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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>FGCS 2025 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="12 papers selected.
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Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing
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Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt et al.
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TL;DR — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.
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Why notable — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/fgcs-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/fgcs-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/fgcs-2025/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="FGCS 2025 Digest"><meta property="og:description" content="12 papers selected.
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Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt et al.
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TL;DR — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.
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Why notable — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements."><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="2025-01-01T00:00:00+00:00"><meta property="article:modified_time" content="2025-01-01T00:00:00+00:00"><meta name=twitter:card content="summary"><meta name=twitter:title content="FGCS 2025 Digest"><meta name=twitter:description content="12 papers selected.
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Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt et al.
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TL;DR — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.
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Why notable — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements."><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":"FGCS 2025 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/fgcs-2025/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"FGCS 2025 Digest","name":"FGCS 2025 Digest","description":"12 papers selected.\nMultifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt et al.\nTL;DR — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.\nWhy notable — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements.\n","keywords":[],"articleBody":"12 papers selected.\nMultifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt et al.\nTL;DR — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.\nWhy notable — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements.\nEfficient distributed continual learning for steering experiments in real-time Thomas Bouvier, Bogdan Nicolae, Alexandru Costan, Tekin Bicer et al.\nTL;DR — Proposes a distributed continual learning architecture that keeps deep learning models synchronized with a running scientific experiment by streaming lightweight updates across edge detectors and HPC backends.\nWhy notable — One of the first systems to close the loop between experimental data streams and model adaptation in real-time without full retraining, validated on synchrotron detector workloads.\nSmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum Juan Aznar-Poveda, Maximilian Franz Ebner, Thomas Fahringer, Zahra Najafabadi Samani et al.\nTL;DR — Introduces SmartKV, a geo-distributed key-value store that uses latency-aware replication policies to deliver consistent low-latency reads across edge, fog, and cloud tiers of the computing continuum.\nWhy notable — Provides a concrete, benchmarked storage primitive for the computing continuum that fills the gap between single-datacenter stores and high-latency cloud object storage.\nScalable compute continuum Valeria Cardellini, Patrizio Dazzi, Gabriele Mencagli, Matteo Nardelli 0001 et al.\nTL;DR — Defines a programming and deployment model for the compute continuum that abstracts resource heterogeneity from edge to cloud, enabling applications to scale dynamically across tiers.\nWhy notable — Provides a principled architectural reference for the continuum that can guide system designers building next-generation distributed runtime environments.\nA comparative study of ad-hoc file systems for extreme scale computing Njoud O. Almaaitah, Francisco Javier García Blas, Genaro Sanchez-Gallegos, Jesús Carretero 0001 et al.\nTL;DR — Benchmarks GekkoFS, BeeGFS, and similar ad-hoc file systems under diverse HPC I/O patterns, characterizing their throughput, metadata performance, and suitability for burst-buffer scenarios.\nWhy notable — The most systematic evaluation of ad-hoc parallel file systems available, giving HPC centers clear data to choose or configure temporary storage for large scientific workflows.