Category II: ACES - Accelerating Computing for Emerging Sciences

类别 II：ACES - 加速新兴科学的计算

• 批准号: 2112356
• 负责人: Honggao Liu
• 金额: $ 500万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112356&HistoricalAwards=false
• 关键词: Category; II; ACES; Accelerating; Computing

The ever-growing complexity of Science and Engineering (S&E) workflows and expectations of Open Science have encouraged researchers to adopt new technologies, such as containerization, virtualization and composability, that enable them to respond to an increasingly complex cyberinfrastructure (CI) landscape while producing shareable, and reproducible results. ACES (Accelerating Computing for Emerging Sciences), an innovative advanced computational prototype to be developed by Texas A&M University, tries to answer a fundamental question: how does one effectively offer a holistic computing platform that can simultaneously meet the needs of a continuum of users in diverse research communities with varying levels of computing adoption? The project will allow researchers to creatively develop new programming models and workflows that utilize these architectures while simultaneously advancing HPC (High Performance Computing) and data science projects.The ACES platform removes significant bottlenecks in advanced computing by introducing the flexibility to aggregate various components (i.e., processors, accelerators and memory) on an as-needed basis to solve problems that were previously not addressable. By letting researchers switch and run on accelerators best suited for their workflows, ACES will benefit many research and development projects in the fields of artificial intelligence and machine learning (AI/ML), cybersecurity, health population informatics, genomics and bioinformatics, human and agricultural life sciences, oil & gas simulations, de novo materials design, climate modeling, molecular dynamics, quantum computing architectures, imaging, smart and connected societies, geosciences, and quantum chemistry. Toward facilitating researcher use, ACES will offer avenues for interactive computing, portals, and cloud connectivity. ACES will support the national research community through coordination systems supported by the National Science Foundation (NSF). Finally, ACES will also leverage existing efforts that promote science and broaden participation in computing at the K-12, collegiate, and professional levels to have a transformative impact nationally by focusing on training, education and outreach. ACES activities are designed to expand the participation of traditionally underrepresented groups in computing and STEM (Science, Technology, Engineering and Mathematics), particularly at minority-serving institutions. ACES will offer fellowships to students, continue efforts to support teacher programs, and offer a number of formal and informal courses, whose materials will be offered to the national community for use free-of-charge.This project funds the development of a dynamically composable high-performance data analysis and computing platform, named ACES. AI and ML are integrated with traditional simulation and modeling approaches in the pursuit of innovation. Edge-computing and instrumental probes have pushed the need to verify, process, store, analyze, and query vast amounts of unstructured data in real time. The coupling of analytics with closely-situated data on highly-usable web-based technologies connected to a compute backend have led to a paradigm shift in expectations from research computing environments. The ACES innovative composable hardware platform helps accelerate transformative changes in research areas that can leverage novel High Bandwidth Memory (HBM) processors and accelerators for analytics and computing. ACES leverages Liqid’s composable framework via PCIe (Peripheral Component Interconnect express) Gen5 on Intel’s HBM Sapphire Rapid processors to offer a rich accelerator testbed consisting of Intel Ponte Vecchio GPUs (Graphics Processing Units), Intel FPGAs (Field Programmable Gate Arrays), NEC Vector Engines, NextSilicon co-processors, Graphcore IPUs (Intelligence Processing Units). The accelerators are coupled with Intel Optane memory and DDN Lustre storage interconnected with Mellanox NDR 400Gbps (gigabit-per-second) InfiniBand to support workflows that benefit from optimized devices. ACES will enable applications and workflows to dynamically integrate the different accelerators, memory, and in-network computing protocols to glean new insights by rapidly processing large volumes of data, and provide researchers with a unique platform to produce complex hybrid programming models that effectively supports calculations that were not feasible before.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.

