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An optimized and scalable eigensolver for sequences of eigenvalue problems

In many scientific applications, the solution of nonlinear differential equations are obtained through the setup and solution of a number of successive eigenproblems. These eigenproblems can be regarded as a sequence whenever the solution of one problem fosters the initialization of the next. In addition, in some eigenproblem sequences, there is a connection between the solutions of adjacent eigenproblems. Whenever it is possible to unravel the existence of such a connection, the eigenproblem sequence is said to be correlated. When facing with a sequence of correlated eigenproblems, the current strategy amounts to solving each eigenproblem in isolation. We propose an alternative approach that exploits such correlation through the use of an eigensolver based on subspace iteration and accelerated with Chebyshev polynomials (Chebyshev filtered subspace iteration (ChFSI)). The resulting eigensolver is optimized by minimizing the number of matrix–vector multiplications and parallelized using the Elemental library framework. Numerical results show that ChFSI achieves excellent scalability and is competitive with current dense linear algebra parallel eigensolvers. Copyright © 2014 John Wiley & Sons, Ltd.