GPU acceleration of local and semilocal density functional calculations in the SPARC electronic structure code

Abstract

We present a GPU-accelerated version of the real-space SPARC electronic structure code for performing Kohn-Sham density functional theory calculations within the local density and generalized gradient approximations. In particular, we develop a modular math kernel based implementation for NVIDIA architectures wherein the computationally expensive operations are carried out on the GPUs, with the remainder of the workload retained on the CPUs. Using representative bulk and slab examples, we show that GPUs enable speedups of up to 6x relative to CPU-only execution, bringing time to solution down to less than 30 seconds for a metallic system with over 14,000 electrons, and enabling significant reductions in computational resources required for a given wall time.

11 pages, 3 figures

References in corpus (4)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Recent progress with large-scale ab initio calculations: the CONQUEST code
• Parallel Self-Consistent-Field Calculations via Chebyshev-Filtered Subspace Acceleration
• On preconditioning the self-consistent field iteration in real-space Density Functional Theory