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		<title>CUDA &#8211; MCC for Fingerprint Minutia Pairing</title>
		<link>https://iriscores.com/2023/01/22/cuda-mcc-for-fingerprint-minutia-pairing/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Sun, 22 Jan 2023 11:58:06 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Acceleration]]></category>
		<category><![CDATA[biometrics]]></category>
		<category><![CDATA[Fingerprint]]></category>
		<category><![CDATA[Latent]]></category>
		<category><![CDATA[MCC]]></category>
		<category><![CDATA[Minutia]]></category>
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		<title>Finite Differences in Electromagnetic with CUDA</title>
		<link>https://iriscores.com/2019/02/16/finite-differences-in-electromagnetic-with-cuda/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Sat, 16 Feb 2019 19:07:23 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Dirichlet]]></category>
		<category><![CDATA[FEM]]></category>
		<category><![CDATA[Gauss-Seidel]]></category>
		<category><![CDATA[Iterative]]></category>
		<category><![CDATA[Jacobi]]></category>
		<category><![CDATA[Microstrip]]></category>
		<category><![CDATA[Potential]]></category>
		<category><![CDATA[SOR]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=281</guid>

					<description><![CDATA[Back at the university, I got excited about one of the methods used for computing various quantities from electro magnetics. Particularly the method of finite differences, which is not among the widely used methods, but is somewhat simple to understand and compute (Its not the most efficient method either &#8211; but it can be easily [&#8230;]]]></description>
		
		
		
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		<title>Real time Julia and Mandelbrot set Visualization</title>
		<link>https://iriscores.com/2018/01/04/real-time-julia-and-mandelbrot-set-visualization/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Thu, 04 Jan 2018 18:46:56 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Fractals]]></category>
		<category><![CDATA[Julia]]></category>
		<category><![CDATA[Mandelbrot]]></category>
		<category><![CDATA[Realtime]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=271</guid>

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		<title>CUDA QSWIFT Library with QR EigenValue solver</title>
		<link>https://iriscores.com/2016/11/10/cuda-qswift-library-with-qr-eigenvalue-solver/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Thu, 10 Nov 2016 15:58:52 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Decomposition]]></category>
		<category><![CDATA[Gaussian Elimination]]></category>
		<category><![CDATA[Gram Schmidt]]></category>
		<category><![CDATA[Hauseholder]]></category>
		<category><![CDATA[Inversion]]></category>
		<category><![CDATA[Matlab]]></category>
		<category><![CDATA[Matrix Multiply]]></category>
		<category><![CDATA[MEX]]></category>
		<category><![CDATA[QR]]></category>
		<category><![CDATA[Trildiagonalization]]></category>
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					<description><![CDATA[When I was deciding which topic to use for my diploma thesis back at my university, there were several interesting topics. I ended up choosing &#8220;Suppression of photobleaching in Super-Resolution Optical Fluctuation Imaging using digital filtering.&#8221; which was kind of close to my specialization and besides, I had a great leader of my thesis. If [&#8230;]]]></description>
		
		
		
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		<title>Cuda FFT R2/R4/R8</title>
		<link>https://iriscores.com/2016/08/25/cuda-fft-r2-r4-r8/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Thu, 25 Aug 2016 22:12:33 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Butterfly]]></category>
		<category><![CDATA[DIF]]></category>
		<category><![CDATA[DIT]]></category>
		<category><![CDATA[FFT]]></category>
		<category><![CDATA[Matlab]]></category>
		<category><![CDATA[MEX]]></category>
		<category><![CDATA[R2]]></category>
		<category><![CDATA[R4]]></category>
		<category><![CDATA[R8]]></category>
		<category><![CDATA[Shared]]></category>
		<category><![CDATA[Twiddle]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=338</guid>

					<description><![CDATA[]]></description>
		
		
		
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		<title>GPS Signal Processing</title>
		<link>https://iriscores.com/2015/12/15/gps-signal-processing/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Tue, 15 Dec 2015 20:13:28 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Correlation]]></category>
		<category><![CDATA[DBZP]]></category>
		<category><![CDATA[Demo]]></category>
		<category><![CDATA[FFT]]></category>
		<category><![CDATA[GPS]]></category>
		<category><![CDATA[ML]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=294</guid>

					<description><![CDATA[Back in the university, one of my favorite projects I worked on was the estimation of a GPS signal delay and a Doppler Offset. The Doppler offset is simply caused by the fact that GNSS satellites move across the sky while broadcasting on a specified frequency. As a result the final received frequency is different. [&#8230;]]]></description>
		
		
		
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		<title>CUDA Bitonic Sort</title>
		<link>https://iriscores.com/2014/05/08/cuda-bitonic-sort/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Thu, 08 May 2014 13:48:18 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Bitonic]]></category>
		<category><![CDATA[Parallel]]></category>
		<category><![CDATA[Sort]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=247</guid>

					<description><![CDATA[Sorting data is a very common task and there are variety of algorithms to do the same (QuickSort, BubbleSort, MergeSort Etc &#8230; ). Not all of them are however suited for CUDA Architecture. Its important to bear that in mind, as people occasionally forces you to do something without the underlying information whether that is [&#8230;]]]></description>
		
		
		
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		<title>CUDA Inclusive Scan</title>
		<link>https://iriscores.com/2013/09/01/cuda-inclusive-scan/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Sun, 01 Sep 2013 18:23:10 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Barrier]]></category>
		<category><![CDATA[Cumulative]]></category>
		<category><![CDATA[Memory]]></category>
		<category><![CDATA[Shared]]></category>
		<category><![CDATA[Sum]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=240</guid>

					<description><![CDATA[My first class of CUDA programming was almost a nightmare ( I have fortunately enrolled spontaneously). I was excited to learn about GPGPU and was expecting to do some really cool stuff on GeForce cards within that semester. The truth however was, that we have spent quite a long time in CPU assembly (SSE Instructions) [&#8230;]]]></description>
		
		
		
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		<title>CUDA Matrix Multiplication</title>
		<link>https://iriscores.com/2013/03/11/cuda-matrix-multiplication/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Mon, 11 Mar 2013 17:53:00 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Global]]></category>
		<category><![CDATA[Matrix]]></category>
		<category><![CDATA[Memory]]></category>
		<category><![CDATA[Nvidia]]></category>
		<category><![CDATA[Optimization]]></category>
		<category><![CDATA[Shared]]></category>
		<category><![CDATA[Tiling]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=234</guid>

					<description><![CDATA[Matrix to Matrix multiplication is a very popular algorithm to show the real performance of any Graphic card. This is by nature due to the fact that this operation is very costly in terms of computing performance as the amount of multiplications required scales with matrix size &#8211; N3 . Even a very bad and [&#8230;]]]></description>
		
		
		
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		<item>
		<title>NVIDIA CUDA Introduction</title>
		<link>https://iriscores.com/2012/12/22/nvidia-cuda-introduction/</link>
		
		<dc:creator><![CDATA[IrisCores]]></dc:creator>
		<pubDate>Sat, 22 Dec 2012 11:23:52 +0000</pubDate>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Introduction]]></category>
		<category><![CDATA[Nvidia]]></category>
		<category><![CDATA[Vector]]></category>
		<guid isPermaLink="false">https://iriscores.com/?p=219</guid>

					<description><![CDATA[Many people are wondering how is it possible that Graphics Processing Units (GPUs) have so much computing power. The answer lies in the history of GPUs. They have always been used as accelerators for graphics and rendering, especially video games. In fact GPUs thanks for their power to all the gamers around the world ?. [&#8230;]]]></description>
		
		
		
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