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

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。这是由数百层深度的神经网络实现的，涉及数亿个参数。训练如此庞大的模型需要分布式计算、非常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品硬件进行训练。除了理论基础的发展之外，该项目还开发了新的算法，用于在不可靠的云基础设施上提供容错，从而大大降低大规模机器学习的成本。该项目的研究成果将广泛传播并融入教育。这个研究项目的重点是减轻分布式机器学习的瓶颈。目前，由于两个原因，扩展效益受到限制：第一，通信是典型的瓶颈；第二，离散效应限制了性能。这两个问题都可以通过编码理论方法得到缓解。这项工作提出了“编码计算”，这是一个将编码理论与分布式计算相结合的变革性框架，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；c)通用分布式计算编码。每个推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。

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.