Runtime compiler for numerical Python.

## Project description

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:

```python

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](https://pypi.python.org/pypi/parakeet/) by running:

pip install parakeet

Dependencies

====

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

* [treelike](https://github.com/iskandr/treelike)

* [nose](https://nose.readthedocs.org/en/latest/) for unit tests

* [NumPy and SciPy](http://www.scipy.org/install.html)

Optional (if using the LLVM backend):

* [llvmpy](http://www.llvmpy.org/#quickstart)

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](http://www.parakeetpython.com)

* Ask questions on the [discussion group](http://groups.google.com/forum/#!forum/parakeet-python)

* Watch the [Parakeet presentation](https://vimeo.com/73895275) from this year's [PyData Boston](http://pydata.org/bos2013), look at the [HotPar slides](https://www.usenix.org/conference/hotpar12/parakeet-just-time-parallel-accelerator-python) from last year

* Contact the [main developer](http://www.rubinsteyn.com) 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 C or LLVM. To switch between these options change `parakeet.config.default_backend` to either "c" or "llvm".

====

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:

```python

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](https://pypi.python.org/pypi/parakeet/) by running:

pip install parakeet

Dependencies

====

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

* [treelike](https://github.com/iskandr/treelike)

* [nose](https://nose.readthedocs.org/en/latest/) for unit tests

* [NumPy and SciPy](http://www.scipy.org/install.html)

Optional (if using the LLVM backend):

* [llvmpy](http://www.llvmpy.org/#quickstart)

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](http://www.parakeetpython.com)

* Ask questions on the [discussion group](http://groups.google.com/forum/#!forum/parakeet-python)

* Watch the [Parakeet presentation](https://vimeo.com/73895275) from this year's [PyData Boston](http://pydata.org/bos2013), look at the [HotPar slides](https://www.usenix.org/conference/hotpar12/parakeet-just-time-parallel-accelerator-python) from last year

* Contact the [main developer](http://www.rubinsteyn.com) 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 C or LLVM. To switch between these options change `parakeet.config.default_backend` to either "c" or "llvm".

## Project details

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Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
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parakeet-0.18.tar.gz (216.8 kB) Copy SHA256 hash SHA256 | Source | None | Oct 31, 2013 |