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We present a parallel implementation of the block-tridiagonal divide-and-conquer algorithm that computes eigensolutions of symmetric block-tridiagonal matrices to reduced accuracy. In our implementation, we use mixed data/task parallelism to achieve data distribution and workload balance. Numerical tests show that our implementation is efficient, scalable and computes eigenpairs to prescribed accuracy. We compare the performance of our parallel eigensolver with that of the ScaLAPACK divide-and-conquer eigensolver on block-tridiagonal matrices. [ABSTRACT FROM AUTHOR]

Describes Computing Approximate Eigensystems (CApE), a framework for approximating eigenpairs in electronic structure computations. Efficient approximation of symmetric linear eigenvalue problems occurring in the self-consistent field method used during electronic structure computations; Algorithmic tools that detect matrix structure in application problems and compute solutions to prescribed accuracy.