Collaborative Research: CNS Core: Small: Optimizing Large-Scale Heterogeneous ML Platforms

大规模人工智能和机器学习（AI/ML）平台在当前的数据革命中发挥着至关重要的作用。为了最大限度地减少用户的工作量，需要一种端到端的解决方案来在可能异构的计算集群上部署复杂的工作流。然而，这种“按钮式”部署背后的调度和资源管理问题是具有挑战性的。 如果不加以解决，这些昂贵的系统将严重利用不足，导致不必要的电力消耗和温室气体排放。该项目将为分布式大规模AI/ML系统开发有效的资源分配策略，以应对挑战。具体来说，该项目将通过分布式优化来加速和并行化在AI/ML平台中占主导地位的大规模优化和推理任务，为异构环境中的落伍者提供容错和鲁棒性。在分布式优化的基础上，该项目将进一步调度AI/ML工作流，子任务之间具有优先约束。最后，在分配的资源可交换的情况下，异构资源在作业之间公平有效地分配，这对于具有图形处理单元（GPU）和其他加速器的AI/ML平台来说是关键。该项目将为学术界和工业界使用的AI/ML平台中的调度和资源分配提供新的基础算法。算法思想将在核心经典模型的背景下开发，因此将比AI/ML平台应用更广泛，例如，到网络、存储、供应链管理等等。该项目将寻求扩大科学，技术，工程和数学方面代表性不足的群体的参与，计划开展的活动包括为中学生开发加速数学课程，为初中和高中学生开发暑期课程，为本科生开发暑期研究课程。该项目将使其软件工件，数据集，该https://adamwierman.com/optimizing-large-scale-heterogeneous-ml-platforms/奖项反映了NSF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。

Large-scale artificial intelligence and machine learning (AI/ML) platforms are playing a vital role in the current data revolution. To minimize efforts from users, an end-to-end solution is desired to deploy complex workflow over possibly heterogeneous computing clusters. However, the scheduling and resource management problems behind such “push-button” deployment are challenging. If left unsolved, these costly systems will be severely under-utilized, leading to unnecessary electricity consumption and greenhouse gas emissions. This project will develop efficient resource allocation policies for distributed, large-scale AI/ML systems to tackle the challenges. Specifically, this project will accelerate and parallelize the large-scale optimization and inference tasks that dominate workloads in AI/ML platforms via distributed optimization that provides fault tolerance and robustness to stragglers in heterogeneous settings. Built upon the distributed optimization, the project will further schedule AI/ML workflows with precedence constraints among sub-tasks. Finally, heterogeneous resources are allocated among jobs fairly and efficiently in the case where the resources being allocated are exchangeable, which is key for AI/ML platforms with graphic processing units (GPUs) and other accelerators. The project will provide new fundamental algorithms for scheduling and resource allocation in AI/ML platforms used across academia and industry. The algorithmic ideas will be developed in the context of core, classical models and so will apply more broadly than AI/ML platforms, e.g., to networking, storage, supply chain management, and beyond. The project will seek to broaden the participation of underrepresented groups in Science, Technology, Engineering and Mathematics by planned activities including the development of accelerated mathematics programs for middle school students, summer programs for middle-school and high-school students, and summer research programs for undergraduate students.The project will make its software artifacts, datasets, and research results available to the research community on the project website at https://adamwierman.com/optimizing-large-scale-heterogeneous-ml-platforms/ Artifacts will be maintained for a minimum of 10 years.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.