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Pushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms
Honghui Shang, Ying Liu 0055, Zhikun Wu, Zhenchuan Chen et al.
TL;DR — Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits.
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System
Kylee Santos, Stan G. Moore, Tomas Oppelstrup, Amirali Sharifian et al."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/sc-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/sc-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/sc-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="SC 2024 Digest"><meta property="og:description" content="15 papers selected.
Pushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms Honghui Shang, Ying Liu 0055, Zhikun Wu, Zhenchuan Chen et al.
TL;DR — Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits.
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System Kylee Santos, Stan G. Moore, Tomas Oppelstrup, Amirali Sharifian et al."><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="SC 2024 Digest"><meta name=twitter:description content="15 papers selected.
Pushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms Honghui Shang, Ying Liu 0055, Zhikun Wu, Zhenchuan Chen et al.
TL;DR — Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits.
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System Kylee Santos, Stan G. Moore, Tomas Oppelstrup, Amirali Sharifian et al."><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":"SC 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/sc-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"SC 2024 Digest","name":"SC 2024 Digest","description":"15 papers selected.\nPushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms Honghui Shang, Ying Liu 0055, Zhikun Wu, Zhenchuan Chen et al.\nTL;DR — Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits.\nBreaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System Kylee Santos, Stan G. Moore, Tomas Oppelstrup, Amirali Sharifian et al.\n","keywords":[],"articleBody":"15 papers selected.\nPushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms Honghui Shang, Ying Liu 0055, Zhikun Wu, Zhenchuan Chen et al.\nTL;DR — Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits.\nBreaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System Kylee Santos, Stan G. Moore, Tomas Oppelstrup, Amirali Sharifian et al.\nTL;DR — Demonstrates how a Cerebras wafer-scale engine shatters the classical MD timescale barrier, enabling microsecond-regime atomistic simulation at unprecedented speed.\nScaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day Jianxiong Li, Boyang Li, Zhuoqiang Guo, Mingzhen Li 0001 et al.\nTL;DR — Achieves 149 ns/day for large-scale deep-potential MD, combining neural-network potentials and HPC engineering to approach DFT accuracy at AIMD-like scale.\nBreaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials Ryan Stocks, Jorge L. Galvez Vallejo, Fiona C. Y. Yu, Calum Snowdon et al.\nTL;DR — First demonstration of MP2-level AIMD at the million-electron and exaFLOP/s scale, a landmark in quantum chemistry on supercomputers.\nFire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning Wei An, Xiao Bi, Guanting Chen 0002, Shanhuang Chen et al.\nTL;DR — Full system co-design report from DeepSeek’s AI-HPC cluster showing 40% cost reduction vs. NVIDIA DGX through network/software optimizations, with production evidence at scale.\nMProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization Gautham Dharuman, Kyle Hippe, Alexander Brace, Sam Foreman et al.\nTL;DR — First exaFLOP/s AI science workflow, integrating multimodal protein design with DPO alignment at supercomputing scale across Frontier and Aurora.\nORBIT: Oak Ridge Base Foundation Model for Earth System Predictability Xiao Wang 0004, Siyan Liu, Aristeidis Tsaris, Jong-Youl Choi et al.