Multidimensional DSP with GPU acceleration: Difference between revisions

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{{AFC submission|t||ts=20151031094109|u=Sing0512|ns=118}} <!--- Important, do not remove this line before article has been created. --->

[[Digital signal processing|Digital signal processing (DSP)]] is an ubiquitous techniquemethodology in scientific and engineering computations. However, practically, to DSP problems are often not only 1-D. For instance, image data are 2-D signals and radar signals are 3-D signals. While the number of dimensions increases, the time and/or storage complexity of processing digital signal grows dramatically. Therefore, solving DSP problems in real-time is extremely difficult in reality.

Modern [[General-purpose computing on graphics processing units|general purpose graphics processing units (GPGPUs)]] are considered having excellent throughput on vector operations and numeric manipulations by high degree of parallel computation. While processing digital signals, particularly multidimensional signals, often involves in a series of vector operations on massive amount of independent data samples, GPGPUs are now widely employed to accelerate multidimensional DSP, such as [[image processing]], [[Video processing|video codec]], [[Radar signal characteristics|radar signal analysis]], [[sonar signal processing]], and [[Ultrasound scan|ultrasound scanning]]. Conceptually, using GPGPU devices to perform multidimensional DSP is able to dramatically reduce the computation complexity compared with [[Cpu|central processing units (CPUs)]], [[Digital signal processor|digital signal processors (DSPs)]], or other [[Field-programmable gate array|FPGA]] accelerators.

== Introduction ==

Processing multidimensional signals is a common problem in scientific researches and/or engineering computations. Notwithstanding, with its high degree of time and storage complexity, it is extremely difficult to process multidimensional signals in real-time. Practically, to accelerate multidimensional DSP, there are some common approaches can be employed.

==== Lower Sampling Rate ====

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 sampling theorem and make a poor quality of outputs.

==== Adopting Super Computers ====

== Approaches ==