The immediate goal of this framework is to provide implementations of sketching-based NLA kernels on a distributed platform, and to broaden the classes of matrices for which these specific implementations work well.

Introduction

This software stack provides sketching-based NLA kernels for more general data analysis and optimization applications; such tasks have significantly different input matrices and performance criteria than arise in the more traditional scientific computing applications. The crucial NLA kernels to be implemented include regression and low-rank approximations of matrices, akin to the Singular Value Decomposition (SVD).

Additionally this library provides a simple distributed Python interface.

Requirements

The framework requires the following dependencies:

• Boost
• Python
• Elemental
• BLAS/LAPACK
• FFTW (optional, required for fast transformations)

See the INSTALL file for build instructions.