Implementing density functional theory (DFT) methods on many-core GPGPU accelerators

Abstract

Density Functional Theory (DFT) is one of the most widely used quantum mechanical methods for calculations of the electronic structure of molecules and surfaces, which achieves an excellent balance of accuracy and computational cost. However, for large molecular systems with few hundred atoms, the computational costs are become very high. Therefore, there is a fast growing demand for much more efficient implementations to utilize DFT for macro molecules. General Purpose Graphics Processors (GPUs) are highly parallel, multi-threaded, many-core processors with tremendous computational capability, which out-paces CPUs in terms of floating-point performance. They are particularly focused for computation intensive and highly data-parallel computations. This thesis will introduce the scope of fine grained parallelism with highly data-parallel GPU implementations of several algorithmic parts of DFT. Furthermore, experimental results and benchmarks will be presented in comparison with a current state of art DFT implementation (Molpro).