GPU acceleration of conjugate gradient method obtaining Green's function for transport-property calculation

We present a graphics processing unit (GPU) acceleration procedure for the transport-property calculation of the RSPACE code, which uses density functional theory and the real-space finite-difference method. Porting the RSPACE code to run on a GPU using Open Accelerator and libraries results in the improvement of the computational speed and efficiency compared with a central processing unit. Furthermore, we achieved a speed up of similar to 4x compared with a single GPU and effective processing in a multi-GPU environment by parallelizing in a way that minimizes communication and synchronization. Our GPU-acceleration procedure is demonstrated by the calculation of electron-transport properties of (4,4) carbon nanotubes (CNTs) with a 5-8-5 defect for which the calculations are suitable for parallel processing on GPUs. It is found that the information loss is strongly affected by the defect geometry when electron waves propagating in CNTs are utilized as the information transfer signal.

Automated summary

In this study, we propose a GPU acceleration procedure for the transport-property calculation of the RSPACE code, which effectively improves computational speed and efficiency. By parallelizing the calculation and minimizing communication and synchronization, we achieved a speed up of approximately 4x in a multi-GPU environment. The influence of defect geometry on information loss in electron waves propagating in carbon nanotubes is also investigated.