<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>iMonad.com &#187; OpenCL</title>
	<atom:link href="http://imonad.com/blog/category/opencl/feed/" rel="self" type="application/rss+xml" />
	<link>http://imonad.com/blog</link>
	<description>Software engineering, Functional programming, Predictive Analytics</description>
	<lastBuildDate>Sat, 19 Dec 2009 09:16:16 +0000</lastBuildDate>
	<generator>http://wordpress.org/?v=2.8.4</generator>
	<language>en</language>
	<sy:updatePeriod>hourly</sy:updatePeriod>
	<sy:updateFrequency>1</sy:updateFrequency>
			<item>
		<title>Commercial Users of Functional Programming Videos</title>
		<link>http://imonad.com/blog/2009/09/commercial-users-of-functional-programming-videos/</link>
		<comments>http://imonad.com/blog/2009/09/commercial-users-of-functional-programming-videos/#comments</comments>
		<pubDate>Thu, 24 Sep 2009 13:15:32 +0000</pubDate>
		<dc:creator>zoo</dc:creator>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[F#]]></category>
		<category><![CDATA[GPU]]></category>
		<category><![CDATA[Haskell]]></category>
		<category><![CDATA[OpenCL]]></category>
		<category><![CDATA[functional programming]]></category>
		<category><![CDATA[science]]></category>

		<guid isPermaLink="false">http://imonad.com/blog/?p=216</guid>
		<description><![CDATA[Just found a lot of videos from the &#8220;Commercial Users of Functional Programming&#8221; Edinburgh 2009
Lectures on Haskel, Scala, Erlang, F# etc.
Running Haskell Array Computations on a GPU

]]></description>
		<wfw:commentRss>http://imonad.com/blog/2009/09/commercial-users-of-functional-programming-videos/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>OpenCL Visual Profiler with Updated Drivers and SDK Code Samples</title>
		<link>http://imonad.com/blog/2009/09/opencl-visual-profiler-with-updated-drivers-and-sdk-code-samples/</link>
		<comments>http://imonad.com/blog/2009/09/opencl-visual-profiler-with-updated-drivers-and-sdk-code-samples/#comments</comments>
		<pubDate>Thu, 10 Sep 2009 06:58:06 +0000</pubDate>
		<dc:creator>zoo</dc:creator>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[OpenCL]]></category>
		<category><![CDATA[NVIDIA]]></category>
		<category><![CDATA[Open]]></category>

		<guid isPermaLink="false">http://imonad.com/blog/?p=196</guid>
		<description><![CDATA[New Tool or Utility has just been posted to NVIDIA site.
This information is titled: OpenCL Visual Profiler with Updated Drivers
and SDK Code Samples
This information is for: General
The information description is:  Leveraging the extensive performance
instrumentation in NVIDIA&#8217;s OpenCL
drivers and hardware performance signals designed into NVIDIA GPUs, the
OpenCL Visual Profiler provides developers with insight into performance
bottlenecks [...]]]></description>
		<wfw:commentRss>http://imonad.com/blog/2009/09/opencl-visual-profiler-with-updated-drivers-and-sdk-code-samples/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Papers on implementing RBM in GPU</title>
		<link>http://imonad.com/blog/2009/07/papers-on-implementing-rbm-in-gpu/</link>
		<comments>http://imonad.com/blog/2009/07/papers-on-implementing-rbm-in-gpu/#comments</comments>
		<pubDate>Wed, 29 Jul 2009 08:51:27 +0000</pubDate>
		<dc:creator>zoo</dc:creator>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[Neural networks]]></category>
		<category><![CDATA[OpenCL]]></category>
		<category><![CDATA[RBM]]></category>
		<category><![CDATA[GPU]]></category>

