融合编码与随机线性代数，加速分布式机器学习计算。
Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching
- 用编码冗余应对慢节点，结合随机压缩提升计算效率。
- 在存在慢速或失效服务器时，显著降低算法延迟。
- 适合大规模分布式系统中的优化与机器学习任务。
编码计算是一种利用编码理论引入冗余以克服大规模系统瓶颈的分布式范式。类似地，随机数值线性代数采用概率方法对线性代数运算进行压缩和加速，解决高维数据分析中的挑战。本文综述了这两个领域的基础，并提出了一种结合两者技术的分布式方案，可在存在慢速或非响应服务器的情况下加速优化与机器学习算法。文中还涉及多个相关主题与数学概念。
原文摘要 · Abstract (English)
Coded computing is a distributed paradigm that uses coding theory to introduce \textit{redundancy} and overcome bottlenecks in large-scale systems. In the same vein, randomized numerical linear algebra employs probabilistic methods to \textit{compress} and accelerate linear algebraic operations, addressing challenges in high-dimensional data analysis. This article reviews the foundations of both fields and presents distributed schemes that combine techniques from both to speed up optimization and machine learning algorithms, in the presence of slow or non-responsive servers. Along the way, we touch on various related topics and mathematical concepts.
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