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CIF: Medium: Collaborative Research: Coded Computing for Large-Scale Machine Learning

CIF：媒介：协作研究：大规模机器学习的编码计算

基本信息

批准号：
1763702
负责人：
Georgios-Alex Dimakis
金额：
$ 29.95万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763702&HistoricalAwards=false
关键词：
CIF Medium Collaborative Research Coded

项目摘要

Deep learning models are breaking new ground in data science tasks including image recognition, automatic translation and autonomous driving. This is achieved by neural networks that can be hundreds of layers deep and involve hundreds of millions of parameters. Training such large models requires distributed computations, very long training times and expensive hardware. This project studies coding theoretic techniques that can accelerate distributed machine learning and allow training with cheaper commodity hardware. Beyond the development of theoretical foundations, this project develops new algorithms for providing fault tolerance over unreliable cloud infrastructure that can significantly reduce the cost of large-scale machine learning. The research outcomes of the project will be broadly disseminated and integrated into education. The specific focus of this research program is on mitigating the bottlenecks of distributed machine learning. Currently, scaling benefits are limited because of two reasons: first, communication is typically the bottleneck and second, straggler effects limit performance. Both problems can be mitigated using coding theoretic methods. This work proposes "coded computing", a transformative framework that combines coding theory with distributed computing to inject computational redundancy in a novel coded form. This framework is then used to develop three research thrusts: a) Coding for Linear Algebraic Computations b) Coding for Iterative Computations and c) Coding for General Distributed Computations. Each of the thrusts operates on a different layer of a machine learning pipeline but all rely on coding theoretic tools and distributed information processing.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.

深度学习模型正在图像识别、自动翻译和自动驾驶等数据科学任务中开辟新天地。这是通过深度可达数百层并涉及数亿个参数的神经网络来实现的。训练如此大的模型需要分布式计算、很长的训练时间和昂贵的硬件。该项目研究编码理论技术，可以加速分布式机器学习并允许使用更便宜的商用硬件进行训练。除了理论基础的发展之外，该项目还开发了新的算法，为不可靠的云基础设施提供容错能力，可以显着降低大规模机器学习的成本。该项目的研究成果将得到广泛传播并融入教育。该研究计划的具体重点是缓解分布式机器学习的瓶颈。目前，由于两个原因，扩展优势受到限制：首先，通信通常是瓶颈，其次，落后者效应限制了性能。使用编码理论方法可以缓解这两个问题。这项工作提出了“编码计算”，这是一种将编码理论与分布式计算相结合的变革框架，以一种新颖的编码形式注入计算冗余。然后，该框架用于开发三个研究重点：a）线性代数计算编码b）迭代计算编码和c）通用分布式计算编码。每个推动力都在机器学习管道的不同层上运行，但都依赖于编码理论工具和分布式信息处理。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。