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Confessions of a Speed Junkie (Overview) | gpgpu-computing.blogspot.com Reviews
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Confessions of a Speed Junkie (Overview): Change... It's a good thing
http://gpgpu-computing.blogspot.com/2009/08/change-its-good-thing.html
Confessions of a Speed Junkie (Overview). Friday, August 14, 2009. Change. It's a good thing. So now you know what GPGPU is, what it is not, the magnitude of the performance gains you can expect to get now and in the near future, and a bit of history as to how we got here. But how can you effect the necessary change in your organization to benefit from this new technology? With all of these impediments to change how do you get the ball rolling? Section and subscribe to my site feed. Live Free or Die.
Confessions of a Speed Junkie (Overview): Cage Match (CPU vs. GPU)
http://gpgpu-computing.blogspot.com/2009/08/cage-match-cpu-vs-gpu.html
Confessions of a Speed Junkie (Overview). Monday, August 3, 2009. Cage Match (CPU vs. GPU). So now that we have a universal measure of computational capacity how does a CPU stack up against a GPU? Containing more double precision ALUs which should yield the performance results in the chart below. (I’m not getting into the details of my calculation, so until they start shipping their new card you’ll just have to take my word for it.). By adding more double precision ALUs Nvidia’s next generation GPU...
Confessions of a Speed Junkie (Overview): GPGPU 101
http://gpgpu-computing.blogspot.com/2009/08/gpgpu-is-practice-of-using-graphics.html
Confessions of a Speed Junkie (Overview). Monday, August 3, 2009. GPGPU is the practice of using graphics processors for general purpose computing. Why on earth would you want to jump through all the hoops required to do that? To answer this we first need to look at how a CPUs and GPUs really work. While this explains what GPGPU is it still doesn't answer the question "Why should I do it? See my next post “Cage Match (CPU vs GPU)”. Monday, August 03, 2009. There was an error in this gadget.
Confessions of a Speed Junkie (Overview): Don’t Hammer in Screws
http://gpgpu-computing.blogspot.com/2009/08/dont-hammer-in-screws.html
Confessions of a Speed Junkie (Overview). Tuesday, August 4, 2009. Don’t Hammer in Screws. Michael Flynn, a professor emeritus at Stanford University, came up with a method of classifying computer architectures. This method of classification became know as “Flynn’s Taxonomy”. The taxonomy is based on the number of concurrent instruction (or control) and data streams available in the architecture. Flynn’s breakdown is as follows:. Single Instruction, Single Data (SISD). Let’s dig a little deeper int...
Confessions of a Speed Junkie (Overview): GPGPU (A Historical Perspective)
http://gpgpu-computing.blogspot.com/2009/08/gpgpu-historical-perspective.html
Confessions of a Speed Junkie (Overview). Wednesday, August 5, 2009. GPGPU (A Historical Perspective). Lib Sh is the result of research at the University of Waterloo Computer Graphic Lab. The language was conceived and implemented by several members of the CGL. Lib Sh is a metaprogramming language for programmable GPUs. As of August 2006, Lib Sh is no longer maintained. RapidMind Inc. was formed to commercialize the research behind Lib Sh. So you want to get your firm moving down the GPGPU path? For the ...
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Confessions of a Speed Junkie (Overview)
Confessions of a Speed Junkie (Overview). Friday, August 14, 2009. In the past if a programs performance was less than adequate for the user you could typically just buy more powerful hardware to make it faster. With the impending collapse of Moore's law this will no longer be true. So until they come up with subatomic computing, programmers will need to embrace parallel computing more in order to squeeze performance gains from their applications. So why am I posting this? Because GPGPU is, in my opinion...
Confessions of a Speed Junkie (Products)
Confessions of a Speed Junkie (Products). Tuesday, August 18, 2009. CULA is a LAPACK library from EM Photonics. It comes in three flavors: basic, premium, and commercial. For more information http:/ www.culatools.com/. Posted by Dr Zaius. Tuesday, August 18, 2009. Tuesday, August 11, 2009. For more information http:/ www.supermicro.com/products/nfo/gpu.cfm. Posted by Dr Zaius. Tuesday, August 11, 2009. You can build your own GPU cluster with Nvidia S1070 1Us connected to rack mounted servers. You wil...
Confessions of a Speed Junkie (Resources)
Confessions of a Speed Junkie (Resources). Thursday, August 13, 2009. Nvidia has an OpenCL beta download available. Nvidia's OpenCL driver will only allow you to run on Nvidia devices. You must register as a Nvidia developer to gain access. For more information. Http:/ www.nvidia.com/object/cuda opencl.html. Http:/ developer.amd.com/gpu/ATIStreamSDKBetaProgram/Pages/default.aspx. Posted by Dr Zaius. Thursday, August 13, 2009. Http:/ developer.amd.com/gpu/ATIStreamSDK/pages/TutorialOpenCL.aspx. Nvidia web...
Confessions of a Speed Junkie (News)
Confessions of a Speed Junkie (News). Thursday, July 15, 2010. EM Photonics Supports Fermi. EM Photonics, Inc. announced today the general availability of CULA 2.0, its GPU-accelerated linear algebra library used by thousands of developers and scientists worldwide. The new version provides support for NVIDIA GPUs based on the latest "Fermi" architecture, which offers HPC users unprecedented performance in double-precision mathematics, faster memory, and new usability features. Posted by Dr Zaius. Acceler...
Confessions of a Speed Junkie (Code Examples)
Confessions of a Speed Junkie (Code Examples). Friday, October 2, 2009. Matrix Multiplication 3 (OpenCL). The memory model for OpenCL applications is strikingly similar to the CUDA memory model (go figure). The only real differences are that CUDA supports texture memory (we didn't get into that in our CUDA examples) and OpenCL provides for a global / constant memory cache (Nvidias new Fermi GPU. Adds a global / constant memory cache). Global Memory (CUDA Global). Constant Memory (CUDA Constant). While it...
Confessions of a Speed Junkie (Benchmarks)
Confessions of a Speed Junkie (Benchmarks). Friday, August 14, 2009. Binomial Option Model Metrics. The GPU that I am using is an Nvidia C1060 Tesla card. The card is housed in a Dell 690 with a 3.2 GHz Intel Xeon processor, 2G of RAM, and the C1060 installed. The system is running RedHat 5.3 64 bit with Nvidia's 3.0 Beta SDK and appropriate beta driver installed. As you can see from the graph above the fastest version of the model was the CUDA implementation. The CUDA version of the model was 2....What ...
