Category I: Anvil - A National Composable Advanced Computational Resource for the Future of Science and Engineering

第一类：Anvil - 面向科学与工程未来的国家级可组合高级计算资源

• 批准号: 2005632
• 负责人: Xiaohui Carol Song
• 金额: $ 995.22万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005632&HistoricalAwards=false
• 关键词: Category; Anvil; National; Composable; Advanced

As computing permeates nearly all fields of science and engineering, there is an exponential growth of computing needs from both the traditional computing-intensive domains and the emerging new and more diverse fields of research. The rise of machine learning and artificial intelligence applications has accelerated and broadened the use of computational resources from research in creating new and more environmentally friendly materials to improving medicine in our fight against deadly diseases. There are three main challenges to meeting this rapidly evolving landscape of national computational needs: a shortage of capacity, increasingly diverse applications, and computational literacy and training. This project aims to meet these challenges and transform the way computing is delivered by developing and deploying a composable advanced computing resource, Anvil, to the national research community to significantly increase both the computing capacity and accessibility. Anvil integrates a large-capacity high-performance computing (HPC) cluster with a comprehensive ecosystem of software, access interfaces, programming environments, and composable services to form a seamless environment able to support a broad range of current and future science and engineering applications. Through a carefully designed student training program and partnerships with regional and other universities, XSEDE, and Women in HPC programs, this project will develop computing competency in the next-generation workforce, and engage and train a broader audience including underrepresented students at minority-serving and EPSCoR (Established Program to Stimulate Competitive Research) institutions.Built with a forward-looking architecture with a high core count, and improved memory bandwidth and I/O, Anvil can effectively support traditional HPC with fast turnaround for high throughput, mid-scale computation jobs. Anvil consists of 1000 128-core computing nodes based on the next-generation AMD Epyc “Milan" architecture that can deliver a total peak performance of 5.3 Petaflops. Each node has 256 GB of memory, and a 100 gigabits/second bandwidth from the Mellanox HDR InfiniBand interconnect, allowing multiple jobs of up to 1024 cores to be run at full speed over the interconnect fabric. These nodes are complemented by 32 large-memory nodes with 1 TB of RAM each, and 16 Nvidia GPU nodes with 4 “Volta Next” GPUs per node. The GPU nodes are capable of 1.57 petaflops of single-precision performance to support machine learning and a wide range of current and future science and engineering applications. Anvil’s multiple tiers of storage systems include a long-term archive, persistent file and campaign storage, a 10 PB scratch file system, a 3 PB flash burst buffer, and object storage to support a variety of workflows and storage needs. Anvil will lower the barrier to entry to advanced computing CI by providing interactive computing and desktop environments that ease the transition for users from diverse domains new to HPC. By providing feature-rich interactive environments such as Open OnDemand and ThinLinc, users can rapidly become productive on Anvil through Linux and Windows desktops, or familiar tools through their browser (e.g., Jupyter, RStudio). Complex scientific software environments and application stacks will be supported via containers orchestrated within a powerful composable subsystem. Anvil supports cloud-bursting of computational workloads as well as use of public cloud machine learning platforms including GPU and FPGA accelerators and software tools to automate hyperparameter tuning and algorithm selection for exploratory ML research. An existing production-quality science gateway at Purdue will support XSEDE researchers to share their data and tools online and facilitate easy access to Anvil and other XSEDE resources in classroom instruction and training activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着计算渗透到几乎所有的科学和工程领域，无论是传统的计算密集型领域还是新兴的、更多样化的研究领域，计算需求都呈指数级增长。机器学习和人工智能应用的兴起加速并扩大了计算资源的使用，从研究创造新的更环保的材料到改善我们对抗致命疾病的医学。要满足这种迅速发展的国家计算需求，有三个主要挑战：能力短缺、应用日益多样化以及计算素养和培训。该项目旨在应对这些挑战，并通过开发和部署可组合的先进计算资源Anvil来改变计算的交付方式，以显著提高计算能力和可访问性。Anvil将大容量高性能计算（HPC）集群与软件、访问接口、编程环境和可组合服务的综合生态系统集成在一起，形成一个无缝的环境，能够支持广泛的当前和未来的科学和工程应用。通过精心设计的学生培训计划以及与地区和其他大学、XSEDE和HPC项目中的女性合作，该项目将培养下一代劳动力的计算能力，并吸引和培训更广泛的受众，包括少数族裔服务机构和EPSCoR（建立计划以刺激竞争性研究）机构中代表性不足的学生。Anvil采用具有前瞻性的架构，具有高核数，改进的内存带宽和I/O，可以有效地支持传统的高性能计算，具有快速周转的高吞吐量，中等规模的计算任务。Anvil由1000个基于下一代AMD Epyc“米兰”架构的128核计算节点组成，可提供5.3千万亿次的总峰值性能。每个节点具有256gb内存，以及来自Mellanox HDR InfiniBand互连的100gb /秒带宽，允许在互连结构上全速运行多达1024个内核的多个作业。这些节点有32个大内存节点，每个节点有1tb的RAM， 16个Nvidia GPU节点，每个节点有4个“Volta Next”GPU。GPU节点具有每秒1.57千万亿次的单精度性能，可支持机器学习以及广泛的当前和未来科学与工程应用。Anvil的多层存储系统包括长期存档、持久文件和活动存储、10pb临时文件系统、3pb闪存突发缓冲区和对象存储，以支持各种工作流程和存储需求。Anvil将通过提供交互式计算和桌面环境来降低进入高级计算CI的门槛，从而使不同领域的用户轻松过渡到高性能计算。通过提供功能丰富的交互环境，如Open OnDemand和ThinLinc，用户可以通过Linux和Windows桌面，或通过浏览器（如Jupyter， RStudio）熟悉的工具，在Anvil上快速提高生产力。复杂的科学软件环境和应用程序栈将通过在强大的可组合子系统中编排的容器来支持。Anvil支持云爆发计算工作负载，以及使用公共云机器学习平台，包括GPU和FPGA加速器和软件工具，以自动进行超参数调优和算法选择，以进行探索性机器学习研究。普渡大学现有的生产质量科学门户将支持XSEDE研究人员在线共享他们的数据和工具，并促进在课堂教学和培训活动中轻松访问Anvil和其他XSEDE资源。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Understanding Factors that Influence Research Computing and Data Careers

