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LLVM JIT compiler as a function decorator

Project description

fastpy

Documentation Status Updates

Python made fast. Decorate your functions with @fast, we will infered the types you used, compile to machine code, and execute.

Biased test showing how fast fastpy is:

Initial code:

def long_loop(a):
  for i in range(100000):
    for j in range(10000):
      a += 1
return a
print long_loop(0)
$ time python loop.py
1000000000
python test.py  39.24s user 0.01s system 99% cpu 39.420 total
$ time pypy loop.py
1000000000
pypy test.py  0.92s user 0.01s system 99% cpu 0.937 total

Now we modify the code to use fastpy

from fastpy import fast

@fast
def long_loop(a):
  for i in range(100000):
    for j in range(10000):
      a += 1
  return a
print long_loop(0)
$  time python loop.py
1000000000
python test.py  0.11s user 0.00s system 99% cpu 0.117 total

Credits

Based on this tutorial http://dev.stephendiehl.com/numpile/

History

0.1.0 (2016-07-09)

  • First release on PyPI.

Project details


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This version
History Node

0.1.1

History Node

0.1.0

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Filename, size & hash SHA256 hash help File type Python version Upload date
fastpy-0.1.1-py2.7.egg (36.8 kB) Copy SHA256 hash SHA256 Egg 2.7 Jul 11, 2016
fastpy-0.1.1-py2.py3-none-any.whl (17.1 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Jul 11, 2016
fastpy-0.1.1.tar.gz (23.0 kB) Copy SHA256 hash SHA256 Source None Jul 11, 2016

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