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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 2024 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="12 papers selected.
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Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
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Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini et al.
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TL;DR — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.
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Why notable — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/fgcs-2024/><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-2024/><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-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="FGCS 2024 Digest"><meta property="og:description" content="12 papers selected.
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Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini et al.
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TL;DR — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.
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Why notable — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale."><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="2024-01-01T00:00:00+00:00"><meta property="article:modified_time" content="2024-01-01T00:00:00+00:00"><meta name=twitter:card content="summary"><meta name=twitter:title content="FGCS 2024 Digest"><meta name=twitter:description content="12 papers selected.
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Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini et al.
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TL;DR — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.
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Why notable — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale."><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 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/fgcs-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"FGCS 2024 Digest","name":"FGCS 2024 Digest","description":"12 papers selected.\nQuantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini et al.\nTL;DR — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.\nWhy notable — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale.\n","keywords":[],"articleBody":"12 papers selected.\nQuantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini et al.\nTL;DR — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.\nWhy notable — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale.\nIntegrating quantum computing resources into scientific HPC ecosystems Thomas L. Beck, Alessandro Baroni 0003, Ryan S. Bennink, Gilles Buchs et al.\nTL;DR — Describes the architecture and middleware decisions made at Oak Ridge National Laboratory to expose quantum devices as first-class resources within an existing HPC facility.\nWhy notable — One of the first concrete descriptions of a production-scale quantum-HPC integration, providing a template other facilities can follow.\nLotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures Jonathan Bader, Fabian Lehmann, Lauritz Thamsen, Ulf Leser et al.\nTL;DR — Lotaru learns lightweight per-workflow runtime prediction models locally on each node using micro-benchmarks, eliminating the need for a centralized profiling service on heterogeneous clusters.\nWhy notable — Addresses a core bottleneck in scientific workflow scheduling with a practical, evaluated approach that works without historical traces.\nThe globus compute dataset: An open function-as-a-service dataset from the edge to the cloud André Bauer 0001, Haochen Pan, Ryan Chard, Yadu N. Babuji et al.\nTL;DR — Releases a large real-world dataset of function invocations across edge, campus, and cloud resources collected from the Globus Compute FaaS platform, along with workload analysis.\nWhy notable — Provides the community with a rare, richly annotated dataset for benchmarking distributed FaaS schedulers and studying edge-to-cloud task patterns at scale.\nA survey on checkpointing strategies: Should we always checkpoint à la Young/Daly? Leonardo Bautista-Gomez, Anne Benoit, Sheng Di, Thomas Hérault et al.\nTL;DR — Surveys decades of checkpointing research and rigorously examines when the classic Young/Daly formula is optimal versus when multi-level, coordinated, or application-aware strategies outperform it.\nWhy notable — A definitive reference for HPC fault tolerance that unifies scattered results and provides clear guidance on choosing a checkpointing strategy for modern exascale workloads.