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parakeet 0.19

Runtime compiler for numerical Python.

Latest Version: 0.23.2


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:

* [treelike](
* [nose]( for unit tests
* [NumPy and SciPy](

Optional (if using the LLVM backend):

* [llvmpy](

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](!forum/parakeet-python)
  * 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.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:
  * "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
File Type Py Version Uploaded on Size
parakeet-0.19.tar.gz (md5) Source 2013-11-05 220KB
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