Accelerating MATLAB with GPU Computing

Preface

Target Readers and Contents

Directions of this Book

1. Accelerating MATLAB without GPU

1.1 Chapter Objectives

1.2 Vectorization

1.3 Preallocation

1.4 For-Loop

1.5 Consider a Sparse Matrix Form

1.6 Miscellaneous Tips

1.7 Examples

2. Configurations for MATLAB and CUDA

2.1 Chapter Objectives

2.2 MATLAB Configuration for c-mex Programming

2.3 “Hello, mex!” using C-MEX

2.4 CUDA Configuration for MATLAB

2.5 Example: Simple Vector Addition Using CUDA

2.6 Example with Image Convolution

2.7 Summary

3. Optimization Planning through Profiling

3.1 Chapter Objectives

3.2 MATLAB Code Profiling to Find Bottlenecks

3.3 c-mex Code Profiling for CUDA

3.4 Environment Setting for the c-mex Debugger

4. CUDA Coding with c-mex

4.1 Chapter Objectives

4.2 Memory Layout for c-mex

4.3 Logical Programming Model

4.4 Tidbits of GPU

4.5 Analyzing Our First Naïve Approach

5. MATLAB and Parallel Computing Toolbox

5.1 Chapter Objectives

5.2 GPU Processing for Built-in MATLAB Functions

5.3 GPU Processing for Non-Built-in MATLAB Functions

5.4 Parallel Task Processing

5.5 Parallel Data Processing

5.6 Direct use of CUDA Files without c-mex

6. Using CUDA-Accelerated Libraries

6.1 Chapter Objectives

6.2 CUBLAS

6.3 CUFFT

6.4 Thrust

7. Example in Computer Graphics

7.1 Chapter Objectives

7.2 Marching Cubes

7.3 Implementation in MATLAB

7.4 Implementation in c-mex with CUDA

7.5 Implementation Using c-mex and GPU

7.6 Conclusion

8. CUDA Conversion Example: 3D Image Processing

8.1 Chapter Objectives

8.2 MATLAB Code for Atlas-Based Segmentation

8.3 Planning for CUDA Optimization Through Profiling

8.4 CUDA Conversion 1 - Regularization

8.5 CUDA Conversion 2 - Image Registration

8.6 CUDA Conversion Results

8.7 Conclusion

Appendix 1. Download and Install the CUDA Library

A1.1 CUDA Toolkit Download

A1.2 Installation

A1.3 Verification

Appendix 2. Installing NVIDIA Nsight into Visual Studio

Bibliography

Index