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	<title>GPGPU&#187; Tag: Floating Point :: GPGPU.org</title>
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	<description>General-Purpose Computation on Graphics Hardware</description>
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		<title>AMD announces GPU with Double Precision Floating Point</title>
		<link>http://gpgpu.org/2007/11/14/amd-announces-gpu-with-double-precision-floating-point</link>
		<comments>http://gpgpu.org/2007/11/14/amd-announces-gpu-with-double-precision-floating-point#comments</comments>
		<pubDate>Wed, 14 Nov 2007 16:03:35 +0000</pubDate>
		<dc:creator>Mark Harris</dc:creator>
				<category><![CDATA[Business]]></category>
		<category><![CDATA[Floating Point]]></category>
		<category><![CDATA[GPUs]]></category>

		<guid isPermaLink="false">http://gpgpu.site/?p=300</guid>
		<description><![CDATA[AMD has announced the AMD FireStream 9170 Stream Processor and an accompanying Software Development Kit (SDK) designed to harness the massive parallel processing power of the graphics processing unit (GPU). The AMD FireStream 9170 will support double-precision floating point technology tailored for scientific and engineering calculations. The AMD FireStream SDK is designed to deliver the [...]]]></description>
			<content:encoded><![CDATA[<p>AMD has announced the AMD FireStream 9170 Stream Processor and an accompanying Software Development Kit (SDK) designed to harness the massive parallel processing power of the graphics processing unit (GPU). The AMD FireStream 9170 will support double-precision floating point technology tailored for scientific and engineering calculations. The AMD FireStream SDK is designed to deliver the tools developers need to create and optimize applications on AMD Stream processors. Built using an open platforms approach, the AMD FireStream SDK allows developers to access key Application Programming Interfaces (APIs) and specifications, enabling performance tuning at the lowest level and development of third party tools. Building on AMDâ€™s Close to the Metal (CTM) interface introduced in 2006, the Compute Abstraction Layer (CAL) provides low-level access to the GPU for development and performance tuning along with forward compatibility to future GPUs. For high-level development, AMD is announcing Brook+, a tool providing C extensions for stream computing based on the Brook project from Stanford University. In addition, AMD also plans to support the AMD Core Math Library (ACML) to provide GPU-accelerated math functions, and the COBRA video library accelerates video transcode. Also available are third-party tools from top industry partners including RapidMind and Microsoft. (<a href="http://biz.yahoo.com/bw/071108/20071107006687.html?.v=1" title="Press Release" target="_blank">Press Release</a>)</p>
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		<title>Native, emulated and mixed precision schemes</title>
		<link>http://gpgpu.org/2007/03/13/native-emulated-and-mixed-precision-schemes</link>
		<comments>http://gpgpu.org/2007/03/13/native-emulated-and-mixed-precision-schemes#comments</comments>
		<pubDate>Tue, 13 Mar 2007 20:17:00 +0000</pubDate>
		<dc:creator>Mark Harris</dc:creator>
				<category><![CDATA[Research]]></category>
		<category><![CDATA[Floating Point]]></category>
		<category><![CDATA[Numerical Algorithms]]></category>
		<category><![CDATA[Papers]]></category>
		<category><![CDATA[Precision]]></category>
		<category><![CDATA[Scientific Computing]]></category>

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		<description><![CDATA[This survey paper by D. Göddeke and R. Strzodka compares native double precision solvers for linear systems of equations as they typically arise in finite element discretizations with emulated- and mixed-precision schemes. Such schemes are particularly suitable for coupled hardware configurations such as GPUs and FPGAs, which serve as co-processors to the general purpose CPU. [...]]]></description>
			<content:encoded><![CDATA[<p>This survey paper by D. Göddeke and R. Strzodka compares native double precision solvers for linear systems of equations as they typically arise in finite element discretizations with emulated- and mixed-precision schemes. Such schemes are particularly suitable for coupled hardware configurations such as GPUs and FPGAs, which serve as co-processors to the general purpose CPU. The results demonstrate that</p>
<ol>
<li> accuracy is preserved even for very ill-conditioned systems,</li>
<li>significant speedups can be achieved (time aspect, GPUs) and</li>
<li>area requirements are reduced (space aspect, FPGA).</li>
</ol>
<p>(<a href="http://www.mathematik.uni-dortmund.de/~goeddeke/pubs/ijpeds06.html" target="_blank">link/preprint</a>)</p>
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