Real-Space Density Functional Theory on Graphical Processing Units: Computational Approach and Comparison to Gaussian Basis Set Methods

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.