Runtime compiler for numerical Python.
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)
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:
The default backend (which uses OpenMP) requires gcc 4.4+.
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.
Parakeet cannot accelerate arbitrary Python code, it only supports a limited subset of the language:
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: