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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>NSDI 2024 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="13 papers selected.
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MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs
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Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang et al.
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TL;DR — ByteDance’s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.
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Harmony: A Congestion-free Datacenter Architecture
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Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys et al."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/nsdi-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/nsdi-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/nsdi-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="NSDI 2024 Digest"><meta property="og:description" content="13 papers selected.
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MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang et al.
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TL;DR — ByteDance’s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.
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Harmony: A Congestion-free Datacenter Architecture Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys 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="NSDI 2024 Digest"><meta name=twitter:description content="13 papers selected.
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MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang et al.
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TL;DR — ByteDance’s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.
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Harmony: A Congestion-free Datacenter Architecture Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys 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":"NSDI 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/nsdi-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"NSDI 2024 Digest","name":"NSDI 2024 Digest","description":"13 papers selected.\nMegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang et al.\nTL;DR — ByteDance\u0026rsquo;s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.\nHarmony: A Congestion-free Datacenter Architecture Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys et al.\n","keywords":[],"articleBody":"13 papers selected.\nMegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang et al.\nTL;DR — ByteDance’s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.\nHarmony: A Congestion-free Datacenter Architecture Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys et al.\nTL;DR — Proposes rethinking datacenter fabrics to eliminate congestion by construction rather than managing it reactively, achieving line-rate throughput without per-packet feedback from Cornell and Google.\nDINT: Fast In-Kernel Distributed Transactions with eBPF Yang Zhou 0008, Xingyu Xiang, Matthew Kiley, Sowmya Dharanipragada et al.\nTL;DR — Demonstrates that eBPF programs executing entirely inside the kernel can enforce linearizable, ACID-compliant distributed transactions at dramatically lower latency than user-space approaches.\nMaking Kernel Bypass Practical for the Cloud with Junction Joshua Fried, Gohar Irfan Chaudhry, Enrique Saurez, Esha Choukse et al.\nTL;DR — Junction (MIT + Microsoft) is the first system to bring full kernel-bypass networking to multi-tenant cloud VMs without requiring application modifications or sacrificing isolation.\nSIEVE is Simpler than LRU: an Efficient Turn-Key Eviction Algorithm for Web Caches Yazhuo Zhang, Juncheng Yang, Yao Yue, Ymir Vigfusson et al.\nTL;DR — Surprising finding that a single-queue eviction policy requiring almost no state changes outperforms LRU and all modern approximations on production web-cache workloads.\nA large-scale deployment of DCTCP Abhishek Dhamija, Balasubramanian Madhavan, Hechao Li, Jie Meng et al.\nTL;DR — A rare, candid production report from Meta on deploying DCTCP at hyperscale, surfacing unexpected interactions with heterogeneous hardware, cross-traffic, and operational constraints.\nBrain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li 0002 et al.\nTL;DR — Executes neural-network inference for traffic classification directly in the programmable switch data plane at line rate, eliminating the round-trip to a CPU-based classifier.