\nAdvancing anomaly detection in computational workflows with active learning Krishnan Raghavan, George Papadimitriou 0002, Hongwei Jin, Anirban Mandal et al.\nTL;DR — Applies active learning to reduce the labeling burden for workflow anomaly detection, selectively querying an oracle for the most informative execution traces within a Pegasus workflow framework.\nWhy notable — Demonstrates that active learning can make anomaly detection practical in real scientific workflows where labeled failure data is scarce, with experiments on production workloads.\nMITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade Shreyas Sunil Gaikwad, Sri Hari Krishna Narayanan, Laurent Hascoët, Jean-Michel Campin et al.\nTL;DR — Describes MITgcm-AD v2, a production-quality adjoint of the MITgcm ocean-atmosphere model generated with Tapenade, enabling global sensitivity analyses and data assimilation at scale.\nWhy notable — A landmark in scientific computing software sustainability: a fully open, differentiable climate model that enables gradient-based inversion for ocean state estimation.\nzCeph: Design and implementation of a ZNS-friendly distributed file system Jinyong Ha 0001, Yongseok Son\nTL;DR — Redesigns the Ceph distributed file system to exploit Zoned Namespace SSDs natively, eliminating write amplification and improving throughput by aligning file system semantics with ZNS zone constraints.\nWhy notable — Demonstrates how next-generation storage hardware (ZNS SSDs) demands rethinking distributed storage stack designs, with significant performance gains on real hardware.\nRADiCe: A Risk Analysis Framework for Data Centers Fabian Mastenbroek, Tiziano De Matteis, Vincent van Beek, Alexandru Iosup\nTL;DR — Provides a quantitative risk analysis framework for data centers that models cascading failures across power, cooling, and compute subsystems using simulation to estimate availability and cost trade-offs.\nWhy notable — Fills a practical gap for data center operators who need principled tools to evaluate infrastructure resilience beyond simple redundancy rules.\nDeadline-constrained security-aware workflow scheduling in hybrid cloud architecture Somayeh Abdi, Mohammad Ashjaei, Saad Mubeen\nTL;DR — Formulates workflow scheduling in hybrid clouds as a multi-objective problem that jointly minimizes cost and execution time while meeting both deadline and data-security placement constraints.\nWhy notable — One of the few scheduling works that treats security classification of tasks as a first-class constraint alongside performance, with practical validation on scientific workflow benchmarks.\nRegen: An object layout regenerator on large-scale production HPC systems Dong Kyu Sung, Sunggon Kim, Sangjin Lee 0003, Houjun Tang et al.\nTL;DR — Regen transparently reorganizes the on-disk layout of HDF5 and NetCDF objects in parallel file systems to match actual access patterns, improving I/O performance without application changes.\nWhy notable — Deployed and validated on a production HPC system, showing significant I/O speedups for real scientific datasets, making it immediately relevant to storage administrators.\nFormal definition and implementation of reproducibility tenets for computational workflows Nicholas J. Pritchard, Andreas Wicenec\nTL;DR — Formalizes a set of reproducibility requirements for scientific workflows and implements a verification layer within the DALIUGE workflow engine that checks compliance at design and execution time.\nWhy notable — Provides the community with a concrete, tool-supported definition of workflow reproducibility, moving beyond aspirational guidelines to enforceable runtime checks.\n","wordCount":"887","inLanguage":"en","datePublished":"2025-01-01T00:00:00Z","dateModified":"2025-01-01T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/fgcs-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">FGCS 2025 Digest</h1><div class=post-meta><span title='2025-01-01 00:00:00 +0000 UTC'>January 1, 2025</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>12 papers selected.</p><hr><h3 id=multifacets-of-lossy-compression-for-scientific-data-in-the-joint-laboratory-of-extreme-scale-computing>Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing<a hidden class=anchor aria-hidden=true href=#multifacets-of-lossy-compression-for-scientific-data-in-the-joint-laboratory-of-extreme-scale-computing>#</a></h3><p><em>Franck Cappello, Mario C. Acosta, Emmanuel Agullo, Hartwig Anzt <em>et al.</em></em></p><p><strong>TL;DR</strong> — A joint JLESC survey covering error-bounded lossy compressors (SZ, ZFP, MGARD) across simulation, AI, and in-situ analytics use cases, with benchmarks on real scientific datasets at extreme scale.