并行执行for循环以在多个GPU内核上同时运行? [英] Parallelizing a for loop to run simultaneously on multiple GPU cores?

问题描述

我知道您可以使用matlabpool和parfor并行运行for循环迭代，但是，我想尝试利用GPU中使用的大量内核来运行更大的内核同时迭代的次数.我想知道是否有内置功能可以做到这一点?

I understand that you can use a matlabpool and parfor to run for loop iterations in parallel, however, I want to try and take advantage of using the high number of cores in my GPU to run a larger number of simultaneous iterations. I was wondering if there is any built in functionality to do this?

据我所知，MATLAB在GPU上运行代码的方法是通过GPUarray进行的，但这似乎并没有使循环并行化，只有循环内的某些功能可以实现.

To my understanding, the method in which MATLAB runs code on the GPU is through a GPUarray, but that does not seem to parallelize a loop, only certain functions inside the loop.

对于我正在运行的循环，每个迭代都可以独立运行，并且循环外唯一需要存在的变量是要处理的数据(一个3D数组，其中第一个索引是时间，每个迭代在不同的时间进行操作)和一个二维输出数组，其中每个迭代都将特定时间的结果存储在其中.每次都是独立的.

For the loop that I am running, each iteration can run independently and the only variables that need to exist outside of the loop is the data to be processed (a 3-D array, where the first index is time, and each iteration is operating on a different time) and a 2-D output array where each iteration is storing the result for a particular time. Each time is independent.

谢谢

推荐答案

使用GPUArray，您可以通过根据MATLAB的arrayfun构造算法来并行运行元素操作.有效地，这隐式地遍历了数组的每个元素，并且可以将MATLAB函数的主体应用于每个元素.该文档为:此处.

With a GPUArray, you can run elementwise operations in parallel by structuring your algorithm in terms of MATLAB's arrayfun. Effectively, this implicitly loops over each element of your arrays, and can apply the body of a MATLAB function to each element. The doc is: here.

有一个简单的演示: 查看全文