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=== GPU Acceleration ===
[[Graphics processing unit|GPUs]] are originally devised to accelerate image processing and video stream 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 to replace supercomputers performing multidimensional DSP.<ref>{{cite journalbook|title = OpenCL: Make Ubiquitous Supercomputing Possible|url = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5581488&tag=1|journal = 2010 12th IEEE International Conference on High Performance Computing and Communications (HPCC)|date = 2010-09-01|pages = 556–561|doi = 10.1109/HPCC.2010.56|first = Slo-Li|last = Chu|first2 = Chih-Chieh|last2 = Hsiao|isbn = 978-1-4244-8335-8}}</ref>
== GPGPU Computations ==
[[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 [[SIMD]] (Single Instruction Multiple Data) computation paradigm.<ref>{{cite journal|title = NVIDIA Tesla: A Unified Graphics and Computing Architecture|url = http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4523358&url=http%253A%252F%252Fieeexplore.ieee.org%252Fxpls%252Fabs_all.jsp%253Farnumber%253D4523358|journal = IEEE Micro|date = 2008-03-01|issn = 0272-1732|pages = 39–55|volume = 28|issue = 2|doi = 10.1109/MM.2008.31|first = E.|last = Lindholm|first2 = J.|last2 = Nickolls|first3 = S.|last3 = Oberman|first4 = J.|last4 = Montrym}}</ref><ref>{{cite book|title = Performance Analysis and Tuning for General Purpose Graphics Processing Units (GPGPU)|last = Kim|first = Hyesoon|publisher = Morgan & Claypool Publishers|year = 2012|isbn = 978-1-60845-954-4|___location = |pages = |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}}</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.
=== Digital Filter Design ===
Designing a multidimensional digital filter is a big challenge, especially [[Infinite impulse response|IIR]] filters. Typically it relies on computers to solve difference equations and obtain a set of approximated solutions. While GPGPU computation is becoming popular, several adaptive algorithms have been proposed to design multidimensional [[Finite impulse response|FIR]] and/or [[Infinite impulse response|IIR]] filters by means of GPGPUs.<ref>{{cite journalbook|title = GPU-efficient Recursive Filtering and Summed-area Tables|url = http://doi.acm.org/10.1145/2024156.2024210|publisher = ACM|journal = Proceedings of the 2011 SIGGRAPH Asia Conference|date = 2011-01-01|___location = New York, NY, USA|isbn = 978-1-4503-0807-6|pages = 176:1–176:12|series = SA '11|doi = 10.1145/2024156.2024210|first = Diego|last = Nehab|first2 = André|last2 = Maximo|first3 = Rodolfo S.|last3 = Lima|first4 = Hugues|last4 = Hoppe}}</ref><ref>{{cite book|title = GPU Gems 2: Programming Techniques For High-Performance Graphics And General-Purpose Computation|last = Pharr|first = Matt|publisher = Pearson Addison Wesley|year = 2005|isbn = 978-0-321-33559-73|___location = |pages = |last2 = Fernando|first2 = Randima}}</ref><ref>{{cite book|title = GPU Computing Gems Emerald Edition|last = Hwu|first = Wen-mei W.|publisher = Morgan Kaufmann Publishers Inc.|year = 2011|isbn = 978-0-12-385963-81|___location = San Francisco, CA, USA|pages = }}</ref>
=== 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 journalbook|title = Processing of synthetic Aperture Radar data with GPGPU|url = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5336272&newsearch=true&queryText=gpgpu%2520radar|journal = IEEE Workshop on Signal Processing Systems, 2009. SiPS 2009|date = 2009-10-01|pages = 309–314|doi = 10.1109/SIPS.2009.5336272|first = C.|last = Clemente|first2 = M.|last2 = Di Bisceglie|first3 = M.|last3 = Di Santo|first4 = N.|last4 = Ranaldo|first5 = M.|last5 = Spinelli|isbn = 978-1-4244-4335-2}}</ref><ref>{{cite journalbook|title = An Efficient SAR Processor Based on GPU via CUDA|url = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5304418|journal = 2nd International Congress on Image and Signal Processing, 2009. CISP '09|date = 2009-10-01|pages = 1–5|doi = 10.1109/CISP.2009.5304418|first = Bin|last = Liu|first2 = Kaizhi|last2 = Wang|first3 = Xingzhao|last3 = Liu|first4 = Wenxian|last4 = Yu|isbn = 978-1-4244-4129-7}}</ref><ref>{{cite journalbook|title = Implementing radar algorithms on CUDA hardware|url = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6872240|journal = Mixed Design of Integrated Circuits Systems (MIXDES), 2014 Proceedings of the 21st International Conference|date = 2014-06-01|pages = 455–458|doi = 10.1109/MIXDES.2014.6872240|first = P.|last = Monsurro|first2 = A.|last2 = Trifiletti|first3 = F.|last3 = Lannutti|isbn = 978-83-63578-05-3}}</ref> GPGPUs can be used to rapidly perform FFT and/or iFFT in this kind of applications.
=== Self-Driving Car ===
Many [[self-driving cars]] apply 3-D image recognition techniques to auto control the vehicles. Clearly, to accommodate the fast changing exterior environment, the recognition and decision processes must be done in real-time. GPGPUs are excellent devices to achieve the goal.<ref>{{cite journalbook|title = Accelerating Cost Aggregation for Real-Time Stereo Matching|url = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6413661|journal = 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS)|date = 2012-12-01|pages = 472–481|doi = 10.1109/ICPADS.2012.71|first = Jianbin|last = Fang|first2 = A.L.|last2 = Varbanescu|first3 = Jie|last3 = Shen|first4 = H.|last4 = Sips|first5 = G.|last5 = Saygili|first6 = L.|last6 = van der Maaten|isbn = 978-1-4673-4565-1}}</ref>
=== Medical Image Processing ===
In order to have accurate diagnosis, 2-D or 3-D medical signals, such as [[ultrasound]], [[X-ray]], [[Magnetic resonance imaging|MRI]], and [[CT scan|CT]], often require very high sampling rate and image resolutions to reconstruct images. By applying GPGPUs' superior computation power, it was shown that we can acquire better-quality medical images<ref>{{cite web|title = Medical Imaging{{!}}NVIDIA|url = http://www.nvidia.com/object/medical_imaging.html|website = www.nvidia.com|accessdate = 2015-11-07}}</ref><ref>{{cite journalbook|title = GPU-based Volume Rendering for Medical Image Visualization|url = http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1615635&url=http%253A%252F%252Fieeexplore.ieee.org%252Fxpls%252Fabs_all.jsp%253Farnumber%253D1615635|journal = Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the|volume = 5|date = 2005-01-01|pages = 5145–5148|doi = 10.1109/IEMBS.2005.1615635|pmid = 17281405|first = Yang|last = Heng|first2 = Lixu|last2 = Gu|isbn = 978-0-7803-8741-6}}</ref>
== References ==
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