Accelerating MATLAB with GPU Computing: A Primer with Examples

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/

Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledgeExplains the related background on hardware, architecture and programming for ease of useProvides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

Preface
1. Accelerating MATLAB without GPU                                                                     
2. Configurations for MATLAB and CUDA                                                                
3. Optimization Planning through Profiling
4. CUDA coding with C-MEX
5. MATLAB with Parallel Computing Toolbox
6. Using CUDA-Accelerated Libraries
7. Example in Computer Graphics: 3D Surface Reconstruction using Marching Cubes
8. Example in 3D Image Processing: Atlas-based Segmentation
APPENDIX
 A.1 Download and install CUDA library
 A.2 Installing NVIDIA Nsight into Visual Studio