GPU加速MATLAB实战：以实例为引导

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"本书《Accelerating MATLAB with GPUs: A Primer with Examples》由Jung W. Suh和Youngmin Kim合著，是关于利用GPU加速MATLAB计算的一本实用指南，对于进行相关研究的读者非常有帮助。" 在MATLAB中使用GPU（图形处理器）计算可以显著提升计算速度，特别是在处理大数据集和复杂算法时。MATLAB支持CUDA和OpenCL等GPU编程框架，使得科研人员和工程师无需深入底层硬件细节，就能利用GPU的强大并行计算能力。本书详细介绍了如何在MATLAB环境中集成GPU计算，从而提高代码执行效率。书中可能涵盖以下关键知识点： 1. **GPU并行计算基础**：介绍GPU的架构和并行计算的基本概念，包括CUDA核心、流式多处理器（SM）、内存层次以及并行线程执行模型。 2. **MATLAB与GPU交互**：解释MATLAB的GPU支持如何工作，包括如何创建和管理GPU数组，以及如何在CPU和GPU之间传输数据。 3. **GPU函数库**：讨论MATLAB中的GPU函数库，如cuFFT（用于快速傅里叶变换）、cuBLAS（用于线性代数运算）和cuSPARSE（用于稀疏矩阵操作）等，并展示如何在MATLAB代码中使用它们。 4. **并行编程模型**：解释如何在MATLAB中实现数据并行和任务并行，包括使用parfor循环、GPU工作队列和并行计算工具箱。 5. **案例研究**：通过实际例子展示GPU加速的效果，可能包括图像处理、数值模拟、机器学习和大数据分析等领域的应用。 6. **性能优化**：探讨如何最大限度地利用GPU的并行计算能力，包括数据布局优化、减少数据传输、选择合适的并行化策略等。 7. **错误调试和性能分析**：介绍如何调试GPU代码，使用MATLAB的性能分析工具来监控和改进代码性能。 8. **硬件选择与兼容性**：讨论不同GPU型号和配置对MATLAB性能的影响，以及如何根据需求选择合适的GPU硬件。 9. **未来发展趋势**：可能涵盖新的GPU技术发展，以及MATLAB在GPU计算方面的未来发展方向。通过本书，读者将能够掌握在MATLAB中有效地利用GPU进行计算的基本技能，提升自己的计算效率，从而在科学研究和工程应用中取得更快的计算结果。无论是初学者还是经验丰富的MATLAB用户，都能从本书中获益匪浅，提升其在GPU并行计算领域的专业知识。

1.6 Miscellaneous Tips

1.6.1 Minimize File Read/Write Within the Loop

Calling functions involving file read/write is very expensive. Therefore, we should

try to minimize or avoid its use especially within a loop. The best way for handling

file read/write is to read files and store data into variables before the loop, and then

use those stored variables inside the loop. After you exit the loop, write the results

back to the file.

It seems simple and obvious to avoid file reading/writing within loop, but when

we use many functions over multiple files, the file reading/writing parts can inad-

vertently reside within the deep inner loop. To detect this inadvertent mistake, we

recommend the profiler to analyze which parts of a code significantly affect the

total processing time. We deal with the use of a profiler in Chapter 3.

1.6.2 Minimize Dynamically Changing the Path and Changing

the Variable Class

Similar to the file reading/writing, setting a path is also expensive. So it should be

minimized. It is also recommended to set the path outside the loop. Changing the

variable/vector/matrix class is also expensive. So it should also be minimized, espe-

cially within loops.

1.6.3 Maintain a Balance Between the Code Readability

and Optimization

Although we emphasize the code optimization so far, the code maintenance is a

very important factor for designing/prototyping algorithm. Therefore, it is not

always a good idea to trade off code readability with heavy optimizations. Please

try to make a balance between code readability and optimization, from which we

can get more benefits by saving costly debugging times while spending more on

creative algorithms and living a happier life.

1.7 Examples

When you run myDisplay.m, you see a figure like Figure 1.1. In this example,

we are going to search for a black book (template image on the left) out of 12

objects (right). Since both the template and object images are in color, the dimen-

sions of both images are three dimensions (x, y, and color). Although the original

size of the object image on the right is 1536 3 2048 3 3 and the size of the

template image on the left is 463 3 463 3 3, each object in the right image is

picked already to have the same size as the template image (

Figure 1.2) to reduce

10 Accelerating MATLAB with GPU Computing

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