Multidimensional DSP with GPU acceleration: Difference between revisions

Revision as of 10:14, 21 December 2017

Revision as of 10:21, 21 December 2017

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[[Digital signal processing]] (DSP) is a ubiquitous methodology in scientific and engineering computations. However, practically, 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 dimension increases, the time and/or storage complexity of processing digital signals grow 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 as having an excellent throughput on vector operations and numeric manipulations through a high degree of parallel computations. While processing digital signals, particularly multidimensional signals, often involves a series of vector operations on massive amountnumbers of independent data samples, GPGPUs are now widely employed to accelerate multidimensional DSP, such as [[image processing]], [[Video processing|video codecs]], [[Radar signal characteristics|radar signal analysis]], [[sonar signal processing]], and [[ultrasound scan]]ning. 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.

== Motivation ==

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=== Lower Sampling Rate ===

A makeshift to achieve a real-time requirement in multidimensional DSP applications is to use a lower sampling rate, which can efficiently reduce the number of samples to be processed at one time and thereby decrease the total processing time. However, this can lead to the aliasing problem due to the [[Nyquist–Shannon sampling theorem|sampling theorem]] and poor-quality outputs. In some applications, such as military radars and medical images, we are eager to have highly precise and accurate results. In such cases, using a lower sampling rate to reduce the amount of computation in the multidimensional DSP ___domain is not always allowable.

=== Digital Signal Processors (DSPs) ===

Digital signal processors are designed specifically to process vector operations. They have been widely used in DSP computations for decades. However, most digital signal processors are only capable of manipulating a few operations in parallel. This kind of designsdesign is sufficient to accelerate audio processing (1-D signals) and image processing (2-D signals). However, with a large amountnumber of data samples inof multidimensional signals, this is still not powerful enough to retrieve computation results in real-time.

=== Supercomputer Assistance ===