了解影响研究计算和数据职业的因素

• DOI: 10.1145/3491418.3530292
• 发表时间: 2022
• 期刊: Practice and Experience in Advanced Research Computing (PEARC22
• 作者: Chaudhry, Shafaq;Pazouki, Arman;Schmitz, Patrick;Hillery, Elizabett;Kee, Kerk
• 通讯作者: Kee, Kerk

Defining Performance of Scientific Application Workloads on the AMD Milan Platform

定义 AMD Milan 平台上科学应用程序工作负载的性能

• DOI: 10.1145/3437359.3465596
• 发表时间: 2021
• 期刊: Practice and Experience in Advanced Research Computing
• 作者: Wu, Tsai-Wei;Lien Harrell, Stephen;Lentner, Geoffrey;Younts, Alex;Weekly, Sam;Mertes, Zoey;Maji, Amiya;Smith, Preston;Zhu, Xiao
• 通讯作者: Zhu, Xiao

Anvil - System Architecture and Experiences from Deployment and Early User Operations

Anvil - 系统架构以及部署和早期用户运营的经验

• DOI: 10.1145/3491418.3530766
• 发表时间: 2022
• 期刊: PEARC '22: Practice and Experience in Advanced Research Computing
• 作者: Song, X. Carol;Smith, Preston;Kalyanam, Rajesh;Zhu, Xiao;Adams, Eric;Colby, Kevin;Finnegan, Patrick;Gough, Erik;Hillery, Elizabett;Irvine, Rick
• 通讯作者: Irvine, Rick

Cyberinfrastructure for sustainability sciences

可持续科学的网络基础设施

• DOI: 10.1088/1748-9326/acd9dd
• 发表时间: 2023
• 期刊: Environmental Research Letters
• 影响因子: 6.7
• 作者: Song, Carol X.;Merwade, Venkatesh;Wang, Shaowen;Witt, Michael;Kumar, Vipin;Irwin, Elena;Zhao, Lan;Walton, Amy
• 通讯作者: Walton, Amy