\nHorus: Granular In-Network Task Scheduler for Cloud Datacenters Parham Yassini, Khaled Diab 0001, Saeed Mahloujifar, Mohamed Hefeeda\nTL;DR — Offloads microsecond-granularity task scheduling decisions into programmable switches, reducing load-balancing latency by orders of magnitude compared to software schedulers.\nCAPA: An Architecture For Operating Cluster Networks With High Availability Bingzhe Liu, Colin Scott, Mukarram Tariq, Andrew D. Ferguson et al.\nTL;DR — Google’s production architecture for maintaining five-nines availability in Jupiter-scale cluster networks, detailing how control-plane redundancy and fast failover are achieved in practice.\nRevisiting Congestion Control for Lossless Ethernet Yiran Zhang, Qingkai Meng 0001, Chaolei Hu, Fengyuan Ren\nTL;DR — Identifies fundamental flaws in PFC-based lossless Ethernet that cause cascading head-of-line blocking and proposes a redesigned congestion control that avoids them.\nSirius: Composing Network Function Chains into P4-Capable Edge Gateways Jiaqi Gao, Jiamin Cao, Yifan Li, Mengqi Liu 0001 et al.\nTL;DR — Alibaba’s production system for composing arbitrary NF chains into P4-programmed edge gateways, replacing a fleet of dedicated middleboxes and cutting per-packet processing cost significantly.\nCASSINI: Network-Aware Job Scheduling in Machine Learning Clusters Sudarsanan Rajasekaran, Manya Ghobadi, Aditya Akella\nTL;DR — Shows that ignoring network topology when scheduling ML training jobs causes severe collective-communication contention, and that topology-aware co-scheduling yields substantial throughput gains.\nCloudcast: High-Throughput, Cost-Aware Overlay Multicast in the Cloud Sarah Wooders, Shu Liu, Paras Jain 0001, Xiangxi Mo et al.\nTL;DR — Berkeley/Penn system that builds overlay multicast trees across cloud regions optimized for egress cost, enabling high-throughput data dissemination at a fraction of unicast cloud egress fees.\n","wordCount":"571","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/nsdi-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">NSDI 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>13 papers selected.</p><hr><h3 id=megascale-scaling-large-language-model-training-to-more-than-10-000-gpus>MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs<a hidden class=anchor aria-hidden=true href=#megascale-scaling-large-language-model-training-to-more-than-10-000-gpus>#</a></h3><p><em>Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang <em>et al.</em></em></p><p><strong>TL;DR</strong> — ByteDance’s full production account of training LLMs at 10,000+ GPUs, with novel co-design of the network stack, fault tolerance, and collective communication to sustain near-linear scaling.</p><hr><h3 id=harmony-a-congestion-free-datacenter-architecture>Harmony: A Congestion-free Datacenter Architecture<a hidden class=anchor aria-hidden=true href=#harmony-a-congestion-free-datacenter-architecture>#</a></h3><p><em>Saksham Agarwal, Qizhe Cai, Rachit Agarwal 0001, David B. Shmoys <em>et al.</em></em></p><p><strong>TL;DR</strong> — Proposes rethinking datacenter fabrics to eliminate congestion by construction rather than managing it reactively, achieving line-rate throughput without per-packet feedback from Cornell and Google.</p><hr><h3 id=dint-fast-in-kernel-distributed-transactions-with-ebpf>DINT: Fast In-Kernel Distributed Transactions with eBPF<a hidden class=anchor aria-hidden=true href=#dint-fast-in-kernel-distributed-transactions-with-ebpf>#</a></h3><p><em>Yang Zhou 0008, Xingyu Xiang, Matthew Kiley, Sowmya Dharanipragada <em>et al.</em></em></p><p><strong>TL;DR</strong> — Demonstrates that eBPF programs executing entirely inside the kernel can enforce linearizable, ACID-compliant distributed transactions at dramatically lower latency than user-space approaches.</p><hr><h3 id=making-kernel-bypass-practical-for-the-cloud-with-junction>Making Kernel Bypass Practical for the Cloud with Junction<a hidden class=anchor aria-hidden=true href=#making-kernel-bypass-practical-for-the-cloud-with-junction>#</a></h3><p><em>Joshua Fried, Gohar Irfan Chaudhry, Enrique Saurez, Esha Choukse <em>et al.</em></em></p><p><strong>TL;DR</strong> — Junction (MIT + Microsoft) is the first system to bring full kernel-bypass networking to multi-tenant cloud VMs without requiring application modifications or sacrificing isolation.</p><hr><h3 id=sieve-is-simpler-than-lru-an-efficient-turn-key-eviction-algorithm-for-web-caches>SIEVE is Simpler than LRU: an Efficient Turn-Key Eviction Algorithm for Web Caches<a hidden class=anchor aria-hidden=true href=#sieve-is-simpler-than-lru-an-efficient-turn-key-eviction-algorithm-for-web-caches>#</a></h3><p><em>Yazhuo Zhang, Juncheng Yang, Yao Yue, Ymir Vigfusson <em>et al.</em></em></p><p><strong>TL;DR</strong> — Surprising finding that a single-queue eviction policy requiring almost no state changes outperforms LRU and all modern approximations on production web-cache workloads.