\nTL;DR — Introduces a large foundation model for Earth system prediction trained on Frontier, demonstrating how exascale AI infrastructure enables climate-scale spatiotemporal modeling.\nDemocratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan, John Kirchenbauer et al.\nTL;DR — Presents an open-source framework for LLM training at thousands-of-GPU scale, systematically analyzing throughput, memory, and communication trade-offs on leadership supercomputers.\nExploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects Daniele De Sensi, Lorenzo Pichetti, Flavio Vella, Tiziano De Matteis et al.\nTL;DR — Comprehensive empirical study of GPU-to-GPU communication across six major supercomputers, revealing bottlenecks and bandwidth characteristics relevant to all distributed AI/HPC workloads.\nNetwork-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI Mikhail Khalilov, Salvatore Di Girolamo, Marcin Chrapek, Rami Nudelman et al.\nTL;DR — Achieves bandwidth-optimal collective communication by offloading broadcast and allgather to SmartNICs, directly benefiting large-scale distributed deep learning.\nA Workflow Roofline Model for End-to-End Workflow Performance Analysis Nan Ding 0006, Brian Austin, Yang Liu 0179, Neil Mehta et al.\nTL;DR — Extends the Roofline model to full end-to-end HPC workflows, enabling systematic performance diagnosis across compute, I/O, and data movement stages.\nGVARP: Detecting Performance Variance on Large-Scale Heterogeneous Systems Xin You 0001, Zhibo Xuan, Hailong Yang 0002, Zhongzhi Luan et al.\nTL;DR — Identifies and diagnoses GPU performance variance at scale on heterogeneous supercomputers, an increasingly critical issue for reproducibility and efficiency.\nA Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale Wesley Brewer, Matthias Maiterth, Vineet Kumar, Rafal P. Wojda et al.\nTL;DR — First deployment of a digital twin for a liquid-cooled exascale system (Frontier), enabling real-time thermal and power management with validated empirical results.\nDoubling Graph Traversal Efficiency to 198 TeraTEPS on the Supercomputer Fugaku Junya Arai, Masahiro Nakao, Yuto Inoue, Kanto Teranishi et al.\nTL;DR — Sets a new world record for graph traversal at 198 TTEPS on Fugaku through novel communication and load-balancing techniques, a landmark Graph500 result.\nMegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive Workloads Luke Logan, Anthony Kougkas, Xian-He Sun\nTL;DR — Novel storage abstraction that transparently tiered memory and storage hierarchies, delivering near-DRAM performance for data-intensive HPC and AI workloads.\n","wordCount":"660","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/sc-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">SC 2024 Digest</h1><div class=post-meta><span title='2024-01-01 00:00:00 +0000 UTC'>January 1, 2024</span>&nbsp;·&nbsp;<span>Publish Assistant</span></div></header><div class="post-content md-content"><p>15 papers selected.</p><hr><h3 id=pushing-the-limit-of-quantum-mechanical-simulation-to-the-raman-spectra-of-a-biological-system-with-100-million-atoms>Pushing the Limit of Quantum Mechanical Simulation to the Raman Spectra of a Biological System with 100 Million Atoms<a hidden class=anchor aria-hidden=true href=#pushing-the-limit-of-quantum-mechanical-simulation-to-the-raman-spectra-of-a-biological-system-with-100-million-atoms>#</a></h3><p><em>Honghui Shang, Ying Liu 0055, Zhikun Wu, Zhenchuan Chen <em>et al.</em></em></p><p><strong>TL;DR</strong> — Gordon Bell-class result scaling ab initio Raman spectroscopy to 100 million atoms, pushing quantum-chemical simulation well beyond prior limits.</p><hr><h3 id=breaking-the-molecular-dynamics-timescale-barrier-using-a-wafer-scale-system>Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System<a hidden class=anchor aria-hidden=true href=#breaking-the-molecular-dynamics-timescale-barrier-using-a-wafer-scale-system>#</a></h3><p><em>Kylee Santos, Stan G. Moore, Tomas Oppelstrup, Amirali Sharifian <em>et al.