How PyPy can help High Performance Computing

Transcript

1. How PyPy can help High Performance Computing How PyPy can help High Performance Computing
2. Short bio Short bio PyPy core dev since 2006 pdb++, CFFI, vmprof, capnpy, ... @antocuni https://github.com/antocuni (https://github.com/antocuni) https://bitbucket.org/antocuni (https://bitbucket.org/antocuni)
3. How many of you use Python? How many of you use Python?
4. How many have ever had performance problems? How many have ever had performance problems?
5. Why do you use Python, then? Why do you use Python, then?
6. Python strong points Python strong points Simplicity Lots of libraries Ecosystem Ok, but why?
7. Python Python REAL strong points strong points Expressive & simple APIs Uniform typesystem (everything is an object) Powerful abstractions
8. Example: JSON Example: JSON
9. JSONObject jsonObj = new JSONObject(jsonString); JSONArray jArray = jsonObj.getJSONArray("data"); int length = jArray.length(); for(int i=0; i<length; i++) { JSONObject jObj = jArray.getJSONObject(i); String id = jObj.optString("id"); String name=jObj.optString("name"); JSONArray ingredientArray = jObj.getJSONArray("Ingredients"); int size = ingredientArray.length(); ArrayList<String> Ingredients = new ArrayList<>(); for(int j=0; j<size; j++) { JSONObject json = ja.getJSONObject(j); Ingredients.add(json.optString("name")); } } // googled for "getJSONArray example", found this: // https://stackoverflow.com/questions/32624166/how-to-get-json-array-within-j son-object
10. obj = json.loads(string) for item in obj['data']: id = item['id'] name = item['name'] ingredients = [] for ingr in item["ingredients"]: ingredients.append(ingr['name'])
11. So far so good, BUT So far so good, BUT abstraction iterators abstraction temp objects abstraction classes/methods/functions core of computation
12. Example of temporary objects Example of temporary objects Bound methods Bound methods In [ ]: class A(object): def foo(self): return 42 a = A() bound_foo = a.foo %timeit a.foo() %timeit bound_foo()
13. Ideally Ideally Think of concepts, not implementation details Think of concepts, not implementation details Real world Real world Details leak to the user Details leak to the user
14. Python problem Python problem Tension between abstractions and performance Tension between abstractions and performance
15. Classical Python approaches to performance Classical Python approaches to performance
16. 1. Work around in the user code 1. Work around in the user code e.g. create bound methods beforehand e.g. create bound methods beforehand
17. 2. Work around in the language specs 2. Work around in the language specs range vs xrange dict.keys vs .iterkeys int vs long array.array vs list Easier to implement Harder to use Clutter the language unnecessarily More complex to understand Not really Pythonic
18. 3. Stay in C as much as possible 3. Stay in C as much as possible In [29]: In [31]: numbers = range(1000) % timeit [x*2 for x in numbers] import numpy as np numbers = np.arange(1000) % timeit numbers*2 10000 loops, best of 3: 47.1 µs per loop The slowest run took 17.59 times longer than the fastest. This could mean that an intermediate result is being cached. 1000000 loops, best of 3: 1.48 µs per loop
19. 4. Rewrite in C 4. Rewrite in C #include "Python.h" Cython CFFI
20. "Rewrite in C" approach "Rewrite in C" approach aka, 90/10 rule aka, 90/10 rule
24. Abstractions cost Code quality => poor performance Python parts become relevant
25. Python in the HPC world Python in the HPC world Python as a glue-only language Python as a glue-only language Tradeo between speed and code quality Tradeo between speed and code quality
26. PyPy PyPy Alternative Python implementation Ideally: no visible difference to the user JIT compiler http://pypy.org (http://pypy.org)
27. How fast is PyPy? How fast is PyPy? Wrong question Wrong question Up to 80x faster in extreme cases 10x faster in good cases 2x faster on "random" code sometime it's just slower
28. PyPy aws PyPy aws Far from being perfect it leaks other implementation details than CPython e.g. JIT warmup, GC pecularities
29. PyPy qualities PyPy qualities
30. Make pythonic, idiomatic code fast Make pythonic, idiomatic code fast
31. Abstractions are (almost) free Abstractions are (almost) free
32. The better the code, the biggest the speedup The better the code, the biggest the speedup
35. Python as a rst class language Python as a rst class language No longer "just glue" No longer "just glue"
36. Example: Sobel lter Example: Sobel lter Extendend version "The Joy of PyPy: Abstractions for Free", EP 2017 https://speakerdeck.com/antocuni/the-joy-of-pypy-jit-abstractions-for-free (https://speakerdeck.com/antocuni/the-joy-of-pypy-jit-abstractions-for-free) https://www.youtube.com/watch?v=NQfpHQII2cU (https://www.youtube.com/watch?v=NQfpHQII2cU)
37. The The BIG problem: C extensions problem: C extensions CPython CPython
38. PyPy (cpyext) PyPy (cpyext)
40. cpyext cpyext PyPy version of Python.h Compatibility layer Most C extensions just work: numpy, scipy, pandas, etc. Slow :( Use CFFI whenever it's possible
41. We are working on it We are working on it Future status (hopefully) Future status (hopefully) All C extensions will just work C code as fast as today, Python code super-fast The best of both worlds PyPy as the default choice for HPC My personal estimate: 6 months of work and we have a fast cpyext (let's talk about money :))
42. That's all That's all Questions? Questions?