  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.