\nScalable I/O aggregation for asynchronous multi-level checkpointing Mikaila J. Gossman, Bogdan Nicolae, Jon C. Calhoun\nTL;DR — Proposes an aggregation layer that pipelines writes across multiple memory and storage tiers asynchronously, reducing checkpoint overhead for large-scale MPI applications.\nWhy notable — Delivers measurable improvements in checkpoint throughput on realistic HPC applications, directly addressing the I/O bottleneck at exascale.\nStructMesh: A storage framework for serverless computing continuum Diana Carrizales-Espinoza, Dante D. Sánchez-Gallegos, José Luis González Compeán, Jesús Carretero 0001\nTL;DR — Introduces a hierarchical storage abstraction that unifies data management across edge, fog, and cloud tiers for serverless workflows, supporting structured data access patterns.\nWhy notable — Offers a practical, evaluated solution to the data management gap in cloud-edge serverless architectures, relevant to scientific and industrial workflow deployment.\nPaving the way to hybrid quantum-classical scientific workflows Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Ewa Deelman\nTL;DR — Defines a taxonomy and reference architecture for hybrid quantum-classical workflows, mapping quantum circuit execution onto existing scientific workflow management system abstractions.\nWhy notable — Provides the conceptual foundations needed to extend tools like Pegasus or Swift to orchestrate quantum subroutines within larger scientific pipelines.\nOnline learning and continuous model upgrading with data streams through the Kafka-ML framework Alejandro Carnero, Cristian Martín 0002, Gwanggil Jeon, Manuel Díaz\nTL;DR — Extends Kafka-ML to support incremental online learning directly from streaming data topics, enabling continuous model updates without retraining from scratch in edge-cloud deployments.\nWhy notable — Demonstrates a full open-source framework that bridges stream processing and ML model lifecycle management, with relevance to IoT and real-time analytics pipelines.\nGRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC systems Martin Molan, Mohsen Seyedkazemi Ardebili, Junaid Ahmed Khan, Francesco Beneventi et al.\nTL;DR — Uses graph neural networks trained on node telemetry to predict imminent failures in exascale HPC clusters before they occur, enabling proactive maintenance and job migration.\nWhy notable — Shows that temporal graph models over system topology substantially outperform per-node anomaly detection, with validation on a real pre-exascale machine.\nQFaaS: A Serverless Function-as-a-Service framework for Quantum computing Hoa T. Nguyen, Muhammad Usman 0009, Rajkumar Buyya\nTL;DR — Proposes QFaaS, a broker-based FaaS platform that abstracts heterogeneous quantum hardware providers behind a unified serverless interface with automatic circuit compilation and resource selection.\nWhy notable — Addresses the pressing need for a cloud-agnostic quantum execution layer, laying groundwork for portable quantum applications across IBM, IonQ, and similar backends.\nEnabling federated learning across the computing continuum: Systems, challenges and future directions Cèdric Prigent, Alexandru Costan, Gabriel Antoniu, Loïc Cudennec\nTL;DR — Systematically surveys the technical barriers to training federated learning models that span IoT devices, edge servers, and cloud data centers, and proposes a reference architecture addressing heterogeneity and mobility.\nWhy notable — A timely synthesis that clarifies open problems at the intersection of federated learning and the compute continuum, useful as a roadmap for system builders.\n","wordCount":"848","inLanguage":"en","datePublished":"2024-01-01T00:00:00Z","dateModified":"2024-01-01T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/fgcs-2024/"},"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 2024 Digest</h1><div class=post-meta><span title='2024-01-01 00:00:00 +0000 UTC'>January 1, 2024</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>12 papers selected.</p><hr><h3 id=quantum-centric-supercomputing-for-materials-science-a-perspective-on-challenges-and-future-directions>Quantum-centric supercomputing for materials science: A perspective on challenges and future directions<a hidden class=anchor aria-hidden=true href=#quantum-centric-supercomputing-for-materials-science-a-perspective-on-challenges-and-future-directions>#</a></h3><p><em>Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini <em>et al.