INQ: a state-of-the art implementation of density functional theory for GPUs*

In this talk I will present INQ, a new implementation of density functional theory (DFT) and time-dependent DFT (TDDFT) written from scratch to work on graphical processing units (GPUs).

Besides GPU support, INQ makes use of modern code design features and techniques, to make development fast and simple, and to ensure the quality of the program. By designing the code around algorithms, rather than against specific implementations and numerical libraries, we provide a concise and modular code that is simple to understand, flexible, and extensible.

What we achieve is a fairly complete DFT/TDDFT implementation in roughly 12,000 lines of open-source C++ code. It represents a modular platform for community-driven application development on emerging high-performance computing architectures. The code is freely accesible at http://gitlab.com/npneq/inq .

In TDDFT simulations on GPU-based supercomputers INQ achieves excellent performance. It can handle hundreds and thousands of atoms, with simulation times of a second or less per time-step, and scale to thousands of GPUs.