Application Of Graphics Processing Unit In Matrix Inversion And Normal Mode Analysis

Graphics processing unit (GPU) is specially used for graphics processing. In recent years, the peak single precision performance of GPU has been increased from several Gflops to Tflops. With the development of its programmability, GPU has been increasingly applied to accelerate the scientific computation. Besides its huge parallel computing power, GPU is also a low-cost, low-power chip and becomes an important part of high performance computers nowadays. How to apply the parallel computing technology of GPU to more scientific computations is currently the hot topic in the high performance computing field. In this paper, for the purpose of demonstrating the programmability and multi-threads parallel computing power of GPU, we have done the following work:Firstly, matrix inversion is an important matrix operation, but the computing process of large-scale matrix inversion is very time-consuming in the serial mode of CPU. In this paper, we map the calculation of matrix inversion onto GPU using Compute Unified Device Architecture (CUDA) provided by NVIDIA according to the hardware characteristics of GPU. And we got a significant speedup (more than 300) and the peak single-precision performance has achieved 230 Gflops which can meet the demand for computing speed of matrix inversion in some scientific computation applications. The single-precision and the double-precision FLOPS of GPU are analyzed according to the results of this program. What is more, we analyze the influence of data transfer time on the parallel performance of GPU and summarize the characteristics of the algorithms fit for GPU for the purpose of applying GPU to molecular dynamics simulation which is a more complex computational system.Secondly, normal mode analysis (NMA) is an effective method to predict collective structural changes in proteins and it is the most time-consuming part in molecular simulation for calculating the sample of free energy. However, the calculations are limited in time scale mainly because the required diagonalization of the matrix is a computationally exhausting task. In this paper, we accelerate NMA process by mapping the most time-consuming part onto GPU. The GPU-accelerated all-atom NMA has achieved a considerable speedup (more than 20) over CPU-based NMA which could reduce the runtime of diagonalization significantly and the peak single-precision performance has achieved 180 Gflops. In addition, we analyze the influence of precision changes on both the computing performance and the accuracy of GPU.