Poss_arr = np.zeros((poss.shape, poss.shape), dtype=np.float64) Radii_arr = np.zeros((radii.shape, radii.shape), dtype=np.float64)
Poss = ::1], float64]))(float64, float64)", parallel=True)ĭef distances_numba_parallel(radii, poss): For example, when I was testing on ~12000 data volume, it took less than one second for each of the calculation section and the NetworkX graph creation and for ~550000 data volume, it took around 25 minutes for calculation section ( numba part) and 7 seconds for graph creation and getting the output list. Now, it is memory-friendly but consumes a much time to calculate these distances ( it consumes just ~10-20% of 16GB memory and ~30-40% of each CPU cores of my 4-cores CPU machine). So, I wrote a non-parallel numba code using lists to do so. This part of the code (calculation section (the numba function) -> finding distances and connections) led to memory leaks when using arrays in parallel scheme by numba (I had this problem when using np.linalg or, too). For doing so, I need to find distances between the spheres before using the graph.
I have written a code to find connected spheres paths using NetworkX library in Python.
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