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UID:submissions.pasc-conference.org_PASC22_sess181_pos108@linklings.com
SUMMARY:P03 - Pylspack: Fast Parallel Algorithms, Data Structures and Soft
 ware for Sparse Matrix Sketching, Column Subset Selection, Regression and 
 Leverage Scores
DESCRIPTION:Poster\n\nP03 - Pylspack: Fast Parallel Algorithms, Data Struc
 tures and Software for Sparse Matrix Sketching, Column Subset Selection, R
 egression and Leverage Scores\n\nSobczyk, Gallopoulos\n\nIn recent work, w
 e developed novel parallel algorithms and data structures and software imp
 lementations for three fundamental operations in Numerical Linear Algebra:
  (i) matrix sketching, (ii) computation of the Gram matrix and (iii) compu
 tation of the squared row norms of the product of two matrices. This prese
 ntation focuses on the ubiquitous Gaussian and CountSketch random projecti
 ons, as well as their combination. We present the data structures for stor
 ing such random projection matrices, that are memory efficient and fully p
 arallelizable both to construct and to multiply with a dense or sparse inp
 ut matrix. We show how these results can applied to other important proble
 ms, namely column subset selection, least squares regression and leverage 
 scores estimations. We also present details of our publicly available impl
 ementation (https://github.com/IBM/pylspack), the Pylspack Python package,
  whose core is written in C++ and paralellized with OpenMP. We show that o
 ur implementations outperform existing state-of-the-art libraries, namely 
 the corresponding implementations from libskylark (https://xdata-skylark.g
 ithub.io/libskylark/) and scikit-learn (https://scikit-learn.org) for the 
 same tasks. Pylspack is fully compatible with standard numerical packages 
 like SciPy and NumPy and is easily obtained via a single command: pip inst
 all git+https://github.com/IBM/pylspack.
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