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

超级计算机或高性能计算（HPC）集群通过提供大量计算资源，在推动科学和工程研究方面发挥着重要作用。随着人工智能（AI）技术在各个领域（包括气候建模、药物发现和物理模拟）的普及，这些系统变得越来越重要，大大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的不断升级和对资源的需求不断增长。与传统的高性能计算任务不同，人工智能算法和模型表现出不同的资源需求，通常导致人工智能任务的资源分配过剩或不足。该项目旨在通过深入分析运行AI工作负载的HPC环境中的资源分配和利用，弥合HPC资源供应与AI应用需求之间的差距。目标是通过有效地重新分配空闲资源以适应大规模人工智能任务，确定最小化资源浪费和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，该计划将为研究界提供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共享，增强高性能计算和人工智能领域的知识库，支持促进科学进步、改善国民健康、繁荣和福利以及为国防做出贡献的更广泛目标。该项目旨在通过创新地利用闲置资源来加快人工智能作业处理并缩短等待时间，从而提高高性能计算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，每个主题都涉及高性能计算环境中资源利用和人工智能性能增强的关键方面。最初的主题是对高性能计算系统中的空闲资源进行全面分析，旨在确定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空闲资源的方法，确保可以在不影响系统稳定性或性能的情况下重新分配这些资源。第三个主题致力于制定战略，利用这些收获的资源显著提高人工智能应用成果，并进一步提高高性能计算操作的整体生产力。该项目将实施一个有形的高性能计算测试平台，配备真实世界的基准和工作负载以及这些主题调查。该测试平台将作为经验验证已开发算法和系统的平台，促进对其在改进高性能计算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。

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.