Скачать или смотреть 23.Vectorization part 2

I remember when I first learned about vectorization, I spent many hours on my computer taking an unvectorized version of an algorithm running it, see how long it run, and then running a vectorized version of the code and seeing how much faster that run, and I just spent hours playing with that. And it frankly blew my mind that the same algorithm vectorized would run so much faster. It felt almost like a magic trick to me. In this video, let's figure out how this magic trick really works. Let's take a deeper look at how a vectorized implementation may work on your computer behind the scenes. Let's look at this for loop. The for loop like this runs without vectorization. If j ranges from 0 to say 15, this piece of code performs operations one after another. On the first timestamp which I'm going to write as t0. It first operates on the values at index 0. At the next time-step, it calculates values corresponding to index 1 and so on until the 15th step, where it computes that. In other words, it calculates these computations one step at a time, one step after another. In contrast, this function in NumPy is implemented in the computer hardware with vectorization. The computer can get all values of the vectors w and x, and in a single-step, it multiplies each pair of w and x with each other all at the same time in parallel. Then after that, the computer takes these 16 numbers and uses specialized hardware to add them altogether very efficiently, rather than needing to carry out distinct additions one after another to add up these 16 numbers. This means that codes with vectorization can perform calculations in much less time than codes without vectorization...