Collaborative Research: OAC: Core: Harvesting Idle Resources Safely and Timely for Large-scale AI Applications in High-Performance Computing Systems

合作研究：OAC：核心：安全及时地收集闲置资源，用于高性能计算系统中的大规模人工智能应用

• 批准号: 2403399
• 负责人: Seung-Jong Park
• 金额: $ 30万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2403399&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Harvesting

Supercomputers, or high-performance computing (HPC) clusters, are instrumental in propelling scientific and engineering research by offering vast computational resources. These systems are increasingly crucial as artificial intelligence (AI) techniques become pervasive across various fields, including climate modeling, drug discovery, and physics simulations, significantly expanding the need for computational power and data management. However, the existing HPC infrastructures face challenges with extended job wait times and suboptimal resource use, primarily due to the escalating complexity of computations and the burgeoning demands for resources. Unlike traditional HPC tasks, AI algorithms and models exhibit distinct resource requirements, often resulting in either excess or insufficient resource allocation for AI tasks. This project aims to bridge the gap between HPC resource provisioning and AI application demands through an in-depth analysis of resource allocation and utilization within HPC environments running AI workloads. The goal is to identify strategies for minimizing resource waste and reducing the length of job queues by efficiently reallocating idle resources to accommodate large-scale AI tasks. By creating and disseminating datasets, models, algorithms, and system source code, this initiative will contribute valuable tools and insights to the research community. The findings will be broadly shared through research papers, technical reports, book chapters, course materials, and tutorials, enhancing the knowledge base in both HPC and AI fields and supporting the broader objectives of promoting scientific progress, improving national health, prosperity, and welfare, and contributing to national defense. This project centers on advancing the efficiency and productivity of HPC systems by innovatively leveraging idle resources to expedite AI job processing and diminish waiting periods. The research is structured around three interconnected themes, each addressing critical aspects of resource utilization and AI performance enhancement within HPC environments. The initial theme undertakes a comprehensive analysis of idle resources in HPC systems, aiming to identify patterns and opportunities for resource optimization. Building on the insights gained, the second theme explores methodologies for the safe and timely harvesting of idle resources across various categories, ensuring that these resources can be reallocated without compromising system stability or performance. The third theme is dedicated to developing strategies that utilize these harvested resources to boost AI application outcomes significantly and, by extension, enhance the overall productivity of HPC operations. The project will implement a tangible HPC testbed equipped with real-world benchmarks and workloads alongside these thematic investigations. This testbed will serve as a platform for empirically validating developed algorithms and systems, facilitating a rigorous assessment of their effectiveness in improving HPC resource allocation and utilization.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.

超级计算机或高性能计算（HPC）集群，通过提供大量的计算资源来推动科学和工程研究。随着人工智能（AI）技术在各个领域的普及，包括气候建模，药物发现和物理模拟，大大扩大了计算能力和数据管理的需求，这些系统越来越至关重要。但是，现有的HPC基础架构通过延长的工作等待时间和次优的资源使用面临挑战，这主要是由于计算的复杂性和对资源的新兴需求的升级。与传统的HPC任务不同，AI算法和模型表现出不同的资源要求，通常导致AI任务的过量或不足资源分配。该项目旨在通过对运行AI工作负载的HPC环境中资源分配和利用的深入分析来弥合HPC资源提供和AI应用程序之间的差距。目的是确定最大程度地减少资源浪费的策略，并通过有效地重新分配空闲资源以适应大规模的AI任务，从而减少工作队列的长度。通过创建和传播数据集，模型，算法和系统源代码，该计划将为研究社区提供宝贵的工具和见解。这些发现将通过研究论文，技术报告，书籍章节，课程材料和教程大致分享，增强了HPC和AI领域的知识基础，并支持促进科学进步，改善国家健康，繁荣和福利的更广泛的目标，并为国防而做出贡献。该项目以创新的利用闲置资源来加快AI工作处理并减少等待时间来提高HPC系统的效率和生产率。该研究围绕三个相互联系的主题进行了结构，每个主题都涉及资源利用的关键方面和HPC环境中AI性能的提高。最初的主题对HPC系统中的空闲资源进行了全面分析，旨在确定资源优化的模式和机会。第二个主题以洞察力为基础，探讨了在各个类别中安全和及时收集闲置资源的方法，以确保可以将这些资源重新分配而不会损害系统稳定性或性能。第三个主题致力于制定利用这些收获的资源来大大提高AI应用结果的策略，并扩展提高HPC运营的整体生产率。该项目将实施一个有形的HPC测试床，配备了现实世界的基准和工作量以及这些主题研究。该测试床将作为实证验证算法和系统的平台，促进对其在改善HPC资源分配和利用方面的有效性进行严格的评估。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来支持的，这是值得的。