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: 2403398
• 负责人: Hao Wang
• 金额: $ 30万
• 依托单位: Louisiana State University
• 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=2403398&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) 集群通过提供大量计算资源，有助于推动科学和工程研究。随着人工智能 (AI) 技术在气候建模、药物发现和物理模拟等各个领域的普及，这些系统变得越来越重要，从而显着扩大了对计算能力和数据管理的需求。然而，现有的 HPC 基础设施面临着作业等待时间延长和资源使用不理想的挑战，这主要是由于计算复杂性不断上升和资源需求不断增长。与传统 HPC 任务不同，AI 算法和模型表现出不同的资源需求，通常会导致 AI 任务的资源分配过多或不足。该项目旨在通过深入分析运行人工智能工作负载的 HPC 环境中的资源分配和利用率，弥合 HPC 资源配置和 AI 应用需求之间的差距。目标是通过有效地重新分配闲置资源来适应大规模人工智能任务，从而确定最小化资源浪费并减少作业队列长度的策略。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贡献有价值的工具和见解。研究结果将通过研究论文、技术报告、书籍章节、课程材料和教程广泛分享，增强高性能计算和人工智能领域的知识库，并支持促进科学进步、改善国民健康、繁荣和福利的更广泛目标，为国防事业做出贡献。该项目的重点是通过创新地利用闲置资源来加快人工智能作业处理并减少等待时间，从而提高 HPC 系统的效率和生产力。该研究围绕三个相互关联的主题进行构建，每个主题都解决 HPC 环境中资源利用和 AI 性能增强的关键方面。最初的主题对 HPC 系统中的闲置资源进行全面分析，旨在识别资源优化的模式和机会。基于所获得的见解，第二个主题探讨了安全、及时地收集各种类别的闲置资源的方法，确保这些资源可以在不影响系统稳定性或性能的情况下重新分配。第三个主题致力于制定战略，利用这些收获的资源显着提高人工智能应用成果，进而提高 HPC 运营的整体生产力。该项目将实施一个有形的 HPC 测试平台，配备真实世界的基准和工作负载以及这些主题调查。该测试平台将作为一个平台，对开发的算法和系统进行实证验证，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评估。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。