</p><hr><h3 id=a-large-scale-deployment-of-dctcp>A large-scale deployment of DCTCP<a hidden class=anchor aria-hidden=true href=#a-large-scale-deployment-of-dctcp>#</a></h3><p><em>Abhishek Dhamija, Balasubramanian Madhavan, Hechao Li, Jie Meng <em>et al.</em></em></p><p><strong>TL;DR</strong> — A rare, candid production report from Meta on deploying DCTCP at hyperscale, surfacing unexpected interactions with heterogeneous hardware, cross-traffic, and operational constraints.</p><hr><h3 id=brain-on-switch-towards-advanced-intelligent-network-data-plane-via-nn-driven-traffic-analysis-at-line-speed>Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed<a hidden class=anchor aria-hidden=true href=#brain-on-switch-towards-advanced-intelligent-network-data-plane-via-nn-driven-traffic-analysis-at-line-speed>#</a></h3><p><em>Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li 0002 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Executes neural-network inference for traffic classification directly in the programmable switch data plane at line rate, eliminating the round-trip to a CPU-based classifier.</p><hr><h3 id=horus-granular-in-network-task-scheduler-for-cloud-datacenters>Horus: Granular In-Network Task Scheduler for Cloud Datacenters<a hidden class=anchor aria-hidden=true href=#horus-granular-in-network-task-scheduler-for-cloud-datacenters>#</a></h3><p><em>Parham Yassini, Khaled Diab 0001, Saeed Mahloujifar, Mohamed Hefeeda</em></p><p><strong>TL;DR</strong> — Offloads microsecond-granularity task scheduling decisions into programmable switches, reducing load-balancing latency by orders of magnitude compared to software schedulers.</p><hr><h3 id=capa-an-architecture-for-operating-cluster-networks-with-high-availability>CAPA: An Architecture For Operating Cluster Networks With High Availability<a hidden class=anchor aria-hidden=true href=#capa-an-architecture-for-operating-cluster-networks-with-high-availability>#</a></h3><p><em>Bingzhe Liu, Colin Scott, Mukarram Tariq, Andrew D. Ferguson <em>et al.</em></em></p><p><strong>TL;DR</strong> — Google’s production architecture for maintaining five-nines availability in Jupiter-scale cluster networks, detailing how control-plane redundancy and fast failover are achieved in practice.</p><hr><h3 id=revisiting-congestion-control-for-lossless-ethernet>Revisiting Congestion Control for Lossless Ethernet<a hidden class=anchor aria-hidden=true href=#revisiting-congestion-control-for-lossless-ethernet>#</a></h3><p><em>Yiran Zhang, Qingkai Meng 0001, Chaolei Hu, Fengyuan Ren</em></p><p><strong>TL;DR</strong> — Identifies fundamental flaws in PFC-based lossless Ethernet that cause cascading head-of-line blocking and proposes a redesigned congestion control that avoids them.</p><hr><h3 id=sirius-composing-network-function-chains-into-p4-capable-edge-gateways>Sirius: Composing Network Function Chains into P4-Capable Edge Gateways<a hidden class=anchor aria-hidden=true href=#sirius-composing-network-function-chains-into-p4-capable-edge-gateways>#</a></h3><p><em>Jiaqi Gao, Jiamin Cao, Yifan Li, Mengqi Liu 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Alibaba’s production system for composing arbitrary NF chains into P4-programmed edge gateways, replacing a fleet of dedicated middleboxes and cutting per-packet processing cost significantly.</p><hr><h3 id=cassini-network-aware-job-scheduling-in-machine-learning-clusters>CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters<a hidden class=anchor aria-hidden=true href=#cassini-network-aware-job-scheduling-in-machine-learning-clusters>#</a></h3><p><em>Sudarsanan Rajasekaran, Manya Ghobadi, Aditya Akella</em></p><p><strong>TL;DR</strong> — Shows that ignoring network topology when scheduling ML training jobs causes severe collective-communication contention, and that topology-aware co-scheduling yields substantial throughput gains.</p><hr><h3 id=cloudcast-high-throughput-cost-aware-overlay-multicast-in-the-cloud>Cloudcast: High-Throughput, Cost-Aware Overlay Multicast in the Cloud<a hidden class=anchor aria-hidden=true href=#cloudcast-high-throughput-cost-aware-overlay-multicast-in-the-cloud>#</a></h3><p><em>Sarah Wooders, Shu Liu, Paras Jain 0001, Xiangxi Mo <em>et al.</em></em></p><p><strong>TL;DR</strong> — Berkeley/Penn system that builds overlay multicast trees across cloud regions optimized for egress cost, enabling high-throughput data dissemination at a fraction of unicast cloud egress fees.</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/mobisys-2024/><span class=title>« Prev</span>
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