Software Index
Linux Software Utilities  

numexpr

download download home home   report broken
important software information
company name:
David M. Cooke, Tim Hochberg, Francesc Alted, Ivan Vilata
license: Freeware
minimum requirements:
functional limitations:
numexpr description
numexpr is a Python library that evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime.
Why It Works

There are two extremes to array expression evaluation. Each binary operation can run separately over the array elements and return a temporary array. This is what NumPy does: 2*a + 3*b uses three temporary arrays as large as a or b. This strategy wastes memory (a problem if the arrays are large). It is also not a good use of CPU cache memory because the results of 2*a and 3*b will not be in cache for the final addition if the arrays are large.

The other extreme is to loop over each element:

for i in xrange(len(a)):
c[i] = 2*a[i] + 3*b[i]

This conserves memory and is good for the cache, but on each iteration Python must check the type of each operand and select the correct routine for each operation. All but the first such checks are wasted, as the input arrays are not changing.

numexpr uses an in-between approach. Arrays are handled in chunks (the first pass uses 256 elements). As Python code, it looks something like this:

for i in xrange(0, len(a), 256):
r0 = a[i:i+256]
r1 = b[i:i+256]
multiply(r0, 2, r2)
multiply(r1, 3, r3)
add(r2, r3, r2)
c[i:i+256] = r2

The 3-argument form of add() stores the result in the third argument, instead of allocating a new array. This achieves a good balance between cache and branch prediction. The virtual machine is written entirely in C, which makes it faster than the Python above.

For more info about numexpr, read the Numexpr's Overview written by the original author (David M. Cooke).

Examples of Use

Using it is simple:

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

>>> a = np.arange(1e6) # Choose large arrays for high performance
>>> 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)

and fast... :-)

>>> timeit a**2 + b**2 + 2*a*b
10 loops, best of 3: 33.3 ms per loop
>>> timeit ne.evaluate("a**2 + b**2 + 2*a*b")
100 loops, best of 3: 7.96 ms per loop # 4.2x faster than NumPy. . Google's official developer site. Featuring APIs, developer tools and technical resources.
Similar software
Net::Telnet (Popularity: ) : Net::Telnet can interact with TELNET port or other TCP ports.

SYNOPSIS

use Net::Telnet ();
see METHODS section below

Net::Telnet allows you to make client connections to a TCP port and do network I/O, especially to a port using the TELNET protocol. Simple I/O methods ...

New Flow Wave (Popularity: ) : New Flow Wave is a GTK theme changed, customized and designed by Nael Ahmed.


How to install?

· Right click on your desktop and select Change Desktop Background
· Click on the first tab: Theme
· Drag and drop the theme's archive in the ...

User reviews

Write a review:
1 2 3 4 5 6 7 8 9 10
1=poor 10=excellent
Write review*
Your name*
Email*
  (Comments are moderated, and will not appear on this site until the editor has approved them)
 
Rate me
supported os's
stats
downloads 5
version 1.3.1
size in Kb 51
popularity   
1016/1272475
user rating 0/10
our rating 0 Stars
share info
Recommend numexpr
Report spyware
New Software
Popular Software
Latest Reviews