CAREER: Automated and Efficient Machine Learning as a Service

职业：自动化高效的机器学习即服务

• 批准号: 2305491
• 负责人: Feng Yan
• 金额: $ 51.75万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305491&HistoricalAwards=false
• 关键词: CAREER; Automated; Efficient; Machine; Learning

Machine-Learning-as-a-Service (MLaaS) is an emerging computing paradigm that provides optimized execution of machine learning tasks, such as model design, model training, and model serving, on cloud infrastructure. Explosive growth in model complexity and data size along with the surging demands of MLaaS is already resulting in substantial increases in computational resource and energy requirements. Unfortunately, existing MLaaS systems have poor resource management and limited support for user specified performance and cost requirements, exacerbating waste in computing resources and energy. This project aims to utilize the unique features of MLaaS to design efficient, automated, and user-centric MLaaS systems. This approach will significantly reduce resource waste and shorten the model design cycles through a variety of novel optimization approaches and by eliminating candidate models that fail to meet model serving latency and target accuracy. To support complete MLaaS workflow, this project will also develop MLaaS model serving methodologies that can meet service level latency requirements with minimum resource consumption using intelligent autoscaling.This project has the potential to tremendously reduce the resource and energy consumptions as well as the carbon footprint associated with the fast-growing societal demands in machine learning and cloud computing. Important insights and technologies will be produced targeting resource management and energy saving of the next-generation machine learning systems and cloud infrastructure. The findings of this project will also contribute to related fields of parallel and distributed systems, performance evaluation and optimization, and green computing. This project will carry out substantial integrated education activities including new course and online education development, integration of industry feedback in education. Additionally, the work will impact undergraduate and graduate students by training them in the art of system optimization combined with the latest machine learning domain knowledge while combining outreach and engagement of students from underrepresented groups and especially women.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.

机器学习-AS-A-Service（MLAAS）是一种新兴的计算范式，可在云基础架构上优化机器学习任务的执行，例如模型设计，模型培训和模型服务。模型复杂性和数据大小的爆炸性增长以及MLAA的飙升需求已经导致计算资源和能源需求的大幅增加。不幸的是，现有的MLAAS系统的资源管理差，对用户指定的性能和成本要求的支持有限，加剧了计算资源和能源中的废物。该项目旨在利用MLAA的独特功能来设计高效，自动化和以用户为中心的MLAAS系统。这种方法将通过各种新型优化方法以及消除无法满足延迟和目标准确性的模型的候选模型来大大减少资源浪费并缩短模型设计周期。为了支持完整的MLAA工作流程，该项目还将开发MLAAS模型服务方法，这些方法可以满足服务水平的潜伏需求，并使用智能自动化使用智能自动化。该项目有可能极大地减少与机器学习和云计算中快速成长的社会需求相关的碳足迹以及碳足迹。重要的见解和技术将是针对下一代机器学习系统和云基础架构的资源管理和能源节省的。该项目的发现还将有助于并行和分布式系统的相关字段，性能评估和优化以及绿色计算。该项目将开展大量的综合教育活动，包括新课程和在线教育发展，行业反馈在教育中的融合。 此外，这项工作将通过对系统优化的艺术进行培训以及最新的机器学习领域知识的培训，同时将来自代表性不足的群体，尤其是女性的学生的参与度结合在一起，从而影响本科生和研究生。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是值得的。

项目成果

A Generic, High-Performance, Compression-Aware Framework for Data Parallel DNN Training

用于数据并行 DNN 训练的通用、高性能、压缩感知框架

• DOI: 10.1109/tpds.2023.3266246
• 发表时间: 2024
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Wu, Hao;Wang, Shiyi;Bai, Youhui;Li, Cheng;Zhou, Quan;Yi, Jun;Yan, Feng;Chen, Ruichuan;Xu, Yinlong
• 通讯作者: Xu, Yinlong

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

NASRec：推荐系统的权重共享神经架构搜索

• DOI: 10.1145/3543507.3583446
• 发表时间: 2023
• 期刊: the ACM Web Conference 2023
• 作者: Zhang, Tunhou;Cheng, Dehua;He, Yuchen;Chen, Zhengxing;Dai, Xiaoliang;Xiong, Liang;Yan, Feng;Li, Hai;Chen, Yiran;Wen, Wei
• 通讯作者: Wen, Wei

: Joint Point Interaction-Dimension Search for 3D Point Cloud

• DOI: 10.1109/wacv56688.2023.00135
• 发表时间: 2022-11
• 期刊: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
• 作者: Tunhou Zhang;Mingyuan Ma;Feng Yan;H. Li;Yiran Chen

Noctua: Towards Practical and Automated Fine-grained Consistency Analysis

Noctua：迈向实用且自动化的细粒度一致性分析

• 发表时间: 2024
• 期刊: European Conference on Computer Systems
• 作者: Ma, Kai;Li, Cheng;Zhu, Enzuo;Chen, Ruichuan;Yan, Feng;Chen, Kang
• 通讯作者: Chen, Kang

ZeRO++: Extremely Efficient Collective Communication for Large Model Training

• 作者: Guanhua Wang;Heyang Qin;Sam Ade Jacobs;Xiaoxia Wu;Connor Holmes;Zhewei Yao;Samyam Rajbhandari;Olatunji Ruwase;Feng Yan;Lei Yang;Yuxiong He Microsoft