We demonstrate an implementation for an approximate rank-k SVD factorization, combining well-known randomized projection techniques with previously known paralel solutions in order to compute steps of the random projection based SVD procedure. We structure the problem in a way that it reduces to fast computation around $k \times k$ matrices computed on a single machine, greatly easing the computability of the problem. The paper is also a tutorial on paralel linear algebra methods using a plain architecture without burdensome frameworks.

翻译：我们展示了一种近似k级 SVD 系数化的应用,将众所周知的随机投影技术与先前已知的抛物线溶液结合起来,以计算随机投影基于SVD程序的步骤。我们将问题组织成这样的方式,它可以降低到快速计算在一台机器上计算到的Kk 乘以kkkk k$矩阵,大大减轻了问题的可计算性。本文也是对抛光线代数方法的辅导,它使用一个平坦的结构,而没有繁琐的框架。