Advancements in High-Performance Computing: Case Studies in Fast Fourier Transform with Graphical Processing Unit Acceleration

In several scientific and engineering applications, the need for fast computational processes is a key factor considering the vast amount of work involved to simulate real-world physical phenomena. This is also the case for the Fast Fourier Transform which is a fundamental method for solving the N-point Discrete Fourier Transform (DFT). However, current DFT implementations on Central Processing Units (CPUs) become extremely slow as the point number grows due to the high number of operations involved in the evaluation of the FFT. For this reason, this study will explore the major improvements of High-Performance Computing (HPC) using the capabilities of Graphical Processing Units (GPUs) to speed up the FFT algorithms. In this project, the FFT algorithm will be executed in parallel on an NVIDIA Quadro K420 GPU using the programming toolkit CUDA (Compute Unified Device Architecture). The results clearly show that, comparing to the CPU implementation, by using the GPU as an accelerator to process the FFT we are able to reduce the time involved in the computational process, which consequently improves the performance scalability as the FFT length increases. For an 8-point FFT, execution time is reduced by 150 times. The performance scales as the input size increases, with GPUs being particularly effective for high-performance computing tasks. The results provide a strong foundation for optimizing FFT algorithms in scientific and engineering applications.