Event Title
PyGASP: Python-based GPU-Accelerated Signal Processing
Presentation Type
Poster/Portfolio
Presenter Major(s)
Computer Science
Mentor Information
Greg Wolffe
Department
School of Computing and Information Systems
Location
Henry Hall Atrium 79
Start Date
10-4-2013 1:00 PM
End Date
10-4-2013 2:00 PM
Keywords
Information, Innovation, and Technology
Abstract
Computational science is the application of computing technology to evaluate mathematical models in order to solve problems in the scientific disciplines. Many scientific fields are experiencing an explosion of data, with signal processing being a crucial technique for aiding interpretation and for distinguishing meaningful information from noise. This process requires tools that can be easily used by researchers from all branches of science and which are fast enough to manage the enormous amount of data being generated. We have produced such a toolkit: an intuitive, high-performance Python library for facilitating large-scale signal analysis. The library consists of three common signal processing transforms and some filters that can be used for a variety of purposes. Each of the transforms has also been accelerated using General Purpose Graphics Processing Unit (GPGPU) programming, so users with CUDA-capable graphics cards can experience large speedups on big signals.
PyGASP: Python-based GPU-Accelerated Signal Processing
Henry Hall Atrium 79
Computational science is the application of computing technology to evaluate mathematical models in order to solve problems in the scientific disciplines. Many scientific fields are experiencing an explosion of data, with signal processing being a crucial technique for aiding interpretation and for distinguishing meaningful information from noise. This process requires tools that can be easily used by researchers from all branches of science and which are fast enough to manage the enormous amount of data being generated. We have produced such a toolkit: an intuitive, high-performance Python library for facilitating large-scale signal analysis. The library consists of three common signal processing transforms and some filters that can be used for a variety of purposes. Each of the transforms has also been accelerated using General Purpose Graphics Processing Unit (GPGPU) programming, so users with CUDA-capable graphics cards can experience large speedups on big signals.