</p><p><strong>Why notable</strong> — The most comprehensive cross-site evaluation of scientific data compression to date, providing actionable guidance on compressor selection for different numerical kernels and accuracy requirements.</p><hr><h3 id=efficient-distributed-continual-learning-for-steering-experiments-in-real-time>Efficient distributed continual learning for steering experiments in real-time<a hidden class=anchor aria-hidden=true href=#efficient-distributed-continual-learning-for-steering-experiments-in-real-time>#</a></h3><p><em>Thomas Bouvier, Bogdan Nicolae, Alexandru Costan, Tekin Bicer <em>et al.</em></em></p><p><strong>TL;DR</strong> — Proposes a distributed continual learning architecture that keeps deep learning models synchronized with a running scientific experiment by streaming lightweight updates across edge detectors and HPC backends.</p><p><strong>Why notable</strong> — One of the first systems to close the loop between experimental data streams and model adaptation in real-time without full retraining, validated on synchrotron detector workloads.</p><hr><h3 id=smartkv-a-cost-effective-and-low-latency-geo-distributed-key-value-store-for-the-computing-continuum>SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum<a hidden class=anchor aria-hidden=true href=#smartkv-a-cost-effective-and-low-latency-geo-distributed-key-value-store-for-the-computing-continuum>#</a></h3><p><em>Juan Aznar-Poveda, Maximilian Franz Ebner, Thomas Fahringer, Zahra Najafabadi Samani <em>et al.</em></em></p><p><strong>TL;DR</strong> — Introduces SmartKV, a geo-distributed key-value store that uses latency-aware replication policies to deliver consistent low-latency reads across edge, fog, and cloud tiers of the computing continuum.</p><p><strong>Why notable</strong> — Provides a concrete, benchmarked storage primitive for the computing continuum that fills the gap between single-datacenter stores and high-latency cloud object storage.</p><hr><h3 id=scalable-compute-continuum>Scalable compute continuum<a hidden class=anchor aria-hidden=true href=#scalable-compute-continuum>#</a></h3><p><em>Valeria Cardellini, Patrizio Dazzi, Gabriele Mencagli, Matteo Nardelli 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Defines a programming and deployment model for the compute continuum that abstracts resource heterogeneity from edge to cloud, enabling applications to scale dynamically across tiers.</p><p><strong>Why notable</strong> — Provides a principled architectural reference for the continuum that can guide system designers building next-generation distributed runtime environments.</p><hr><h3 id=a-comparative-study-of-ad-hoc-file-systems-for-extreme-scale-computing>A comparative study of ad-hoc file systems for extreme scale computing<a hidden class=anchor aria-hidden=true href=#a-comparative-study-of-ad-hoc-file-systems-for-extreme-scale-computing>#</a></h3><p><em>Njoud O. Almaaitah, Francisco Javier García Blas, Genaro Sanchez-Gallegos, Jesús Carretero 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Benchmarks GekkoFS, BeeGFS, and similar ad-hoc file systems under diverse HPC I/O patterns, characterizing their throughput, metadata performance, and suitability for burst-buffer scenarios.</p><p><strong>Why notable</strong> — The most systematic evaluation of ad-hoc parallel file systems available, giving HPC centers clear data to choose or configure temporary storage for large scientific workflows.</p><hr><h3 id=advancing-anomaly-detection-in-computational-workflows-with-active-learning>Advancing anomaly detection in computational workflows with active learning<a hidden class=anchor aria-hidden=true href=#advancing-anomaly-detection-in-computational-workflows-with-active-learning>#</a></h3><p><em>Krishnan Raghavan, George Papadimitriou 0002, Hongwei Jin, Anirban Mandal <em>et al.</em></em></p><p><strong>TL;DR</strong> — Applies active learning to reduce the labeling burden for workflow anomaly detection, selectively querying an oracle for the most informative execution traces within a Pegasus workflow framework.</p><p><strong>Why notable</strong> — Demonstrates that active learning can make anomaly detection practical in real scientific workflows where labeled failure data is scarce, with experiments on production workloads.</p><hr><h3 id=mitgcm-ad-v2-open-source-tangent-linear-and-adjoint-modeling-framework-for-the-oceans-and-atmosphere-enabled-by-the-automatic-differentiation-tool-tapenade>MITgcm-AD v2: Open source tangent linear and adjoint modeling framework for the oceans and atmosphere enabled by the Automatic Differentiation tool Tapenade<a hidden class=anchor aria-hidden=true href=#mitgcm-ad-v2-open-source-tangent-linear-and-adjoint-modeling-framework-for-the-oceans-and-atmosphere-enabled-by-the-automatic-differentiation-tool-tapenade>#</a></h3><p><em>Shreyas Sunil Gaikwad, Sri Hari Krishna Narayanan, Laurent Hascoët, Jean-Michel Campin <em>et al.