A Note on QR-Based Model Reduction: Algorithm, Software, and Gravitational Wave Applications

TL;DR

This paper explores QR-based model reduction methods, compares algorithms, introduces parallel software, and demonstrates their effectiveness on large-scale gravitational wave data from black hole mergers.

Contribution

It establishes the equivalence of different QR-based reduction approaches, develops scalable parallel software, and applies it to large gravitational wave modeling problems.

Findings

QR-based methods achieve errors comparable to POD

The software scales efficiently on supercomputers

Successful application to large gravitational wave datasets

Abstract

While the proper orthogonal decomposition (POD) is optimal under certain norms it's also expensive to compute. For large matrix sizes, it is well known that the QR decomposition provides a tractable alternative. Under the assumption that it is rank--revealing QR (RRQR), the approximation error incurred is similar to the POD error and, furthermore, we show the existence of an RRQR with exactly same error estimate as POD. To numerically realize an RRQR decomposition, we will discuss the (iterative) modified Gram Schmidt with pivoting (MGS) and reduced basis method by employing a greedy strategy. We show that these two, seemingly different approaches from linear algebra and approximation theory communities are in fact equivalent. Finally, we describe an MPI/OpenMP parallel code that implements one of the QR-based model reduction algorithms we analyze. This code was developed with model reduction in mind, and includes functionality for tasks that go beyond what is required for standard QR decompositions. We document the code's scalability and show it to be capable of tackling large problems. In particular, we apply our code to a model reduction problem motivated by gravitational waves emitted from binary black hole mergers and demonstrate excellent weak scalability on the supercomputer Blue Waters up to 32,768 cores and for complex, dense matrices as large as 10,000-by-3,276,800 (about half a terabyte in size).