vectorization

问题 I want to optimize my code for vectorization using -msse2 -ftree-vectorizer-verbose=2. I have the following simple code: int main(){ int a[2048], b[2048], c[2048]; int i; for (i=0; i<2048; i++){ b[i]=0; c[i]=0; } for (i=0; i<2048; i++){ a[i] = b[i] + c[i]; } return 0; } Why do I get the note test.cpp:10: note: not vectorized: not enough data-refs in basic block. Thanks! 回答1: For what it's worth, after adding an asm volatile("": "+m"(a), "+m"(b), "+m"(c)::"memory"); near the end of main , my

问题 I need to implement this following function with NumPy - where F_l(x) are N number of arrays that I need to calculate, which are dependent on an array G(x) , that I am given, and A_j are N coefficients that are also given. I would like to implement it in NumPy as I would have to calculate F_l(x) for every iteration of my program. The dummy way to do this is by for loops and ifs: import numpy as np A = np.arange(1.,5.,1) G = np.array([[1.,2.],[3.,4.]]) def calcF(G,A): N = A.size print A print

问题 Is there any way to make this work without for loops? import import numpy as np import matplotlib.pyplot as plt L = 1 N = 255 dh = 2*L/N dh2 = dh*dh phi_0 = 1 c = int(N/2) r_0 = L/2 arr = np.empty((N, N)) for i in range(N): for j in range(N): arr[i, j] = phi_0 if (i - c)**2 + (j - c)**2 < r_0**2/dh2 else 0 plt.imshow(arr) I've tried calling function(x[None,:], y[:, None]), where: function(i, j): return phi_0 if (i - c)**2 + (j - c)**2 < r_0**2/dh2 else 0 but it requires list .any or .all

问题 I have a matrix [1 2 3 6 7 1 2 1] and would like to remove mirror imaged pairs..i.e. output would be either: [1 2 3 6 7 1] or [3 6 7 1 2 1] Is there a simple way to do this? I can imagine a complicated for loop, something like (or a version which wouldn't delete the original pair..only the duplicates): for i=1:y var1=(i,1); var2=(i,2); for i=1:y if array(i,1)==var1 && array(i,2)==var2 | array(i,1)==var2 && array(i,2)==var1 array(i,1:2)=[]; end end end thanks 回答1: How's this for simplicity - A

问题 I have array and need max of rolling difference with dynamic window. a = np.array([8, 18, 5,15,12]) print (a) [ 8 18 5 15 12] So first I create difference by itself: b = a - a[:, None] print (b) [[ 0 10 -3 7 4] [-10 0 -13 -3 -6] [ 3 13 0 10 7] [ -7 3 -10 0 -3] [ -4 6 -7 3 0]] Then replace upper triangle matrix to 0: c = np.tril(b) print (c) [[ 0 0 0 0 0] [-10 0 0 0 0] [ 3 13 0 0 0] [ -7 3 -10 0 0] [ -4 6 -7 3 0]] Last need max values per diagonal, so it means: max([0,0,0,0,0]) = 0 max([-10,13

问题 I have array and need max of rolling difference with dynamic window. a = np.array([8, 18, 5,15,12]) print (a) [ 8 18 5 15 12] So first I create difference by itself: b = a - a[:, None] print (b) [[ 0 10 -3 7 4] [-10 0 -13 -3 -6] [ 3 13 0 10 7] [ -7 3 -10 0 -3] [ -4 6 -7 3 0]] Then replace upper triangle matrix to 0: c = np.tril(b) print (c) [[ 0 0 0 0 0] [-10 0 0 0 0] [ 3 13 0 0 0] [ -7 3 -10 0 0] [ -4 6 -7 3 0]] Last need max values per diagonal, so it means: max([0,0,0,0,0]) = 0 max([-10,13

问题 I want to improve the performance of the for loop in this function. import numpy as np import random def play_game(row, n=1000000): """Play the game! This game is a kind of random walk. Arguments: row (int[]): row index to use in the p matrix for each step in the walk. Then length of this array is the same as n. n (int): number of steps in the random walk """ p = np.array([[ 0.499, 0.499, 0.499], [ 0.099, 0.749, 0.749]]) X0 = 100 Y0 = X0 % 3 X = np.zeros(n) tempX = X0 Y = Y0 for j in range(n)

问题 I have a large 10,000,000+ length array that contains rows. I need to individually shuffle those rows. For example: [[1,2,3] [1,2,3] [1,2,3] ... [1,2,3]] to [[3,1,2] [2,1,3] [1,3,2] ... [1,2,3]] I'm currently using map(numpy.random.shuffle, array) But it's a python (not NumPy) loop and it's taking 99% of my execution time. Sadly, the PyPy JIT doesn't implement numpypy.random , so I'm out of luck. Is there any faster way? I'm willing to use any library ( pandas , scikit-learn , scipy , theano

问题 I'm looking to calculate highly parallelized trig functions (in block of like 1024), and I'd like to take advantage of at least some of the parallelism that modern architectures have. When I compile a block for(int i=0; i<SIZE; i++) { arr[i]=sin((float)i/1024); } GCC won't vectorize it, and says not vectorized: relevant stmt not supported: D.3068_39 = __builtin_sinf (D.3069_38); Which makes sense to me. However, I'm wondering if there's a library to do parallel trig computations. With just a

问题 I recently started working with the Kinect V2 on Linux with pylibfreenect2. When I first was able to show the depth frame data in a scatter plot I was disappointed to see that none of the depth pixels seemed to be in the correct location. Side view of a room (notice that the ceiling is curved). I did some research and realized there's some simple trig involved to do the conversions. To test I started with a pre-written function in pylibfreenect2 which accepts a column, row and a depth pixel