Skylark (Sketching Library) 0.1
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.
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.
The framework requires the following dependencies:
See the INSTALL file for build instructions.