论文标题
近似加权$ CR $编码矩阵乘法
Approximate Weighted $CR$ Coded Matrix Multiplication
论文作者
论文摘要
最常见的之一，但同时在线性代数中昂贵的操作是将两个矩阵$ a $ a $ and $ b $倍增。随着机器学习的快速发展和数据量的增加，执行快速矩阵密集型乘法已成为一个主要障碍。克服此问题的两种不同的方法是：1）近似产品； 2）分布执行乘法。 \ textit {$ cr $ -mmultiplication}是一个近似值，其中$ a $ a $ a和$ b $的列和行分别用更换进行采样。在分布式设置中，多个工人并行执行矩阵乘法子任务。一些工人可能是Stragglers，这意味着他们没有及时完成任务。我们提出了一个新颖的\ textit {近似加权$ CR $编码矩阵乘法}方案，该方案可提高分布式矩阵乘法的性能。
One of the most common, but at the same time expensive operations in linear algebra, is multiplying two matrices $A$ and $B$. With the rapid development of machine learning and increases in data volume, performing fast matrix intensive multiplications has become a major hurdle. Two different approaches to overcoming this issue are, 1) to approximate the product; and 2) to perform the multiplication distributively. A \textit{$CR$-multiplication} is an approximation where columns and rows of $A$ and $B$ are respectively sampled with replacement. In the distributed setting, multiple workers perform matrix multiplication subtasks in parallel. Some of the workers may be stragglers, meaning they do not complete their task in time. We present a novel \textit{approximate weighted $CR$ coded matrix multiplication} scheme, that achieves improved performance for distributed matrix multiplication.