		<guid isPermaLink="false">http://imonad.com/blog/?p=189</guid>
		<description><![CDATA[Just read some papers on how to implement Restricted Boltzmann Machine on GPU. In RBM most computationally intensive part is weight update stage. Using GPU (CUDA, OpenCL, etc) can speed this stage 5-70 times depending on GPU and algorithm used. Major bottleneck in the implementations is the communication between main memory and the GPU unit:
&#8220;Design [...]]]></description>
		<wfw:commentRss>http://imonad.com/blog/2009/07/papers-on-implementing-rbm-in-gpu/feed/</wfw:commentRss>
		<slash:comments>4</slash:comments>
		</item>
		<item>
		<title>OpenCL 1.0 Conformance Candidate Release</title>
		<link>http://imonad.com/blog/2009/05/opencl-10-conformance-candidate-release/</link>
		<comments>http://imonad.com/blog/2009/05/opencl-10-conformance-candidate-release/#comments</comments>
		<pubDate>Wed, 13 May 2009 07:05:15 +0000</pubDate>
		<dc:creator>zoo</dc:creator>
				<category><![CDATA[CUDA]]></category>
		<category><![CDATA[GPU]]></category>
		<category><![CDATA[OpenCL]]></category>

		<guid isPermaLink="false">http://imonad.com/blog/?p=170</guid>
		<description><![CDATA[I just received a message from NVIDIA that they are releasing a OpenCL 1.0 Conformance
Candidate to GPU Computing registered developers:
The information description is:  We are pleased to announce the release of
our OpenCL 1.0 Conformance
Candidate to GPU Computing registered developers.  You now have access
to the OpenCL drivers we submitted this week to the Khronos OpenCL
working group.
The [...]]]></description>
		<wfw:commentRss>http://imonad.com/blog/2009/05/opencl-10-conformance-candidate-release/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Neural Information Processing Systems (NIPS) 2008 papers</title>
		<link>http://imonad.com/blog/2008/12/neural-information-processing-systems-nips-2008-papers/</link>
		<comments>http://imonad.com/blog/2008/12/neural-information-processing-systems-nips-2008-papers/#comments</comments>
		<pubDate>Sat, 20 Dec 2008 10:26:19 +0000</pubDate>
		<dc:creator>zoo</dc:creator>
				<category><![CDATA[Neural networks]]></category>
		<category><![CDATA[OpenCL]]></category>
		<category><![CDATA[RBM]]></category>
		<category><![CDATA[science]]></category>

		<guid isPermaLink="false">http://imonad.com/blog/?p=116</guid>
		<description><![CDATA[Neural Information Processing Systems (NIPS) 2008 papers.
Some papers that attracted my attention:
The Recurrent Temporal Restricted Boltzmann
Machine (PDF)

Supervised Bipartite Graph Inference (PDF)

Implicit Mixtures of Restricted Boltzmann Machines (PDF)

]]></description>
		<wfw:commentRss>http://imonad.com/blog/2008/12/neural-information-processing-systems-nips-2008-papers/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Restricted Boltzmann Machine &#8211; Short Tutorial</title>
		<link>http://imonad.com/blog/2008/10/restricted-boltzmann-machine/</link>
		<comments>http://imonad.com/blog/2008/10/restricted-boltzmann-machine/#comments</comments>
		<pubDate>Wed, 08 Oct 2008 20:20:09 +0000</pubDate>
		<dc:creator>zoo</dc:creator>
				<category><![CDATA[Neural networks]]></category>
		<category><![CDATA[OpenCL]]></category>
		<category><![CDATA[Python]]></category>
		<category><![CDATA[RBM]]></category>
		<category><![CDATA[science]]></category>

		<guid isPermaLink="false">http://imonad.com/blog/?p=65</guid>
		<description><![CDATA[I have read a lot of papers on RBM and it seems to be difficult to grasp all the implementation details.
So, I want to share my experience with people facing the same problems. My tutorial is based on variant of RBM-s named Continuous Restricted Boltzmann Machine or CRBM for short. CRBM have very close implementation [...]]]></description>
		<wfw:commentRss>http://imonad.com/blog/2008/10/restricted-boltzmann-machine/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
	</channel>
</rss>