</em></em></p><p><strong>TL;DR</strong> — Demonstrates how a Cerebras wafer-scale engine shatters the classical MD timescale barrier, enabling microsecond-regime atomistic simulation at unprecedented speed.</p><hr><h3 id=scaling-molecular-dynamics-with-ab-initio-accuracy-to-149-nanoseconds-per-day>Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day<a hidden class=anchor aria-hidden=true href=#scaling-molecular-dynamics-with-ab-initio-accuracy-to-149-nanoseconds-per-day>#</a></h3><p><em>Jianxiong Li, Boyang Li, Zhuoqiang Guo, Mingzhen Li 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Achieves 149 ns/day for large-scale deep-potential MD, combining neural-network potentials and HPC engineering to approach DFT accuracy at AIMD-like scale.</p><hr><h3 id=breaking-the-million-electron-and-1-eflops-barriers-biomolecular-scale-ab-initio-molecular-dynamics-using-mp2-potentials>Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials<a hidden class=anchor aria-hidden=true href=#breaking-the-million-electron-and-1-eflops-barriers-biomolecular-scale-ab-initio-molecular-dynamics-using-mp2-potentials>#</a></h3><p><em>Ryan Stocks, Jorge L. Galvez Vallejo, Fiona C. Y. Yu, Calum Snowdon <em>et al.</em></em></p><p><strong>TL;DR</strong> — First demonstration of MP2-level AIMD at the million-electron and exaFLOP/s scale, a landmark in quantum chemistry on supercomputers.</p><hr><h3 id=fire-flyer-ai-hpc-a-cost-effective-software-hardware-co-design-for-deep-learning>Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning<a hidden class=anchor aria-hidden=true href=#fire-flyer-ai-hpc-a-cost-effective-software-hardware-co-design-for-deep-learning>#</a></h3><p><em>Wei An, Xiao Bi, Guanting Chen 0002, Shanhuang Chen <em>et al.</em></em></p><p><strong>TL;DR</strong> — Full system co-design report from DeepSeek&rsquo;s AI-HPC cluster showing 40% cost reduction vs. NVIDIA DGX through network/software optimizations, with production evidence at scale.</p><hr><h3 id=mprot-dpo-breaking-the-exaflops-barrier-for-multimodal-protein-design-workflows-with-direct-preference-optimization>MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization<a hidden class=anchor aria-hidden=true href=#mprot-dpo-breaking-the-exaflops-barrier-for-multimodal-protein-design-workflows-with-direct-preference-optimization>#</a></h3><p><em>Gautham Dharuman, Kyle Hippe, Alexander Brace, Sam Foreman <em>et al.</em></em></p><p><strong>TL;DR</strong> — First exaFLOP/s AI science workflow, integrating multimodal protein design with DPO alignment at supercomputing scale across Frontier and Aurora.</p><hr><h3 id=orbit-oak-ridge-base-foundation-model-for-earth-system-predictability>ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability<a hidden class=anchor aria-hidden=true href=#orbit-oak-ridge-base-foundation-model-for-earth-system-predictability>#</a></h3><p><em>Xiao Wang 0004, Siyan Liu, Aristeidis Tsaris, Jong-Youl Choi <em>et al.</em></em></p><p><strong>TL;DR</strong> — Introduces a large foundation model for Earth system prediction trained on Frontier, demonstrating how exascale AI infrastructure enables climate-scale spatiotemporal modeling.</p><hr><h3 id=democratizing-ai-open-source-scalable-llm-training-on-gpu-based-supercomputers>Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers<a hidden class=anchor aria-hidden=true href=#democratizing-ai-open-source-scalable-llm-training-on-gpu-based-supercomputers>#</a></h3><p><em>Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan, John Kirchenbauer <em>et al.</em></em></p><p><strong>TL;DR</strong> — Presents an open-source framework for LLM training at thousands-of-GPU scale, systematically analyzing throughput, memory, and communication trade-offs on leadership supercomputers.</p><hr><h3 id=exploring-gpu-to-gpu-communication-insights-into-supercomputer-interconnects>Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects<a hidden class=anchor aria-hidden=true href=#exploring-gpu-to-gpu-communication-insights-into-supercomputer-interconnects>#</a></h3><p><em>Daniele De Sensi, Lorenzo Pichetti, Flavio Vella, Tiziano De Matteis <em>et al.