CUDA 和 MATLAB 用于循环优化
我将尝试使用 CUDA 优化用 MATLAB 编写的一些代码。我最近开始进行 CUDA 编程，但我对它的工作原理有了一个大概的了解。
所以，假设我想将两个矩阵相加。在 CUDA 中，我可以编写一个算法，利用线程来计算结果矩阵中每个元素的答案。然而，这种技术是不是可能与 MATLAB 已经采用的技术类似？那么，效率岂不是与技术无关，只取决于硬件层面吗？
I'm going to attempt to optimize some code written in MATLAB, by using CUDA. I recently started programming CUDA, but I've got a general idea of how it works.
So, say I want to add two matrices together. In CUDA, I could write an algorithm that would utilize a thread to calculate the answer for each element in the result matrix. However, isn't this technique probably similar to what MATLAB already does? In that case, wouldn't the efficiency be independent of the technique and attributable only to the hardware level?
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该技术可能类似，但请记住，对于 CUDA，您有数百个线程同时运行。如果 MATLAB 使用线程并且这些线程在四核上运行，则每个时钟周期只能执行 4 个线程，而在同一时钟周期内可能会在 CUDA 上运行数百个线程。
所以回答你的问题，是的，这个例子中的效率与技术无关，仅归因于硬件。
The technique might be similar but remember with CUDA you have hundreds of threads running simultaneously. If MATLAB is using threads and those threads are running on a Quad core, you are only going to get 4 threads excuted per clock cycle while you might achieve a couple of hundred threads to run on CUDA with that same clock cycle.
So to answer you question, YES, the efficiency in this example is independent of the technique and attributable only to the hardware.
答案是明确的，所有的效率都是硬件级别的。我不知道matlab到底是如何工作的，但是CUDA的优点是可以同时执行多个线程，这与matlab不同。
顺便说一句，如果您的问题很小，或者需要很多读写操作，CUDA 可能只会带来额外的麻烦。
The answer is unequivocally yes, all the efficiencies are hardware level. I don't how exactly matlab works, but the advantage of CUDA is that mutltiple threads can be executed simultaneously, unlike matlab.
On a side note, if your problem is small, or requires many read write operations, CUDA will probably only be an additional headache.
CUDA对matlab有官方支持。
[需要链接]
您可以利用 mex 文件从 MATLAB 在 GPU 上运行。
瓶颈在于数据从 CPU-RAM 传输到 GPU 的速度。因此，如果传输被最小化并以大块的形式完成，则加速效果会很大。
CUDA has official support for matlab.
[need link]
You can make use of mex files to run on GPU from MATLAB.
The bottleneck is the speed at which data is transfered from CPU-RAM to GPU. So if the transfer is minimized and done in large chunks, the speedup is great.
对于简单的事情，最好使用 Matlab PCT 中的 gpuArray 支持。你可以在这里查看
http://www.mathworks.de/de/help/distcomp/using -gpuarray.html
对于添加 gpuArray、乘法、最小值、最大值等操作，他们使用的实现往往没问题。我确实发现，为了进行像abs(y-Hx).^2这样的小矩阵的批量操作之类的事情，你最好编写一个小内核来为你做这件事。
For simple things, it's better to use the gpuArray support in the Matlab PCT. You can check it here
http://www.mathworks.de/de/help/distcomp/using-gpuarray.html
For things like adding gpuArrays, multiplications, mins, maxs, etc., the implementation they use tends to be OK. I did find out that for making things like batch operations of small matrices like abs(y-Hx).^2, you're better off writing a small Kernel that does it for you.