</em></em></p><p><strong>TL;DR</strong> — A comprehensive roadmap from IBM, national labs, and universities identifying key algorithmic, software, and hardware challenges for using quantum processors alongside classical HPC to advance materials science simulations.</p><p><strong>Why notable</strong> — Essential reading for any researcher planning quantum-classical hybrid workflows, covering the full stack from error mitigation to application mapping at scale.</p><hr><h3 id=integrating-quantum-computing-resources-into-scientific-hpc-ecosystems>Integrating quantum computing resources into scientific HPC ecosystems<a hidden class=anchor aria-hidden=true href=#integrating-quantum-computing-resources-into-scientific-hpc-ecosystems>#</a></h3><p><em>Thomas L. Beck, Alessandro Baroni 0003, Ryan S. Bennink, Gilles Buchs <em>et al.</em></em></p><p><strong>TL;DR</strong> — Describes the architecture and middleware decisions made at Oak Ridge National Laboratory to expose quantum devices as first-class resources within an existing HPC facility.</p><p><strong>Why notable</strong> — One of the first concrete descriptions of a production-scale quantum-HPC integration, providing a template other facilities can follow.</p><hr><h3 id=lotaru-locally-predicting-workflow-task-runtimes-for-resource-management-on-heterogeneous-infrastructures>Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures<a hidden class=anchor aria-hidden=true href=#lotaru-locally-predicting-workflow-task-runtimes-for-resource-management-on-heterogeneous-infrastructures>#</a></h3><p><em>Jonathan Bader, Fabian Lehmann, Lauritz Thamsen, Ulf Leser <em>et al.</em></em></p><p><strong>TL;DR</strong> — Lotaru learns lightweight per-workflow runtime prediction models locally on each node using micro-benchmarks, eliminating the need for a centralized profiling service on heterogeneous clusters.</p><p><strong>Why notable</strong> — Addresses a core bottleneck in scientific workflow scheduling with a practical, evaluated approach that works without historical traces.</p><hr><h3 id=the-globus-compute-dataset-an-open-function-as-a-service-dataset-from-the-edge-to-the-cloud>The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud<a hidden class=anchor aria-hidden=true href=#the-globus-compute-dataset-an-open-function-as-a-service-dataset-from-the-edge-to-the-cloud>#</a></h3><p><em>André Bauer 0001, Haochen Pan, Ryan Chard, Yadu N. Babuji <em>et al.</em></em></p><p><strong>TL;DR</strong> — Releases a large real-world dataset of function invocations across edge, campus, and cloud resources collected from the Globus Compute FaaS platform, along with workload analysis.</p><p><strong>Why notable</strong> — Provides the community with a rare, richly annotated dataset for benchmarking distributed FaaS schedulers and studying edge-to-cloud task patterns at scale.</p><hr><h3 id=a-survey-on-checkpointing-strategies-should-we-always-checkpoint-à-la-youngdaly>A survey on checkpointing strategies: Should we always checkpoint à la Young/Daly?<a hidden class=anchor aria-hidden=true href=#a-survey-on-checkpointing-strategies-should-we-always-checkpoint-à-la-youngdaly>#</a></h3><p><em>Leonardo Bautista-Gomez, Anne Benoit, Sheng Di, Thomas Hérault <em>et al.</em></em></p><p><strong>TL;DR</strong> — Surveys decades of checkpointing research and rigorously examines when the classic Young/Daly formula is optimal versus when multi-level, coordinated, or application-aware strategies outperform it.</p><p><strong>Why notable</strong> — A definitive reference for HPC fault tolerance that unifies scattered results and provides clear guidance on choosing a checkpointing strategy for modern exascale workloads.</p><hr><h3 id=scalable-io-aggregation-for-asynchronous-multi-level-checkpointing>Scalable I/O aggregation for asynchronous multi-level checkpointing<a hidden class=anchor aria-hidden=true href=#scalable-io-aggregation-for-asynchronous-multi-level-checkpointing>#</a></h3><p><em>Mikaila J. Gossman, Bogdan Nicolae, Jon C. Calhoun</em></p><p><strong>TL;DR</strong> — Proposes an aggregation layer that pipelines writes across multiple memory and storage tiers asynchronously, reducing checkpoint overhead for large-scale MPI applications.</p><p><strong>Why notable</strong> — Delivers measurable improvements in checkpoint throughput on realistic HPC applications, directly addressing the I/O bottleneck at exascale.</p><hr><h3 id=structmesh-a-storage-framework-for-serverless-computing-continuum>StructMesh: A storage framework for serverless computing continuum<a hidden class=anchor aria-hidden=true href=#structmesh-a-storage-framework-for-serverless-computing-continuum>#</a></h3><p><em>Diana Carrizales-Espinoza, Dante D. Sánchez-Gallegos, José Luis González Compeán, Jesús Carretero 0001</em></p><p><strong>TL;DR</strong> — Introduces a hierarchical storage abstraction that unifies data management across edge, fog, and cloud tiers for serverless workflows, supporting structured data access patterns.