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+.
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.
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
- NumPy array expressions (i.e. x[1:, :] + 2 * y[:-1, ::2])
- Some NumPy library functions like np.ones and np.sin (look at the mappings module for a full list)
- List literals (interpreted as array construction)
- List comprehensions (interpreted as array comprehensions)
- Parakeet’s higher order array operations like parakeet.imap, parakeet.scan, and parakeet.allpairs
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”: compiles with gcc, 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