CNS Core: Small: Multi-Scale GPU Resource Management for AI Applications

随着从多个来源收集的文本、音频和视频等数据量不断增加，更好地了解收集的数据的需求也在增加。 近年来，大型数据集的分析已经发展成为复杂的人工智能（AI）技术。这是因为需求已经从人类分析数据扩展到使机器能够自己理解数据。该项目的总体目标是使此类AI应用（特别是深度学习）变得更高效。图形处理单元（GPU）通常用于上述AI应用中。 该项目解决了现代GPU资源管理的基本限制。为此，该项目计划采取整体方法，重点关注三个领域：（1）细粒度共享单个GPU;（2）粗粒度共享GPU集群;（3）动态重新调整以减轻通信对分布式深度学习的影响。其核心技术包括时间调度和空间资源分配与部分或没有知识的作业持续时间或工作负载特性。作为该项目的一部分设计的算法将具有不仅仅在GPU集群上运行AI应用程序的应用程序。提高GPU效率将有助于降低使用AI的成本，从而导致深度学习技术的广泛使用。这将使人工智能在增强/虚拟现实和实时交互式视频分析等新兴领域的新应用成为可能，同时使其更具成本效益。该项目包括计划与业界合作，将研究转化为实践，并将其成果纳入研究生/本科生课程。最后，它将建立在密歇根大学已经建立的外展活动的基础上，以帮助更好地向不同的学生群体和公众传达人工智能对社会的影响。为该项目生成和收集的所有代码和数据，包括软件系统、模拟器和仿真器，将作为开源资源在www.example.com上向公众提供。该奖项反映了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。

As an increasing amount of data, such as text, audio, and video, are collected from many sources, the need to better understand the collected data is increasing as well. The analysis of large datasets has evolved into complex artificial intelligence (AI) techniques in recent years. This is because the need has expanded from just a human analyzing the data to enabling a machine to make sense of it on its own. The overarching goal of this project is to enable such AI applications - specifically, deep learning - to become more efficient.Graphics Processing Units (GPUs) are often used in AI applications like those mentioned above. This project addresses the fundamental limitations in resource management in modern GPUs. To this end, this project plans to take a holistic approach with three broad focus areas: (1) fine-grained sharing of individual GPUs; (2) coarse-grained sharing of a GPU cluster; and (3) dynamic readjustments to mitigate the impact of communication on distributed deep learning. The core techniques include temporal scheduling and spatial resource allocation with partial or no knowledge of job durations or workload characteristics. Algorithms designed as part of this project will have applications beyond simply running AI applications on GPU clusters. Increasing GPU efficiency will help reduce the cost of using AI, leading to pervasive use of deep learning techniques. This will enable new applications of AI in emerging domains such as augmented/virtual reality and real-time interactive video analytics, while making them more cost-effective. The project includes plans to work with industry to translate the research into practice and to include its outcomes in graduate/undergraduate curricula. Lastly, it will build upon already-established outreach activities at the University of Michigan to help better convey the impact of AI on society to diverse student population groups and the general public. All code and data generated and collected for this project, including software systems, simulators, and emulators, will be made available to the public as open-source resources at https://github.com/symbioticlab. They will be will be retained for at least the duration of the project.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.