numexpr

Fast numerical expression evaluator for NumPy

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andreabedini falted matthew.brett robbmcleod

Project description

Author:: David M. Cooke, Francesc Alted and others
Contact:: faltet@gmail.com
URL:: https://github.com/pydata/numexpr
Documentation:: http://numexpr.readthedocs.io/en/latest/
Travis CI:
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What is NumExpr?

NumExpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like '3*a+4*b') are accelerated and use less memory than doing the same calculation in Python.

In addition, its multi-threaded capabilities can make use of all your cores – which generally results in substantial performance scaling compared to NumPy.

Last but not least, numexpr can make use of Intel’s VML (Vector Math Library, normally integrated in its Math Kernel Library, or MKL). This allows further acceleration of transcendent expressions.

How NumExpr achieves high performance

The main reason why NumExpr achieves better performance than NumPy is that it avoids allocating memory for intermediate results. This results in better cache utilization and reduces memory access in general. Due to this, NumExpr works best with large arrays.

NumExpr parses expressions into its own op-codes that are then used by an integrated computing virtual machine. The array operands are split into small chunks that easily fit in the cache of the CPU and passed to the virtual machine. The virtual machine then applies the operations on each chunk. It’s worth noting that all temporaries and constants in the expression are also chunked. Chunks are distributed among the available cores of the CPU, resulting in highly parallelized code execution.

The result is that NumExpr can get the most of your machine computing capabilities for array-wise computations. Common speed-ups with regard to NumPy are usually between 0.95x (for very simple expressions like 'a + 1') and 4x (for relatively complex ones like 'a*b-4.1*a > 2.5*b'), although much higher speed-ups can be achieved for some functions and complex math operations (up to 15x in some cases).

NumExpr performs best on matrices that are too large to fit in L1 CPU cache. In order to get a better idea on the different speed-ups that can be achieved on your platform, run the provided benchmarks.

Usage

>>> import numpy as np
>>> import numexpr as ne

>>> a = np.arange(1e6)   # Choose large arrays for better speedups
>>> b = np.arange(1e6)

>>> ne.evaluate("a + 1")   # a simple expression
array([  1.00000000e+00,   2.00000000e+00,   3.00000000e+00, ...,
         9.99998000e+05,   9.99999000e+05,   1.00000000e+06])

>>> ne.evaluate('a*b-4.1*a > 2.5*b')   # a more complex one
array([False, False, False, ...,  True,  True,  True], dtype=bool)

>>> ne.evaluate("sin(a) + arcsinh(a/b)")   # you can also use functions
array([        NaN,  1.72284457,  1.79067101, ...,  1.09567006,
        0.17523598, -0.09597844])

>>> s = np.array(['abba', 'abbb', 'abbcdef'])
>>> ne.evaluate("'abba' == s")   # string arrays are supported too
array([ True, False, False], dtype=bool)

Documentation

Please see the official documentation at numexpr.readthedocs.io. Included is a user guide, benchmark results, and the reference API.

Authors

Please see AUTHORS.txt.

License

NumExpr is distributed under the MIT license.

Project details

These details have been verified by PyPI

Maintainers

andreabedini falted matthew.brett robbmcleod

Release history Release notifications | RSS feed

2.10.1

Jun 20, 2024

2.10.0

Apr 2, 2024

2.9.0

Jan 26, 2024

2.8.8

Dec 11, 2023

2.8.7

Sep 26, 2023

2.8.6

Sep 12, 2023

2.8.5

Aug 6, 2023

2.8.4

Oct 26, 2022

2.8.3

Jun 25, 2022

2.8.1

Dec 10, 2021

2.8.0

Dec 2, 2021

2.7.3

Mar 3, 2021

2.7.2

Dec 29, 2020

2.7.1

Jan 5, 2020

2.7.0

Aug 14, 2019

2.6.9

Dec 21, 2018

2.6.8

Aug 19, 2018

This version

2.6.7

Aug 12, 2018

2.6.6

Jul 16, 2018

2.6.6.dev0 pre-release yanked

May 1, 2018

Reason this release was yanked:

Not intended to be released

2.6.5

May 1, 2018

2.6.4

Sep 15, 2017

2.6.3

Sep 14, 2017

2.6.2

Jan 29, 2017

2.6.1

Jul 17, 2016

2.6.0

Jun 1, 2016

2.5.2

Apr 7, 2016

2.5.1

Mar 31, 2016

2.5

Feb 6, 2016

2.4.6

Nov 2, 2015

2.4.5

Nov 2, 2015

2.4.4

Sep 14, 2015

2.4.3

Apr 27, 2015

2.4.2

Apr 22, 2015

2.4.1

Apr 14, 2015

2.4

Apr 13, 2014

2.4-rc2 pre-release

Apr 7, 2014

2.4-rc1 pre-release

Apr 6, 2014

2.3.1

Feb 18, 2014

2.3

Jan 26, 2014

2.3-rc1 pre-release

Jan 24, 2014

2.2.2

Sep 27, 2013

2.2.1

Sep 8, 2013

2.2

Aug 31, 2013

2.1

Apr 27, 2013

2.0.1

Jan 8, 2012

2.0

Nov 27, 2011

1.4.2

Jan 25, 2011

1.4.1

Oct 20, 2010

1.4

Aug 1, 2010

1.3.1

Jun 23, 2009

1.3

Jun 2, 2009

1.2

Feb 18, 2009

1.1.1

Jan 23, 2009

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numexpr 2.6.7

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