科学与工程学（S＆E）工作流以及开放科学的期望不断增长的复杂性，鼓励研究人员采用新技术，例如容器化，虚拟化和合成性，使他们能够对日益复杂的Cyber​​infratucture（CI）景观做出反应，同时产生可产生可再现的结果。 ACE（加速新兴科学的计算），这是由德克萨斯A＆M大学开发的创新的先进计算原型，试图回答一个基本问题：如何有效地提供一个可以简单地满足采用量化计算水平的潜水员研究社区中持续的用户的整体计算平台？该项目将允许研究人员创造性地开发新的编程模型和工作流，这些模型和工作流程利用这些体系结构，同时推进HPC（高性能计算）和数据科学项目。ACES平台通过引入各种组件（即处理器，加速器和存储器）的灵活性来消除高级计算中的重要瓶颈。 By letting researchers switch and run on accelerators best suited for their workflows, ACES will benefit many research and development projects in the fields of artificial intelligence and machine learning (AI/ML), cybersecurity, health population informatives, genomics and bioinformatics, human and agricultural life sciences, oil & gas simulations, de novo materials design, climate modeling, molecular dynamics, quantum computing architectures, imaging,聪明和连接的社会，地球科学和量子化学。为了促进研究人员的使用，ACE将为交互式计算，门户和云连接提供途径。 ACE将通过国家科学基金会（NSF）支持的协调系统来支持国家研究界。最后，ACE还将利用现有的努力来促进科学并扩大K-12，大学和专业水平的计算参与，从而通过专注于培训，教育和宣传来在全国范围内产生变革性的影响。 ACES活动旨在扩大传统代表性不足的群体参与计算和STEM（科学，技术，工程和数学），尤其是在少数族裔服务机构中。 ACE将向学生提供奖学金，继续努力支持教师课程，并提供许多正式和非正式的课程，其材料将提供给国家社区免费使用。该项目资助了动态组合的高性能数据分析和计算平台的开发。 AI和ML与传统的模拟和建模方法集成在一起，以追求创新。边缘计算和工具性问题推动了实时验证，处理，存储，分析和查询大量非结构化数据的需求。分析的耦合以及连接到计算后端的高度可用Web的技术的紧密固定数据导致了研究计算环境的期望的范式转移。 ACES创新的组合硬件平台有助于加速研究领域的变革性变化，这些变化可以利用新颖的高带宽内存（HBM）处理器和加速器来进行分析和计算。 Aces通过PCIE（外围组件互连Express）Gen5在Intel的HBM Sapphire快速处理器上利用Liqid的合并框架，以提供由Intel Ponte Vecchio GPUS组成的丰富加速器，由Intel Ponte Vecchio GPU（图形处理单位）组成IPU（智能处理单元）。加速器与Intel Optane内存和DDN光泽存储与Mellanox NDR 400GBPS（每秒千兆位）Infiniband相互连接，以支持从优化设备中受益的工作流。 ACE将启用应用和工作流程，以动态整合不同的加速器，记忆和网络内计算协议，通过快速处理大量数据来收集新的见解，并为研究人员提供一个独特的平台，以产生复杂的混合编程模型，从而可以通过诚实地支持NSF的宣传来支持该奖项，从而通过nsf的宣传来支持，并支持了nsf的宣传。优点和更广泛的影响审查标准。

项目成果

Scaling Study of Flow Simulations on Composable Cyberinfrastructure

可组合网络基础设施流模拟的规模化研究

• DOI: 10.1145/3569951.3597565
• 发表时间: 2023
• 期刊: USA
• 作者: Mishra, Sambit;Witherden, Freddie;Chakravorty, Dhruva;Perez, Lisa;Dang, Francis
• 通讯作者: Dang, Francis

Porting AI/ML Models to Intelligence Processing Units (IPUs)

• DOI: 10.1145/3569951.3603632
• 发表时间: 2023-07
• 期刊: Practice and Experience in Advanced Research Computing
• 作者: Abhinand Nasari;Lujun Zhai;Zhenhua He;Hieu Hanh Le;S. Cui;Dhruva K. Chakravorty;Jian Tao;Honggao Liu

Benchmarking the Performance of Accelerators on National Cyberinfrastructure Resources for Artificial Intelligence / Machine Learning Workloads

• DOI: 10.1145/3491418.3530772
• 发表时间: 2022-07
• 期刊: Practice and Experience in Advanced Research Computing
• 作者: Abhinand Nasari;Hieu Hanh Le;Richard Lawrence;Zhenhua He;Xin Yang;Mario Krell;A. Tsyplikhin;M. Tatineni;Tim Cockerill;Lisa M. Perez;Dhruva K. Chakravorty;Honggao Liu

Performance of Distributed Deep Learning Workloads on a Composable Cyberinfrastructure

可组合网络基础设施上分布式深度学习工作负载的性能

• DOI: 10.1145/3569951.3593601
• 发表时间: 2023
• 期刊: USA.
• 作者: He, Zhenhua;Saluja, Aditi;Lawrence, Richard;Chakravorty, Dhruva;Dang, Francis;Perez, Lisa;Liu, Honggao
• 通讯作者: Liu, Honggao

Extending Functionalities on a Web-based Portal for Research Computing

扩展基于 Web 的研究计算门户的功能

• 发表时间: 2022
• 期刊: PEARC '22: Practice and Experience in Advanced Research Computing
• 作者: Duy Pham, Kyle Hsu