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.
@article{arxiv.1306.2953,
title = {Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods},
author = {Xavier Andrade and Alán Aspuru-Guzik},
journal= {arXiv preprint arXiv:1306.2953},
year = {2013}
}