Multidimensional DSP with GPU acceleration: Difference between revisions

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Multidimensional Digital Signal Processing (MDSP) refers to the extension of [[Digital signal processing]] (DSP) techniques to signals that vary in more than one dimension. While conventional DSP typically deals with one-dimensional data, such as time-varying [[Audio signal|audio signals]], MDSP involves processing signals in two or more dimensions. Many of the principles from one-dimensional DSP, such as [[Fourier transform|Fourier transforms]] and [[filter design]], have analogous counterparts in multidimensional signal processing.

Modern [[general-purpose computing on graphics processing units]] (GPGPUs) have an excellent throughput on vector operations and numeric manipulations through a high degree of parallel computations. Processing digital signals, particularly multidimensional signals, often involves a series of vector operations on massive numbers 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, GPGPUs dramatically reduce the computation complexity when compared with [[Cpu|central processing unitsunit]]s (CPUs)]], [[Digitaldigital signal processor]]s (DSPs), or other [[Field-programmable gate array|FPGA]] accelerators.

==Motivation==

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[[File:SIMD GPGPU.jpg|alt= Figure illustrating a SIMD/vector computation unit in GPGPUs..|thumb|GPGPU/SIMD computation model]]

Modern GPU designs are mainly based on the [[Single instruction, multiple data|SIMD]] (Single Instruction Multiple Data) computation paradigm.<ref>{{cite journal|title=NVIDIA Tesla: A Unified Graphics and Computing Architecture|journal=IEEE Micro|date=2008-03-01|issn=0272-1732|pages=39–55|volume=28|issue=2|doi=10.1109/MM.2008.31|first1=E.|last1=Lindholm|first2=J.|last2=Nickolls|first3=S.|last3=Oberman|first4=J.|last4=Montrym|s2cidbibcode=2793450|url=https://ieeexplore2008IMicr.ieee.org/document/452335828b..39L |url-accesss2cid=registration2793450|language=en}}</ref><ref>{{cite book|title=Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)|last1=Kim|first1=Hyesoon|author1-link=Hyesoon Kim|publisher=Morgan & Claypool Publishers|year=2012|isbn=978-1-60845-954-4|last2=Vuduc|first2=Richard|last3=Baghsorkhi|first3=Sara|last4=Choi|first4=Jee|last5=Hwu|first5=Wen-Mei W.|editor-last=Hill|editor-first=Mark D.|doi=10.2200/S00451ED1V01Y201209CAC020|language=en}}</ref> This type of GPU devices is so-called [[General-purpose computing on graphics processing units|general-purpose GPUs (GPGPUs)]].

GPGPUs are able to perform an operation on multiple independent data concurrently with their vector or SIMD functional units. A modern GPGPU can spawn thousands of concurrent threads and process all threads in a batch manner. With this nature, GPGPUs can be employed as DSP accelerators easily while many DSP problems can be solved by [[Divide and conquer algorithms|divide-and-conquer]] algorithms. A large scale and complex DSP problem can be divided into a bunch of small numeric problems and be processed altogether at one time so that the overall time complexity can be reduced significantly. For example, multiplying two {{math|''M'' × ''M''}} matrices can be processed by {{math|''M'' × ''M''}} concurrent threads on a GPGPU device without any output data dependency. Therefore, theoretically, by means of GPGPU acceleration, we can gain up to {{math|''M'' × ''M''}} speedup compared with a traditional CPU or digital signal processor.

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===Radar signal reconstruction and analysis===

Radar systems usually need to reconstruct numerous 3-D or 4-D data samples in real-time. Traditionally, particularly in military, this needs supercomputers' support. Nowadays, GPGPUs are also employed to replace supercomputers to process radar signals. For example, to process [[Synthetic aperture radar|synthetic aperture radar (SAR)]] signals, it usually involves multidimensional [[Fast Fourier transform|FFT]] computations.<ref>{{cite book|date=2009-10-01|pages=309–314|doi=10.1109/SIPS.2009.5336272|first1=C.|last1=Clemente|first2=M.|last2=Di Bisceglie|first3=M.|last3=Di Santo|first4=N.|last4=Ranaldo|first5=M.|last5=Spinelli|title=2009 IEEE Workshop on Signal Processing Systems|chapter=Processing of synthetic Aperture Radar data with GPGPU|isbn=978-1-4244-4335-2|s2cid=18932083|url=https://ieeexplore.ieee.org/document/5336272|url-access=limited|language=en}}</ref><ref>{{cite book|date=2009-10-01|pages=1–5|doi=10.1109/CISP.2009.5304418|first1=Bin|last1=Liu|first2=Kaizhi|last2=Wang|first3=Xingzhao|last3=Liu|first4=Wenxian|last4=Yu|title=2009 2nd International Congress on Image and Signal Processing|chapter=An Efficient SAR Processor Based on GPU via CUDA|isbn=978-1-4244-4129-7|s2cid=18801932}}</ref><ref>{{cite book|date=2014-06-01|pages=455–458|doi=10.1109/MIXDES.2014.6872240|first1=P.|last1=Monsurro|first2=A.|last2=Trifiletti|first3=F.|last3=Lannutti|title=2014 Proceedings of the 21st International Conference Mixed Design of Integrated Circuits and Systems (MIXDES)|chapter=Implementing radar algorithms on CUDA hardware|isbn=978-83-63578-05-3|s2cid=16482715}}</ref> GPGPUs can be used to rapidly perform FFT and/or iFFT in this kind of applications.

===Self-driving cars===