title: Real-Space Density Functional Theory on Graphical Processing Units: Computational Approach and Comparison to Gaussian Basis Set Methods.
abstract: We discuss the application of graphical processing units (GPUs) to accelerate real-space density functional theory (DFT) calculations. To make our implementation efficient, we have developed a scheme to expose the data parallelism available in the DFT approach; this is applied to the different procedures required for a real-space DFT calculation. We present results for current-generation GPUs from AMD and Nvidia, which show that our scheme, implemented in the free code Octopus, can reach a sustained performance of up to 90 GFlops for a single GPU, representing a significant speed-up when compared to the CPU version of the code. Moreover, for some systems, our implementation can outperform a GPU Gaussian basis set code, showing that the real-space approach is a competitive alternative for DFT simulations on GPUs.
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University , 12 Oxford Street, Cambridge, Massachusetts 02138, United States.
authors.forename: Xavier
authors.initials: X
authors.lastname: Andrade
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University , 12 Oxford Street, Cambridge, Massachusetts 02138, United States.
authors.forename: Alán
authors.initials: A
authors.lastname: Aspuru-Guzik