GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development.
1. Chapter 1: Introduction - p. xvi 2. Chapter 2: Getting started - p. 19 3. Chapter 3: Parallel Computing Toolbox - p. 35 4. Chapter 4: Introduction to GPU programming in MATLAB - p. 70 5. Chapter 5: GPU programming on MATLAB toolboxes - p. 107 6. Chapter 6: Multiple GPUs - p. 170 7. Chapter 7: Run CUDA or PTX code - p. 198 8. Chapter 8: MATLAB MEX functions containing CUDA code - p. 218 9. Chapter 9: CUDA-accelerated libraries - p. 240 10. Chapter 10: Profiling code and improving GPU performance - p. 268 11. References - p. 295 12. List of Examples - p. 297 13. Index - p. 301
GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development.
1. Chapter 1: Introduction - p. xvi 2. Chapter 2: Getting started - p. 19 3. Chapter 3: Parallel Computing Toolbox - p. 35 4. Chapter 4: Introduction to GPU programming in MATLAB - p. 70 5. Chapter 5: GPU programming on MATLAB toolboxes - p. 107 6. Chapter 6: Multiple GPUs - p. 170 7. Chapter 7: Run CUDA or PTX code - p. 198 8. Chapter 8: MATLAB MEX functions containing CUDA code - p. 218 9. Chapter 9: CUDA-accelerated libraries - p. 240 10. Chapter 10: Profiling code and improving GPU performance - p. 268 11. References - p. 295 12. List of Examples - p. 297 13. Index - p. 301
