Accelerating MATLAB with GPU Computing - Jung W Suh
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Présentation Accelerating Matlab With Gpu Computing de Jung W Suh Format Broché
- Livre Littérature Générale
Résumé : Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers' projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/
Biographie:
Jung W. Suh is a senior algorithm engineer and research scientist at KLA-Tencor. Dr. Suh received his Ph.D. from Virginia Tech in 2007 for his 3D medical image processing work. He was involved in the development of MPEG-4 and Digital Mobile Broadcasting (DMB) systems in Samsung Electronics. He was a senior scientist at HeartFlow, Inc., prior to joining KLA-Tencor. His research interests are in the fields of biomedical image processing, pattern recognition, machine learning and image/video compression. He has more than 30 journal and conference papers and 6 patents....
Sommaire:
Preface1. Accelerating MATLAB without GPU 2. Configurations for MATLAB and CUDA 3. Optimization Planning through Profiling4. CUDA coding with C-MEX5. MATLAB with Parallel Computing Toolbox6. Using CUDA-Accelerated Libraries 7. Example in Computer Graphics: 3D Surface Reconstruction using Marching Cubes 8. Example in 3D Image Processing: Atlas-based SegmentationAPPENDIX A.1 Download and install CUDA library A.2 Installing NVIDIA Nsight into Visual Studio...
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