</em></em></p><p><strong>TL;DR</strong> — Comprehensive empirical study of GPU-to-GPU communication across six major supercomputers, revealing bottlenecks and bandwidth characteristics relevant to all distributed AI/HPC workloads.</p><hr><h3 id=network-offloaded-bandwidth-optimal-broadcast-and-allgather-for-distributed-ai>Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI<a hidden class=anchor aria-hidden=true href=#network-offloaded-bandwidth-optimal-broadcast-and-allgather-for-distributed-ai>#</a></h3><p><em>Mikhail Khalilov, Salvatore Di Girolamo, Marcin Chrapek, Rami Nudelman <em>et al.</em></em></p><p><strong>TL;DR</strong> — Achieves bandwidth-optimal collective communication by offloading broadcast and allgather to SmartNICs, directly benefiting large-scale distributed deep learning.</p><hr><h3 id=a-workflow-roofline-model-for-end-to-end-workflow-performance-analysis>A Workflow Roofline Model for End-to-End Workflow Performance Analysis<a hidden class=anchor aria-hidden=true href=#a-workflow-roofline-model-for-end-to-end-workflow-performance-analysis>#</a></h3><p><em>Nan Ding 0006, Brian Austin, Yang Liu 0179, Neil Mehta <em>et al.</em></em></p><p><strong>TL;DR</strong> — Extends the Roofline model to full end-to-end HPC workflows, enabling systematic performance diagnosis across compute, I/O, and data movement stages.</p><hr><h3 id=gvarp-detecting-performance-variance-on-large-scale-heterogeneous-systems>GVARP: Detecting Performance Variance on Large-Scale Heterogeneous Systems<a hidden class=anchor aria-hidden=true href=#gvarp-detecting-performance-variance-on-large-scale-heterogeneous-systems>#</a></h3><p><em>Xin You 0001, Zhibo Xuan, Hailong Yang 0002, Zhongzhi Luan <em>et al.</em></em></p><p><strong>TL;DR</strong> — Identifies and diagnoses GPU performance variance at scale on heterogeneous supercomputers, an increasingly critical issue for reproducibility and efficiency.</p><hr><h3 id=a-digital-twin-framework-for-liquid-cooled-supercomputers-as-demonstrated-at-exascale>A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale<a hidden class=anchor aria-hidden=true href=#a-digital-twin-framework-for-liquid-cooled-supercomputers-as-demonstrated-at-exascale>#</a></h3><p><em>Wesley Brewer, Matthias Maiterth, Vineet Kumar, Rafal P. Wojda <em>et al.</em></em></p><p><strong>TL;DR</strong> — First deployment of a digital twin for a liquid-cooled exascale system (Frontier), enabling real-time thermal and power management with validated empirical results.</p><hr><h3 id=doubling-graph-traversal-efficiency-to-198-terateps-on-the-supercomputer-fugaku>Doubling Graph Traversal Efficiency to 198 TeraTEPS on the Supercomputer Fugaku<a hidden class=anchor aria-hidden=true href=#doubling-graph-traversal-efficiency-to-198-terateps-on-the-supercomputer-fugaku>#</a></h3><p><em>Junya Arai, Masahiro Nakao, Yuto Inoue, Kanto Teranishi <em>et al.</em></em></p><p><strong>TL;DR</strong> — Sets a new world record for graph traversal at 198 TTEPS on Fugaku through novel communication and load-balancing techniques, a landmark Graph500 result.</p><hr><h3 id=megammap-blurring-the-boundary-between-memory-and-storage-for-data-intensive-workloads>MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive Workloads<a hidden class=anchor aria-hidden=true href=#megammap-blurring-the-boundary-between-memory-and-storage-for-data-intensive-workloads>#</a></h3><p><em>Luke Logan, Anthony Kougkas, Xian-He Sun</em></p><p><strong>TL;DR</strong> — Novel storage abstraction that transparently tiered memory and storage hierarchies, delivering near-DRAM performance for data-intensive HPC and AI workloads.</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/nsdi-2024/><span class=title>« Prev</span>
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