A Scalable Eigensolver for Real-space Pseudopotential Density Functional Theory: A Polynomial-filtered Spectrum Slicing Method

First-principles electronic structure calculations are a popular avenue for understanding and predicting properties of materials. However, solving the electronic structures of the materials of interest, such as complex biomolecules, nanostructures, and interfacial systems can require descriptions of systems with many atoms, e.g., systems with over 10,000 atoms. Systems of this size pose a challenge to current electronic structure computation software. We will present recent work using a spectrum-slicing algorithm, which is implemented in a real-space pseudopotential density functional theory code, PARSEC. The spectrum slicing method builds an additional layer of parallelization on top of the Chebyshev-filtered subspace iteration. Our approach provides more flexibility to fully utilize the computing power of modern distributed parallel computers. We will demonstrate the scalability of the algorithm and discuss outstanding challenges.