High-Performance Python

Python is fast!

• Python is fast to write, but natively 10x - 100x slower than C.

• Python has great C interop, so you can use C for the slow parts.

• This makes Python competitive with C.

Before you try this at home…

• “Premature optimization is the root of all evil.”

• Use external standards for how fast your code needs to be.

• Remember: performance is a tradeoff against readability, maintainability, and developer time.

Part 1: General Optimization

Profile Your Code

• 95%+ of your code is irrelevant to performance.

• A profiler will tells you which 5% is important.

Profile Your CodeIn Python, use cProfile:

source: https://ymichael.com/2014/03/08/profiling-python-with-cprofile.html

https://ymichael.com/2014/03/08/profiling-python-with-cprofile.html

Basics• Make sure your Big-O performance is optimal.

• Move operations outside of loops.

• Use cacheing for repeated calculations.

• Apply algebraic simplifications.

Accidentally QuadraticThe *most* common issue:

def find_intersection(list_one, list_two): intersection = [] for a in list_one: if a in list_two: intersection.append(a) return intersection

Accidentally QuadraticThe *most* common issue:

def find_intersection(list_one, list_two): intersection = [] for a in list_one: if a in list_two: intersection.append(a) return intersection

def find_intersection(list_one, list_two): intersection = [] list_two = set(list_two) for a in list_one: if a in list_two: intersection.append(a) return intersection

Business Logic

Leverage business logic. You’ll often have NP-Complete optimizations to make.

The underlying business reasoning should guide your approximations.

Part II: Python Optimization

Libraries• Use numpy, scipy, pandas, scikit-learn, etc.

• Incredible built-in functionality. If you need something esoteric, try combining built-ins or adapting a more general built-in approach.

• Extremely fast, thoroughly optimized, and best of all, already written.

Pure Python Tips

• Function calls are expensive. Avoid them and avoid recursion.

• Check the runtime of built-in data types.

• Make variables local. Global lookups are expensive.

• Use map/filter/reduce instead of for loops, they’re written in C.

• Vectorize! numpy arrays are much faster than lists.

Mixed Tips

• Vectorize! numpy arrays are much faster than lists.

Mixed Tips

def complex_sum(in_list): in_list = [(a + 2) for a in in_list] # more transformations

return sum(in_list)

def complex_sum(in_list): in_list = np.array(in_list) in_list += 2 # more transformations

return in_list.sum()

Mixed Tips• Vectorize! numpy arrays are much faster than lists.

• Array allocation can be a bottleneck. Try moving it outside of loops.

Mixed Tips• Vectorize! numpy arrays are much faster than lists.

• Array allocation can be a bottleneck. Try moving it outside of loops.

n = 10 ** 3 output = 0

for i in xrange(10**9): result = np.zeros(n) ## calculations ## output += result.sum()

result = np.zeros(10**3) output = 0

for i in xrange(10**9): result[:] = 0 # zero out array ## calculations ## output += result.sum()

• Cython: inline C code directly into Python.

Last Resort: C

def fib(int n): cdef int a, b, temp a = 0 b = 1 while b < n: temp = b b = a + b a = temp


Last Resort: C

def fib(n): a = 0 b = 1 while b < n: temp = b b = a + b a = temp return b


Last Resort: C

def fib(int n): cdef int a, b, temp a = 0 b = 1 while b < n: temp = b b = a + b a = temp return b

Last Resort: C


• C extensions: write C and call it from Python.

Last Resort: C


• C extensions: write C and call it from Python.

• Limit these techniques to hot loops.

Things I haven’t mentioned

• multithreading: basically doesn’t work in Python

• pypy: A Python JIT compiler with a different ecosystem

Warning

Optimization is addictive.

Conclusions• Avoid premature optimizations!

Have objective benchmarks you’re trying to hit.

• Profile your code. You will be surprised by the results.

• The gold standard for performance is highly-tuned C (that’s already been written by someone else)

Resources

• Programming Pearls (Jon Bentley)

• accidentallyquadratic.tumblr.com

• Performance Engineering of Software Systems, 6.172, MIT OpenCourseWare

• cProfile Docs

• Cython Docs

• Guido Van Rossum’s advice:python.org/doc/essays/list2str

General Python Specific

Contact me: [email protected]

High-Performance Python

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