CUDA programs for solving the time-dependent dipolar Gross-Pitaevskii equation in an anisotropic trap

TL;DR

This paper introduces CUDA-accelerated programs for solving the dipolar Gross-Pitaevskii equation, significantly improving computational speed on Nvidia GPUs for both stationary and non-stationary solutions.

Contribution

The paper develops CUDA-based versions of existing GP equation solvers, utilizing GPU acceleration and cuFFT library to enhance performance and speed.

Findings

Average speedup of 12 to 25 times on GPU

Efficient implementation of split-step Crank-Nicolson method on CUDA

Demonstrated improved computational performance

Abstract

In this paper we present new versions of previously published numerical programs for solving the dipolar Gross-Pitaevskii (GP) equation including the contact interaction in two and three spatial dimensions in imaginary and in real time, yielding both stationary and non-stationary solutions. New versions of programs were developed using CUDA toolkit and can make use of Nvidia GPU devices. The algorithm used is the same split-step semi-implicit Crank-Nicolson method as in the previous version (R. Kishor Kumar et al., Comput. Phys. Commun. 195, 117 (2015)), which is here implemented as a series of CUDA kernels that compute the solution on the GPU. In addition, the Fast Fourier Transform (FFT) library used in the previous version is replaced by cuFFT library, which works on CUDA-enabled GPUs. We present speedup test results obtained using new versions of programs and demonstrate an average speedup of 12 to 25, depending on the program and input size.