新人不懂规矩，发现有100M限制，还不能上传.7z......


在论坛呆了一段时间，也决定自己发一篇~
这本书介绍了1001个加速MATLAB的小技巧，目录清晰，768页完整版。有比较热的GPU加速方法，而且比别的人便宜哦~

The MATLAB® programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow.

Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with thousands of helpful tips, it leaves no stone unturned, discussing every aspect of MATLAB.

Ideal for novices and professionals alike, the book describes MATLAB performance in a scale and depth never before published. It takes a comprehensive approach to MATLAB performance, illustrating numerous ways to attain the desired speedup.

The book covers MATLAB, CPU, and memory profiling and discusses various tradeoffs in performance tuning. It describes both the application of standard industry techniques in MATLAB, as well as methods that are specific to MATLAB such as using different data types or built-in functions.

The book covers MATLAB vectorization, parallelization (implicit and explicit), optimization, memory management, chunking, and caching. It explains MATLAB’s memory model and details how it can be leveraged. It describes the use of GPU, MEX, FPGA, and other forms of compiled code, as well as techniques for speeding up deployed applications. It details specific tips for MATLAB GUI, graphics, and I/O. It also reviews a wide variety of utilities, libraries, and toolboxes that can help to improve performance.

Sufficient information is provided to allow readers to immediately apply the suggestions to their own MATLAB programs. Extensive references are also included to allow those who wish to expand the treatment of a particular topic to do so easily.

Supported by an active website, and numerous code examples, the book will help readers rapidly attain significant reductions in development costs and program run times.

Table of Contents

Chapter 1: Introduction to Performance Tuning
Chapter 2: Profiling MATLAB® Performance
Chapter 3: Standard Performance-Tuning Techniques
Chapter 4: MATLAB®-Specific Techniques
Chapter 5: Implicit Parallelization (Vectorization and Indexing)
Chapter 6: Explicit Parallelization Using MathWorks Toolboxes
Chapter 7: Explicit Parallelization by Other Means
Chapter 8: Using Compiled Code
Chapter 9: Memory-Related Techniques
Chapter 10: Graphics and GUI
Chapter 11: I/O Techniques

Appendix A: Additional Resources
Appendix B: Performance Tuning Checklist