3 ms·
Interesting find about the indexing. I just had the opposite experience, swapped from numpy to torch in a project and got 2000x speedup on some indexing and bas
by sideshowb 5y ago
Interesting find about the indexing. I just had the opposite experience, swapped from numpy to torch in a project and got 2000x speedup on some indexing and basic maths wrapped in autodiff. And I haven't moved it onto cuda yet.
- nuisance-bear 5y agoHere's an example that illustrates the phenomenon. If memory serves me right, index latency is superlinear in dimension count. import time, torch from itertools import product N = 100 ten = torch.randn(N,N,N) arr = ten.numpy() def indexTimer(val): start = time.time() for i,j,k in product(range(N), range(N), range(N)): x = val[i, j, k] end = time.time() print('{:.2f}'.format(end-start)) indexTimer(ten) indexTimer(arr)
- sideshowb 5y agoAh, I'm guessing it's the loop that kills you. Arrays/tensors are supposed to be used as a whole, I imagine e.g. x=val**2 would be much faster. I'm indexing one array with another e.g. x=y[z] to pull out all the values I want at once into another array for processing. And not using a python loop for the expensive part.
- deleted 5y ago[deleted]