Multidimensional DSP with GPU acceleration: Difference between revisions

Revision as of 06:41, 12 November 2015

Revision as of 06:41, 12 November 2015

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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 a ubiquitous methodology 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 dimension 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.

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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 journal|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}}</ref>.

== GPGPU Computations ==

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===C++ Amp===

[[C++ AMP|C++ Amp]] is a programming model proposed by [[Microsoft]]. C++ Amp is a [[C++]] based library designed for programming SIMD processors<ref>{{Cite web|title = C++ AMP (C++ Accelerated Massive Parallelism)|url = https://msdn.microsoft.com/en-us/library/hh265137.aspx|website = msdn.microsoft.com|accessdate = 2015-11-05}}</ref>.

===OpenAcc===

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== Real Applications ==

===Digital Filter Design===

Designing a digital filter in multidimensional area 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 journal|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 = 0321335597|___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 = 0123859638|___location = San Francisco, CA, USA|pages = }}</ref>.

===Radar Signal Reconstruction and Analysis===

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===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 requires very high sampling rate and image resolutions to reconstruct images. By applying GPGPUs' superior computation power, it has been proved that we can acquire better quality on medical images<ref>{{Cite web|title = Medical Imaging{{!}}NVIDIA|url = http://www.nvidia.com/object/medical_imaging.html|website = www.nvidia.com|publisher = https://plus.google.com/104889184472622775891|accessdate = 2015-11-07}}</ref><ref>{{Cite journal|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|date = 2005-01-01|pages = 5145-5148|doi = 10.1109/IEMBS.2005.1615635|first = Yang|last = Heng|first2 = Lixu|last2 = Gu}}</ref>.

==References==

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{{Parallel computing}}

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[[:Category:Digital signal processors]]

[[:Category:GPGPU]]

[[:Category:Parallel computing]]

[[Category:Digital signal processing]]

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[[:Category:Digital signal processors]]

[[:Category:GPGPU]]

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[[Category:Parallel computing]]