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Confessions of a Speed Junkie (Code Examples) | gpgpu-computing4.blogspot.com Reviews
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Confessions of a Speed Junkie (Code Examples): CUDA Program Structure
http://gpgpu-computing4.blogspot.com/2009/08/cuda-program-structure.html
Confessions of a Speed Junkie (Code Examples). Tuesday, August 25, 2009. CUDA’s parallel programming model is designed to overcome the many challenges of parallel programming while providing a quick learning curve for programmers familiar with C. At its core are three abstractions: a hierarchy of thread groups, shared memory, and thread synchronization. These abstractions are exposed to the programmer via a small set of language extensions. C for CUDA provides a minimal set of extensions to the C languag...
Confessions of a Speed Junkie (Code Examples): Matrix Multiplication 1 (OpenCL)
http://gpgpu-computing4.blogspot.com/2009/09/matrix-multiplication-1.html
Confessions of a Speed Junkie (Code Examples). Tuesday, September 22, 2009. Matrix Multiplication 1 (OpenCL). I will assume that you have already downloaded and installed the appropriate CUDA driver, toolkit and SDK from Nvidia. You can get your hands on Nvidia's beta OpenCL at http:/ www.nvidia.com/object/cuda opencl.html. Main program (Listing 1). Multiply two matrices A * B = C. Allocates a matrix with random float entries. Void randomInit(float* data, int size). For (int i = 0; i. Set seed for rand().
Confessions of a Speed Junkie (Code Examples): Matrix Multiplication 2 (CUDA)
http://gpgpu-computing4.blogspot.com/2009/08/matrix-multiplication-2.html
Confessions of a Speed Junkie (Code Examples). Monday, August 31, 2009. Matrix Multiplication 2 (CUDA). In Matrix Multiplication 1. We learned how to use CUDA to offload a SIMD algorithm to the GPU. We picked a trivial example just to get our feet wet. Now let’s make it a bit more complicated. Instead of multiplying 3 by 3 matrices together we are going to multiply 1024 by 1024 matrices together. You might ask… how is this more complicated? Main program (Listing 1). Multiply two matrices A * B = C. 3 pri...
Confessions of a Speed Junkie (Code Examples): Matrix Multiplication 3 (OpenCL)
http://gpgpu-computing4.blogspot.com/2009/10/matrix-multiplication-3-opencl.html
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 (Code Examples): OpenCL Compiler (OCLTools)
http://gpgpu-computing4.blogspot.com/2009/09/opencl-compiler-oclcc.html
Confessions of a Speed Junkie (Code Examples). Friday, September 18, 2009. Why would a vendor compile to an intermediate representation instead of a device specific binary? This allows OpenCL vendors a greater degree of flexibility in forward and backward application compatibility as the underlying hardware implementations change. So what does this lack of compiler mean to you the developer? What if my product is video encoding / decoding software? Posted by Dr Zaius. Friday, September 18, 2009.
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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 ...
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パーツ 周辺機器は 5,000(税込). Windows 7 Home Premium. Windows 7 Home Premium. Windows 7 Home Preimum. Windows 7 Home Premium. Windows 7 Home Premium. Windows 7 Home Premium. Windows 7 Home Premium. Windows 7 Home Premium. Windows 7 Home Premium. 自作セット パーツの犬モデル にGTX980シリーズ搭載の 008 が登場. 自作セット パーツの犬モデル にMini-ITX採用の 007 が登場. 001と同等のスペックを、Mini-ITXマザーボードと容量15.5Lのコンパクトキューブに凝縮 コンパクトで3Dゲームも楽しめるPCを自作した い人に要注目な本モデルの魅力を見てみました。 PC初心者に最適な 自作セット パーツの犬モデル 001 が登場. この001は、8万円台で新生FFXIVが とても快適 最高設定 な高コストパフォーマンスが魅力の人気NO.1モデルです。 2015年...
gpgpu.net :: gpgpu.net
GPGPUNET에 오신 것을 환영합니다. 앞으로 GPGPU 기술에 대한 트렌드 및 기술에 대한 내용들을 포스팅 할 예정이오니,. 같이 커뮤니티나 스터디를 결성하고 싶으신 분은 댓글로 알려주시면 감사하겠습니다. 이전 달의 달력을 보여줍니다. 다음 달의 달력을 보여줍니다. S Blog is powered by Daum.
GPGPU.org :: General-Purpose computation on Graphics Processing Units
General-Purpose Computation on Graphics Hardware. CUDA and OpenCL Training Courses 2015. June 11th, 2015. 8220;Acceleware offers industry leading training courses for software developers looking to increase their skills in writing or optimizing applications for highly parallel processing. The training focuses on using GPUs for computing and the associated popular programming languages. Clients will access our top rated training techniques for parallel programming. Acceleware’s Training Courses 2015.
GPGPU.RU: Новые публикации | GPGPU.ru
Skip to main content. Использование видеокарт для вычислений. Fri, 10/05/2012 - 22:33. Движок gpgpu.ru был обновлен на свежую версию (Drupal 7). В связи с этим:. Что-то может не работать, пишите. Временно (на несколько дней) не работает вход через Facebook, будет скоро исправлено. Комментарии от анонимов, даже прошедшие через антиспам, премодерируются. Комментарии от зарегистрированных пользователей - публикуются сразу (либо сразу же выкидываются антиспамом). Tue, 08/28/2012 - 12:40. Стоит отметить, что ...
