High Performance Solution of Skew-symmetric Eigenvalue Problems with Applications in Solving the Bethe-Salpeter Eigenvalue Problem

TL;DR

This paper introduces a high-performance, scalable solver for dense skew-symmetric eigenvalue problems, extending the ELPA library, significantly improving computational efficiency for applications like the Bethe-Salpeter equation in quantum physics.

Contribution

We extend the ELPA library to efficiently solve skew-symmetric eigenvalue problems, enabling faster computations for quantum physics applications such as the Bethe-Salpeter eigenvalue problem.

Findings

Performance up to 3.67 times higher than existing methods.

Runtime for Bethe-Salpeter eigenvalue problem improved by a factor of 10.

Method is scalable and supports GPU acceleration.

Abstract

We present a high-performance solver for dense skew-symmetric matrix eigenvalue problems. Our work is motivated by applications in computational quantum physics, where one solution approach to solve the so-called Bethe-Salpeter equation involves the solution of a large, dense, skew-symmetric eigenvalue problem. The computed eigenpairs can be used to compute the optical absorption spectrum of molecules and crystalline systems. One state-of-the art high-performance solver package for symmetric matrices is the ELPA (Eigenvalue SoLvers for Petascale Applications) library. We extend the methods available in ELPA to skew-symmetric matrices. This way, the presented solution method can benefit from the optimizations available in ELPA that make it a well-established, efficient and scalable library, such as GPU support. We compare performance and scalability of our method to the only available high-performance approach for skew-symmetric matrices, an indirect route involving complex arithmetic. In total, we achieve a performance that is up to 3.67 higher than the reference method using Intel's ScaLAPACK implementation. The runtime to solve the Bethe-Salpeter-Eigenvalue problem can be improved by a factor of 10. Our method is freely available in the current release of the ELPA library.