SPARC is an open-source software package for the accurate, efficient, and scalable solution of the Kohn-Sham equations. The package is straightforward to install/use and highly competitive with state-of-the-art planewave codes, demonstrating comparable performance on a small number of processors and order-of-magnitude advantages as the number of processors increases. Notably, the current version of SPARC brings solution times down to a few seconds for systems with O(100-500) atoms on large-scale parallel computers, outperforming planewave counterparts by an order of magnitude and more. Future versions will target similar solution times for large-scale systems containing many thousands of atoms, and the efficient solution of systems containing a hundred thousand atoms and more.

** Publications **

Matlab-Simulation Package for Ab-initio Real-space Calculations (M-SPARC) is a real-space code for performing electronic structure calculations based on Kohn-Sham Density Functional Theory (DFT). It provides a rapid prototyping platform for the development and testing of new algorithms and methods in real-space DFT. Additionally, it provides a convenient avenue for the accurate first principles study of small to moderate sized systems.

** Publications **

SQDFT is a code for performing high-temperature Born-Oppenheimer Quantum Molecular Dynamics (QMD) in the framework of Kohn-Sham Density Functional Theory (DFT). While applicable at any temperature, the SQDFT code is most efficient at high temperature where the Fermi-Dirac distribution becomes smoother and density matrix becomes correspondingly more localized. SQDFT employs the O(N) infinite-cell Clenshaw-Curtis Spectral Quadrature (SQ) method, a technique that is applicable to metallic as well as insulating systems, is highly parallelizable to hundreds of thousands of processors, becomes increasingly efficient with increasing temperature, and provides results corresponding to the infinite crystal without the need of Brillouin zone integration.

** Publications **

The Alternating Anderson-Richardson (AAR) method provides an efficient and scalable alternative to current state-of-the-art preconditioned Krylov solvers for the solution of large, sparse linear systems on high performance computing platforms, with increasing advantage as the number of processors is increased. Moreover, the method is simple and general, applying to symmetric and nonsymmetric systems, real and complex alike.

** Publications **