modular design allows it to be adapted across different storage backends and query patterns commonly seen in DOE workloads.</p><p><a href=https://doi.org/10.1145/3731545.3731591>→ Read paper</a></p><hr><h3 id=ipcomp-interpolation-based-progressive-lossy-compression-for-scientific-applications>IPComp: Interpolation Based Progressive Lossy Compression for Scientific Applications<a hidden class=anchor aria-hidden=true href=#ipcomp-interpolation-based-progressive-lossy-compression-for-scientific-applications>#</a></h3><p><em>Zhuoxun Yang, Sheng Di, Longtao Zhang, Ruoyu Li <em>et al.</em></em></p><p><strong>TL;DR</strong> — Introduces interpolation-driven progressive lossy compression that lets users trade accuracy for compression ratio at query time rather than at write time, without re-compressing stored data.</p><p><strong>Why notable</strong> — Progressive reconstruction is a long-sought capability for HPC I/O; IPComp achieves it with competitive compression ratios and builds on the widely used SZ/ZFP lineage, making adoption in existing scientific workflows straightforward.</p><p><a href=https://doi.org/10.1145/3731545.3731578>→ Read paper</a></p><hr><h3 id=advancing-scientific-data-compression-via-cross-field-prediction>Advancing Scientific Data Compression via Cross-Field Prediction<a hidden class=anchor aria-hidden=true href=#advancing-scientific-data-compression-via-cross-field-prediction>#</a></h3><p><em>Youyuan Liu, Wenqi Jia 0003, Taolue Yang, Bo Jiang <em>et al.</em></em></p><p><strong>TL;DR</strong> — Exploits correlations between different physical fields in multi-field scientific datasets to improve lossy compression ratios beyond what single-field methods can achieve.</p><p><strong>Why notable</strong> — Multi-field simulations (climate, combustion, fusion) dominate HPC storage consumption; cross-field prediction represents a principled, generally applicable step change in compression efficiency for these workloads.</p><p><a href=https://doi.org/10.1145/3731545.3731592>→ Read paper</a></p><hr><h3 id=flux-emulator-first-insights-into-optimizing-scheduling-for-exascale-hpc>Flux Emulator: First Insights into Optimizing Scheduling for Exascale HPC<a hidden class=anchor aria-hidden=true href=#flux-emulator-first-insights-into-optimizing-scheduling-for-exascale-hpc>#</a></h3><p><em>W. Jay Ashworth, Ian Lumsden, Jim Garlick, Mark Grondona <em>et al.</em></em></p><p><strong>TL;DR</strong> — Presents an emulation infrastructure for the Flux workload manager that enables scheduling algorithm evaluation at exascale node counts without requiring access to a full exascale machine.</p><p><strong>Why notable</strong> — Validating schedulers at exascale is otherwise infeasible before systems exist; Flux Emulator directly supports the scheduling research needed to maximize utilization of Frontier- and Aurora-class systems.</p><p><a href=https://doi.org/10.1145/3731545.3735121>→ Read paper</a></p><hr><h3 id=hyperf-end-to-end-autotuning-framework-for-high-performance-computing>HYPERF: End-to-End Autotuning Framework for High-Performance Computing<a hidden class=anchor aria-hidden=true href=#hyperf-end-to-end-autotuning-framework-for-high-performance-computing>#</a></h3><p><em>Juseong Park, Yongwon Shin, Junghyun Lee, Junseo Lee <em>et al.</em></em></p><p><strong>TL;DR</strong> — Delivers an end-to-end autotuning framework that jointly optimizes compiler flags, runtime parameters, and problem-specific configurations for HPC applications through structured search.</p><p><strong>Why notable</strong> — Manual tuning of HPC codes for new architectures is expensive and error-prone; HYPERF’s end-to-end scope distinguishes it from prior tools that target only one layer of the software stack, offering broader applicability across the HPC software ecosystem.</p><p><a href=https://doi.org/10.1145/3731545.3731588>→ Read paper</a></p><hr><h3 id=efficient-and-cost-effective-hpc-on-the-cloud>Efficient and Cost-Effective HPC on the Cloud<a hidden class=anchor aria-hidden=true href=#efficient-and-cost-effective-hpc-on-the-cloud>#</a></h3><p><em>Aditya Bhosale, Laxmikant V. Kalé, Sara Kokkila Schumacher</em></p><p><strong>TL;DR</strong> — Demonstrates how Charm++-based adaptive runtime techniques—load balancing, dynamic over-decomposition, and message-driven execution—can recover near-on-premises HPC performance on cloud instances despite higher network variability.</p><p><strong>Why notable</strong> — Cloud-HPC convergence is a major community priority as on-premises clusters face procurement delays; this paper provides a practitioner-oriented analysis of which runtime adaptations deliver the best performance-per-dollar on AWS and Azure.</p><p><a href=https://doi.org/10.1145/3731545.3744667>→ Read paper</a></p><hr><h3 id=bringing-differential-privacy-to-hpc-privacy-preserving-transformations-of-hpc-traces>Bringing Differential Privacy to HPC: Privacy-Preserving Transformations of HPC Traces<a hidden class=anchor aria-hidden=true href=#bringing-differential-privacy-to-hpc-privacy-preserving-transformations-of-hpc-traces>#</a></h3><p><em>Ana Luisa Veroneze Solórzano, Rohan Basu Roy, Benjamin Schwaller, Sara Petra Walton <em>et al.</em></em></p><p><strong>TL;DR</strong> — Applies differential privacy mechanisms to HPC job and performance traces, enabling centers to share workload data for research without exposing sensitive user or application information.</p><p><strong>Why notable</strong> — Sharing HPC traces is critical for reproducible scheduling and performance research but is often blocked by privacy concerns; this work provides a rigorous, deployable solution that could unlock a significant new supply of public HPC datasets.</p><p><a href=https://doi.org/10.1145/3731545.3731573>→ Read paper</a></p></div><footer class=post-footer><ul class=post-tags><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/hpc/>Hpc</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/distributed-systems/>Distributed-Systems</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/networking/>Networking</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/storage/>Storage</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/scheduling/>Scheduling</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/cloud-hpc/>Cloud-Hpc</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/performance/>Performance</a></li></ul><nav class=paginav><a class=next href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/atc-2025/><span class=title>Next »</span>
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