Given
the above attempt to use prange crashes, my question stands:

What is the correct way ( using prange or an alternative method ) to parallelize this Python for-loop?

如下所述，在 20-omp-threads 上運行類似的 C++ 循環并獲得 8 倍 加速是微不足道的.必須有一種使用 Numba 的方法，因為 for 循環是令人尷尬的并行(并且因為稀疏矩陣向量乘法是科學計算中的基本操作).

As noted below, it was trivial to parallelize a similar for loop in C++ and obtain an 8x speedup, having been run on 20-omp-threads. There must be a way to do it using Numba, since the for loop is embarrassingly parallel (and since sparse matrix-vector multiplication is a fundamental operation in scientific computing).

編輯 2:
這是我的 csrMult() 的 C++ 版本.在 C++ 版本中并行化 for() 循環使我的測試中的代碼快了大約 8 倍.這向我表明，在使用 Numba 時，Python 版本應該可以實現類似的加速.

Edit 2:
Here is my C++ version of csrMult(). Parallelizing the for() loop in the C++ version makes the code about 8x faster in my tests. This suggests to me that a similar speedup should be possible for the Python version when using Numba.

void csrMult(VectorXd& Ax, VectorXd& x, vector<double>& Adata, vector<int>& Aindices, vector<int>& Aindptr)
{
    // This code assumes that the size of Ax is numRowsA.
    #pragma omp parallel num_threads(20)
    {       
        #pragma omp for schedule(dynamic,590) 
        for (int i = 0; i < Ax.size(); i++)
        {
            double Ax_i = 0.0;
            for (int dataIdx = Aindptr[i]; dataIdx < Aindptr[i + 1]; dataIdx++)
            {
                Ax_i += Adata[dataIdx] * x[Aindices[dataIdx]];
            }

            Ax[i] = Ax_i;
        }
    }
}

推薦答案

Numba 已經更新，prange() 現在可以使用了！ _{(我在回答我自己的問題.)}

Numba has been updated and prange() works now! _{(I'm answering my own question.)}

本博文，日期為 2017 年 12 月 12 日.以下是博客的相關片段:

The improvements to Numba's parallel computing capabilities are discussed in this blog post, dated December 12, 2017. Here is a relevant snippet from the blog:

很久以前(超過 20 個版本！)，Numba 曾經支持編寫名為 prange() 的并行循環的習慣用法.大一之后在 2014 年重構代碼庫，這個特性不得不被移除，但它一直是最常被請求的 Numba 功能之一從那之后.英特爾開發人員并行化陣列后表達，他們意識到帶回 prange 將是公平的容易

Long ago (more than 20 releases!), Numba used to have support for an idiom to write parallel for loops called prange(). After a major refactoring of the code base in 2014, this feature had to be removed, but it has been one of the most frequently requested Numba features since that time. After the Intel developers parallelized array expressions, they realized that bringing back prange would be fairly easy

使用 Numba 版本 0.36.1，我可以使用以下簡單代碼并行化我令人尷尬的并行 for-循環:

Using Numba version 0.36.1, I can parallelize my embarrassingly parallel for-loop using the following simple code:

@numba.jit(nopython=True, parallel=True)
def csrMult_parallel(x,Adata,Aindices,Aindptr,Ashape): 

    numRowsA = Ashape[0]    
    Ax = np.zeros(numRowsA)

    for i in numba.prange(numRowsA):
        Ax_i = 0.0        
        for dataIdx in range(Aindptr[i],Aindptr[i+1]):

            j = Aindices[dataIdx]
            Ax_i += Adata[dataIdx]*x[j]

        Ax[i] = Ax_i            

    return Ax

在我的實驗中，并行化 for 循環使函數的執行速度比我在問題開頭發布的版本快大約八倍，該版本已經使用 Numba，但未并行化.此外，在我的實驗中，并行版本比使用 scipy 的稀疏矩陣向量乘法函數的命令 Ax = A.dot(x) 快大約 5 倍.Numba 已經碾壓了 scipy，我終于有了一個 與 MATLAB 一樣快的 Python 稀疏矩陣向量乘法例程.

In my experiments, parallelizing the for-loop made the function execute about eight times faster than the version I posted at the beginning of my question, which was already using Numba, but which was not parallelized. Moreover, in my experiments the parallelized version is about 5x faster than the command Ax = A.dot(x) which uses scipy's sparse matrix-vector multiplication function. Numba has crushed scipy and I finally have a python sparse matrix-vector multiplication routine that is as fast as MATLAB.

這篇關于使用 Numba 時如何并行化此 Python for 循環的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！