Collaborative Research: Frameworks: Scalable Performance and Accuracy analysis for Distributed and Extreme-scale systems (SPADE)
协作研究:框架:分布式和超大规模系统的可扩展性能和准确性分析 (SPADE)
基本信息
- 批准号:2311709
- 负责人:
- 金额:$ 44.7万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-15 至 2027-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Advances in computer simulations have made scientific discoveries more accessible. However, with the evolution of computing technology, each new generation of hardware and software presents unique performance and reliability challenges. These challenges must be addressed to fully harness the potential of these evolving technologies. SPADE is a project aimed to tackle these issues head-on. At its core, SPADE builds on the PAPI performance monitoring library - a tool used by the High-Performance Computing (HPC) community for over two decades. SPADE aims to enhance this legacy by creating methods that can assess and improve performance and accuracy on a wide range of advanced and evolving hardware and software technologies. This endeavor is not just about improving computational science but also about fostering diversity and education of a new generation of application scientists, engineers, and computer scientists. By providing an understanding of, and the ability to, navigate the intricate details of emerging technologies in the computing realm, SPADE is directly contributing to the advancement of this field. This will also democratize access to HPC, allowing a more diverse range of researchers and institutions to contribute to scientific discovery. Moreover, as SPADE aims to improve the capabilities of computer simulations, it enhances the ability to tackle a broad range of challenges - from understanding climate change to drug discovery. In essence, beyond advancing the HPC field, SPADE intends delivering a real-world impact by unlocking the full potential of computational science.The SPADE project focuses on advancing the monitoring, optimization, evaluation, and decision-making capabilities for extreme-scale systems. These critical capabilities are pivotal for both the High-Performance Computing (HPC) community and the scientific applications community that leverage these systems. With the evolution of HPC resources toward extreme scale, there is an increasing need for integrated performance and accuracy analysis frameworks to understand and mitigate performance and reliability challenges. To meet these needs, SPADE aims to deliver software and application programming interfaces (APIs) that broaden support for heterogeneity and scalability across a diverse range of computing platforms, including emerging vendor technologies. The SPADE project intends to utilize the established PAPI performance monitoring library to address the demands of scientific and machine learning applications effectively. Specifically, SPADE's mission includes: (1) developing monitoring capabilities for innovative and advanced technologies across the hardware stack; (2) designing novel abstractions that encapsulate the internal behavior of software components and facilitate interoperability across the software stack; (3) implementing a new performance and accuracy analysis framework that capitalizes on the efficiency and flexibility of C++'s object-oriented nature; (4) integrating new analysis functionality with various software stack layers and scientific and machine learning applications; and (5) examining new accuracy vs. performance trade-offs introduced with low-precision floating-point types. In essence, SPADE facilitates innovations in cyberinfrastructure development by enabling efficient and comprehensive resource utilization of extreme-scale platforms.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.
计算机模拟的进步使科学发现变得更加容易。然而,随着计算技术的发展,每一代新一代的硬件和软件都带来了独特的性能和可靠性挑战。必须解决这些挑战,以充分利用这些不断发展的技术的潜力。 SPADE 是一个旨在正面解决这些问题的项目。 SPADE 的核心基于 PAPI 性能监控库,这是高性能计算 (HPC) 社区使用了二十多年的工具。 SPADE 旨在通过创建可以评估和提高各种先进和不断发展的硬件和软件技术的性能和准确性的方法来增强这一传统。这项努力不仅是为了提高计算科学,而且是为了促进新一代应用科学家、工程师和计算机科学家的多样性和教育。通过提供对计算领域新兴技术复杂细节的理解和驾驭能力,SPADE 直接为该领域的进步做出了贡献。这也将使 HPC 的使用变得民主化,让更多不同的研究人员和机构为科学发现做出贡献。此外,由于 SPADE 旨在提高计算机模拟的能力,因此它增强了应对从理解气候变化到药物发现等广泛挑战的能力。从本质上讲,除了推进 HPC 领域之外,SPADE 还打算通过释放计算科学的全部潜力来产生现实世界的影响。SPADE 项目专注于推进超大规模系统的监控、优化、评估和决策能力。这些关键功能对于高性能计算 (HPC) 社区和利用这些系统的科学应用程序社区都至关重要。随着 HPC 资源向极端规模发展,越来越需要集成性能和准确性分析框架来了解和缓解性能和可靠性挑战。为了满足这些需求,SPADE 旨在提供软件和应用程序编程接口 (API),以扩大对各种计算平台(包括新兴供应商技术)的异构性和可扩展性的支持。 SPADE项目打算利用已建立的PAPI性能监控库来有效满足科学和机器学习应用程序的需求。具体来说,SPADE的使命包括:(1)开发跨硬件堆栈的创新和先进技术的监控能力; (2) 设计新颖的抽象,封装软件组件的内部行为并促进整个软件堆栈的互操作性; (3) 实现一个新的性能和准确性分析框架,该框架利用了 C++ 面向对象特性的效率和灵活性; (4) 将新的分析功能与各种软件堆栈层以及科学和机器学习应用程序集成; (5) 检查低精度浮点类型引入的新精度与性能权衡。从本质上讲,SPADE 通过实现超大规模平台的高效和综合资源利用来促进网络基础设施开发的创新。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Vincent Weaver其他文献
Vincent Weaver的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Vincent Weaver', 18)}}的其他基金
SI2-SSI: Collaborative Proposal: Performance Application Programming Interface for Extreme-Scale Environments (PAPI-EX)
SI2-SSI:协作提案:极端规模环境的性能应用程序编程接口 (PAPI-EX)
- 批准号:
1450122 - 财政年份:2015
- 资助金额:
$ 44.7万 - 项目类别:
Standard Grant
相似国自然基金
基于自复位混合阻尼实现钢框架综合韧性提升的多性态地震响应机理与设计调控方法研究
- 批准号:52378182
- 批准年份:2023
- 资助金额:50 万元
- 项目类别:面上项目
协同控制支撑钢框架自复位与耗能协调机理和韧性优化方法研究
- 批准号:52308195
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
P-S-Se三元共价框架结构设计及在锂硫电池中的无穿梭效应储锂机理研究
- 批准号:22379114
- 批准年份:2023
- 资助金额:50 万元
- 项目类别:面上项目
选择性分离水产品中全氟辛酸的金属有机框架的设计制备及吸附机制研究
- 批准号:32302234
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
基于共价有机框架的噬菌体-光催化协同靶向抗菌策略用于顽固性细菌感染的研究
- 批准号:22378279
- 批准年份:2023
- 资助金额:50 万元
- 项目类别:面上项目
相似海外基金
Collaborative Research: Scalable Manufacturing of Large-Area Thin Films of Metal-Organic Frameworks for Separations Applications
合作研究:用于分离应用的大面积金属有机框架薄膜的可扩展制造
- 批准号:
2326714 - 财政年份:2024
- 资助金额:
$ 44.7万 - 项目类别:
Standard Grant
Collaborative Research: AF: Small: Structural Graph Algorithms via General Frameworks
合作研究:AF:小型:通过通用框架的结构图算法
- 批准号:
2347321 - 财政年份:2024
- 资助金额:
$ 44.7万 - 项目类别:
Standard Grant
Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
- 批准号:
2411294 - 财政年份:2024
- 资助金额:
$ 44.7万 - 项目类别:
Standard Grant
Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
- 批准号:
2411298 - 财政年份:2024
- 资助金额:
$ 44.7万 - 项目类别:
Standard Grant
Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
- 批准号:
2411296 - 财政年份:2024
- 资助金额:
$ 44.7万 - 项目类别:
Standard Grant