Async Techniques and Examples in Python Transcripts
Chapter: Parallelism in C with Cython
Lecture: Cython syntax compared

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0:00 I'm guessing most of you have never written Cython code.
0:03 You might not even know what Cython code looks like.
0:07 Maybe if you saw it you could tell me
0:09 okay, I think that's not Python, it might Cython
0:11 but it turns out the language is slightly
0:14 ever so slightly different. So, let's look at its syntax.
0:16 And here's the example we're going to work with.
0:19 We're going to do some computational stuff here.
0:21 The function is called do_math.
0:23 It takes a start and the number of times
0:25 it's going to increment from there.
0:27 And we've seen this before when we did our threading
0:29 and our multiprocessing.
0:30 It's just a silly math function that does useless math
0:33 but it lets us do performance testing
0:35 and parallelism testing and things like that.
0:37 It's using Python 3.5's type annotations.
0:42 So, up there we have start:, num: and so on
0:44 but of course we could omit those
0:45 and things will still work just the same.
0:47 Right, so this is pure Python, this is not Cython.
0:50 I'm going to show you this function converted
0:53 over to Cython. You ready? Keep your eye on it.
0:57 There's the Cython. There's Python. There's Cython.
1:01 Two changes, one, we have more type annotations
1:05 so start in the argument there is no longer an integer
1:08 it's a Cython int.
1:10 In Python, in CPython, integers and numbers and stuff
1:13 they're still basically objects on the heap.
1:16 They're not allocated on the stack as four bytes
1:18 or eight bytes or whatever the size of these are.
1:21 They're actually allocated in the heap
1:23 and there's pointers, and that really slows down math
1:25 and adds a lot of overhead.
1:26 So we can explicitly say that start
1:28 and num are Cython integers
1:30 and that means they're going to be allocated on the stack in C.
1:34 And then we have three local variables
1:35 which we had before but we didn't explicitly say the type.
1:38 Here we're saying explicitly that's a cython.float.
1:41 cython.float, cython.float.
1:42 And then, we're doing our while loop exactly the same.
1:46 So that's the only change.
1:47 We've done a little bit of type annotations
1:50 and we're using a more high performance square root.
1:53 We were using math.sqrt before
1:55 which is Python's standard library.
1:57 Fine, and that would still work in Cython
1:59 but it turns out to be slower and it prohibits
2:02 one of the techniques we want to apply later.
2:04 So we have this libc.math that actually has sqrt
2:08 and a bunch of other math operations
2:10 that you might want to do.
2:11 So we're going to use Cython's built-in
2:14 more high performance math operation. That's it.
2:18 I'll flip back and forth a couple times for you
2:20 one more time.
2:21 Just notice, just a few more type decorations
2:23 and a different square root. Pretty sweet, right?