parakeet 0.22

Runtime compiler for numerical Python.

Parakeet

Parakeet is a runtime accelerator for an array-oriented subset of Python. If you’re doing a lot of number crunching in Python, Parakeet may be able to significantly speed up your code.

To accelerate a function, wrap it with Parakeet’s @jit decorator:

import numpy as np
from parakeet import jit

x = np.array([1,2,3])
y = np.tanh(x * alpha) + beta

@jit
def fast(x, alpha = 0.5, beta = 0.3):
  return np.tanh(x * alpha) + beta

@jit
def loopy(x, alpha = 0.5, beta = 0.3):
  y = np.empty_like(x, dtype = float)
  for i in xrange(len(x)):
    y[i] = np.tanh(x[i] * alpha) + beta
  return y

@jit
def comprehension(x, alpha = 0.5, beta = 0.3):
  return np.array([np.tanh(xi*alpha) + beta for xi in x])

assert np.allclose(fast(x), y)
assert np.allclose(loopy(x), y)
assert np.allclose(comprehension(x), y)

Install

You should be able to install Parakeet from its PyPI package by running:

pip install parakeet

Dependencies

Parakeet is written for Python 2.7 (sorry internet) and depends on:

• dsltools

• nose for unit tests

• NumPy

How does it work?

Your untyped function gets used as a template from which multiple type specializations are generated (for each distinct set of input types). These typed functions are then churned through many optimizations before finally getting translated into native code.

More information

• Read more about Parakeet on the project website

• Ask questions on the discussion group

• Watch the Parakeet presentation from this year’s PyData Boston, look at the HotPar slides from last year

• Contact the main developer directly

Supported language features

Parakeet cannot accelerate arbitrary Python code, it only supports a limited subset of the language:

• Scalar operations (i.e. “x + 3 * y”)

• Control flow (if-statements, loops, etc…)

• Nested functions and lambdas

• Tuples

• Slices

• NumPy array expressions (i.e. “x[1:, :] + 2 * y[:-1, ::2]”)

• NumPy array constructors (i.e. np.ones, np.empty, etc..)

• NumPy ufuncs (i.e. np.sin, np.exp, etc..)

• List literals (interpreted as array construction)

• List comprehensions (interpreted as array comprehensions)

• Parakeet’s “adverbs” (higher order array operations like parakeet.map, parakeet.reduce)

Backends

Parakeet currently supports compilation to sequential C, multi-core C with OpenMP (default), or LLVM (deprecated). To switch between these options change parakeet.config.backend to one of: * “c”: lowers all parallel operators to loops, compile sequential code with gcc * “openmp”: also compiles with gcc, but parallel operators run across multiple cores (default) * “cuda”: launch parallel operations on the GPU (experimental) * “llvm”: older backend, has fallen behind and some programs may not work