You can use inplace operators where appropriate to avoid memory allocation. a *= b c += a Kevin From: 腾刘 Hello everyone, I 'm here again to ask a naive question about Numpy performance. As far as I know, Numpy's vectorization operator is very effective because it utilizes SIMD instructions and multi-threads compared to index-style programming (using a "for" loop and assigning each element with its index in array). I 'm wondering how fast Numpy could be so I did some experiments. Take this simple task as an example: a = np.random.rand(10 000 000) b = np.random.rand(10 000 000) c = a + b To check the performance, I wrote a simple C++ implementation of adding two arrays using multi-threads too (with the compile options of: -O3 -mavx2). I found that the C++ implementation is slightly faster than Numpy (running 100 times each to get a rather convincing statistic). Here comes the first question, how come there is this efficiency gap? I guess this is because Numpy needs to load the shared object and find the wrapper of ufunc and then finally execute the underlying computation. Am I right? Am I missing something here? Then I did another experiment for this statement: d = a * b + c , where a, b, c and d are all numpy arrays. I also use C++ to implement this logic and found that C++ is 2 times faster than Numpy on average (also executed 100 times each). I guess this is because in python we first calculate: temporary_var = a * b and then: d = temporary_var + c so we have an unnecessary memory transfer overhead. Since each array is very large, Numpy needs to write temporary_var to memory and then read it back to cache. However in C++ we could just write d[i] = a[i] * b[i] + c[i] and we won't create a temporary array along with the memory transfer penalty. So another problem is if there is a method to avoid this kind of overhead? I 've learned that in Numpy we could create our own ufunc with: frompyfunc, but it seems that there is no SIMD optimization nor multi-threads utilized since this is 100 times slower than "d = a * b + c" way. |
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