Scaling Fast Fourier Transforms to Exascale on NVIDIA GPUs for Enhanced Productivity
Introduction to Fast Fourier Transforms in Scientific Computing Fast Fourier Transforms (FFTs) are essential computational tools that convert data between time (or spatial) domains and frequency domains. Their applications span multiple fields, including molecular dynamics, signal processing, computational fluid dynamics (CFD), wireless multimedia, and machine learning. Efficiently performing FFTs is critical to solving complex scientific problems and increasing computational productivity. Challenges in Scaling FFTs for Large-Scale Problems As scientific problems grow in size and complexity, FFT computations must handle massive datasets that require distributed processing. Scaling FFTs to exascale computing levels introduces challenges such as data communication overhead, memory bandwidth limitations, and load balancing across compute units. These factors can reduce overall productivity if not addressed properly. Modern NVIDIA GPU Architectures for FFT Computation NVIDIA’s l...