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Runtime compiler for numerical Python.

Project description


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

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

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

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)


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

pip install parakeet


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

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

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.reduce)


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:

  • “openmp”: also compiles with gcc, but parallel operators run across multiple cores (default)

  • “c”: lowers all parallel operators to loops, compile sequential code with gcc

  • “cuda”: launch parallel operations on the GPU (experimental)

  • “llvm”: older backend, has fallen behind and some programs may not work

  • “interp” : pure Python intepreter used for debugging optimizations, only try this if you think CPython is about 10,000x too fast for your taste

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