</em></em></p><p><strong>TL;DR</strong> — Describes MITgcm-AD v2, a production-quality adjoint of the MITgcm ocean-atmosphere model generated with Tapenade, enabling global sensitivity analyses and data assimilation at scale.</p><p><strong>Why notable</strong> — A landmark in scientific computing software sustainability: a fully open, differentiable climate model that enables gradient-based inversion for ocean state estimation.</p><hr><h3 id=zceph-design-and-implementation-of-a-zns-friendly-distributed-file-system>zCeph: Design and implementation of a ZNS-friendly distributed file system<a hidden class=anchor aria-hidden=true href=#zceph-design-and-implementation-of-a-zns-friendly-distributed-file-system>#</a></h3><p><em>Jinyong Ha 0001, Yongseok Son</em></p><p><strong>TL;DR</strong> — Redesigns the Ceph distributed file system to exploit Zoned Namespace SSDs natively, eliminating write amplification and improving throughput by aligning file system semantics with ZNS zone constraints.</p><p><strong>Why notable</strong> — Demonstrates how next-generation storage hardware (ZNS SSDs) demands rethinking distributed storage stack designs, with significant performance gains on real hardware.</p><hr><h3 id=radice-a-risk-analysis-framework-for-data-centers>RADiCe: A Risk Analysis Framework for Data Centers<a hidden class=anchor aria-hidden=true href=#radice-a-risk-analysis-framework-for-data-centers>#</a></h3><p><em>Fabian Mastenbroek, Tiziano De Matteis, Vincent van Beek, Alexandru Iosup</em></p><p><strong>TL;DR</strong> — Provides a quantitative risk analysis framework for data centers that models cascading failures across power, cooling, and compute subsystems using simulation to estimate availability and cost trade-offs.</p><p><strong>Why notable</strong> — Fills a practical gap for data center operators who need principled tools to evaluate infrastructure resilience beyond simple redundancy rules.</p><hr><h3 id=deadline-constrained-security-aware-workflow-scheduling-in-hybrid-cloud-architecture>Deadline-constrained security-aware workflow scheduling in hybrid cloud architecture<a hidden class=anchor aria-hidden=true href=#deadline-constrained-security-aware-workflow-scheduling-in-hybrid-cloud-architecture>#</a></h3><p><em>Somayeh Abdi, Mohammad Ashjaei, Saad Mubeen</em></p><p><strong>TL;DR</strong> — Formulates workflow scheduling in hybrid clouds as a multi-objective problem that jointly minimizes cost and execution time while meeting both deadline and data-security placement constraints.</p><p><strong>Why notable</strong> — One of the few scheduling works that treats security classification of tasks as a first-class constraint alongside performance, with practical validation on scientific workflow benchmarks.</p><hr><h3 id=regen-an-object-layout-regenerator-on-large-scale-production-hpc-systems>Regen: An object layout regenerator on large-scale production HPC systems<a hidden class=anchor aria-hidden=true href=#regen-an-object-layout-regenerator-on-large-scale-production-hpc-systems>#</a></h3><p><em>Dong Kyu Sung, Sunggon Kim, Sangjin Lee 0003, Houjun Tang <em>et al.</em></em></p><p><strong>TL;DR</strong> — Regen transparently reorganizes the on-disk layout of HDF5 and NetCDF objects in parallel file systems to match actual access patterns, improving I/O performance without application changes.</p><p><strong>Why notable</strong> — Deployed and validated on a production HPC system, showing significant I/O speedups for real scientific datasets, making it immediately relevant to storage administrators.</p><hr><h3 id=formal-definition-and-implementation-of-reproducibility-tenets-for-computational-workflows>Formal definition and implementation of reproducibility tenets for computational workflows<a hidden class=anchor aria-hidden=true href=#formal-definition-and-implementation-of-reproducibility-tenets-for-computational-workflows>#</a></h3><p><em>Nicholas J. Pritchard, Andreas Wicenec</em></p><p><strong>TL;DR</strong> — Formalizes a set of reproducibility requirements for scientific workflows and implements a verification layer within the DALIUGE workflow engine that checks compliance at design and execution time.</p><p><strong>Why notable</strong> — Provides the community with a concrete, tool-supported definition of workflow reproducibility, moving beyond aspirational guidelines to enforceable runtime checks.</p></div><footer class=post-footer><ul class=post-tags></ul><nav class=paginav><a class=prev href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/eurosys-2025/><span class=title>« Prev</span>
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