</p><p><strong>Why notable</strong> — Offers a practical, evaluated solution to the data management gap in cloud-edge serverless architectures, relevant to scientific and industrial workflow deployment.</p><hr><h3 id=paving-the-way-to-hybrid-quantum-classical-scientific-workflows>Paving the way to hybrid quantum-classical scientific workflows<a hidden class=anchor aria-hidden=true href=#paving-the-way-to-hybrid-quantum-classical-scientific-workflows>#</a></h3><p><em>Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Ewa Deelman</em></p><p><strong>TL;DR</strong> — Defines a taxonomy and reference architecture for hybrid quantum-classical workflows, mapping quantum circuit execution onto existing scientific workflow management system abstractions.</p><p><strong>Why notable</strong> — Provides the conceptual foundations needed to extend tools like Pegasus or Swift to orchestrate quantum subroutines within larger scientific pipelines.</p><hr><h3 id=online-learning-and-continuous-model-upgrading-with-data-streams-through-the-kafka-ml-framework>Online learning and continuous model upgrading with data streams through the Kafka-ML framework<a hidden class=anchor aria-hidden=true href=#online-learning-and-continuous-model-upgrading-with-data-streams-through-the-kafka-ml-framework>#</a></h3><p><em>Alejandro Carnero, Cristian Martín 0002, Gwanggil Jeon, Manuel Díaz</em></p><p><strong>TL;DR</strong> — Extends Kafka-ML to support incremental online learning directly from streaming data topics, enabling continuous model updates without retraining from scratch in edge-cloud deployments.</p><p><strong>Why notable</strong> — Demonstrates a full open-source framework that bridges stream processing and ML model lifecycle management, with relevance to IoT and real-time analytics pipelines.</p><hr><h3 id=graafe-graph-anomaly-anticipation-framework-for-exascale-hpc-systems>GRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC systems<a hidden class=anchor aria-hidden=true href=#graafe-graph-anomaly-anticipation-framework-for-exascale-hpc-systems>#</a></h3><p><em>Martin Molan, Mohsen Seyedkazemi Ardebili, Junaid Ahmed Khan, Francesco Beneventi <em>et al.</em></em></p><p><strong>TL;DR</strong> — Uses graph neural networks trained on node telemetry to predict imminent failures in exascale HPC clusters before they occur, enabling proactive maintenance and job migration.</p><p><strong>Why notable</strong> — Shows that temporal graph models over system topology substantially outperform per-node anomaly detection, with validation on a real pre-exascale machine.</p><hr><h3 id=qfaas-a-serverless-function-as-a-service-framework-for-quantum-computing>QFaaS: A Serverless Function-as-a-Service framework for Quantum computing<a hidden class=anchor aria-hidden=true href=#qfaas-a-serverless-function-as-a-service-framework-for-quantum-computing>#</a></h3><p><em>Hoa T. Nguyen, Muhammad Usman 0009, Rajkumar Buyya</em></p><p><strong>TL;DR</strong> — Proposes QFaaS, a broker-based FaaS platform that abstracts heterogeneous quantum hardware providers behind a unified serverless interface with automatic circuit compilation and resource selection.</p><p><strong>Why notable</strong> — Addresses the pressing need for a cloud-agnostic quantum execution layer, laying groundwork for portable quantum applications across IBM, IonQ, and similar backends.</p><hr><h3 id=enabling-federated-learning-across-the-computing-continuum-systems-challenges-and-future-directions>Enabling federated learning across the computing continuum: Systems, challenges and future directions<a hidden class=anchor aria-hidden=true href=#enabling-federated-learning-across-the-computing-continuum-systems-challenges-and-future-directions>#</a></h3><p><em>Cèdric Prigent, Alexandru Costan, Gabriel Antoniu, Loïc Cudennec</em></p><p><strong>TL;DR</strong> — Systematically surveys the technical barriers to training federated learning models that span IoT devices, edge servers, and cloud data centers, and proposes a reference architecture addressing heterogeneity and mobility.</p><p><strong>Why notable</strong> — A timely synthesis that clarifies open problems at the intersection of federated learning and the compute continuum, useful as a roadmap for system builders.</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-2024/><span class=title>« Prev</span>
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