Multidimensional DSP with GPU acceleration: Difference between revisions

Revision as of 22:59, 1 November 2015

Revision as of 23:09, 1 November 2015

Line 11:

Using a lower sampling rate can efficiently reduce the number of samples to be processed at one time and thereby decreasing the computation complexity. However, this can lead to the aliasing problem in the [[Nyquist–Shannon sampling theorem|sampling theorem]] and make a poor quality of outputs. In some applications, such as military radars, we are eager to have highly precise and accurate results. In such cases, using a lower sampling rate in multidimensional DSP is not allowable.

==== Adopting Super Computers and Designated HardwareSupercomputers ====

In order to accelerate multidimensional DSP computations, using dedicated super computerssupercomputers or cluster computers is required in some situations, e.g., [[weather forecasting]]. However, using super computers designated to simply perform DSP operations takes considerable cost and energy consumption. It is not suitable for all multidimensional DSP applications.

==== GPU Acceleration ====

GPUs are originally designed to accelerate image processing and video rendering. Moreover, since modern GPUs' have good ability to perform numeric computations in parallel with a relatively low cost and better energy efficiency, GPUs are becoming a popular alternative-approach to replace supercomputers performing multidimensional DSP.

== Approaches ==