http://d.puremagic.com/issues/show_bug.cgi?id=5636



--- Comment #5 from bearophile_h...@eml.cc 2012-03-06 20:05:50 PST ---
A discussion thread:
http://www.digitalmars.com/webnews/newsgroups.php?art_group=digitalmars.D&article_id=160067

One of the messages:
http://www.digitalmars.com/webnews/newsgroups.php?art_group=digitalmars.D&article_id=160128


Vector operations like a[]<b[] are meant to return an array of bools. To see
how this is useful you probably must think in terms of vector-style
programming. In NumPy the use of arrays of booleans is common:

>>> from numpy import *
>>> a = array([3,6,8,9])
>>> a == 6
array([False,  True, False, False], dtype=bool)
>>> a >= 7
array([False, False,  True,  True], dtype=bool)
>>> a < 5
array([ True, False, False, False], dtype=bool)
>>> # count all the even numbers
>>> sum( (a%2) == 0 )
2
>>> b = array([2,6,7,10])
>>> a == b
array([False,  True, False, False], dtype=bool)
>>> a < b
array([False, False, False,  True], dtype=bool)


They are sometimes used as masks, it's useful if you have a Vector type that
supports multi-index syntax:

i = scipy.array([0,1,2,1]) # array of indices for the first axis
j = scipy.array([1,2,3,4]) # array of indices for the second axis
a[i,j] # return array([a[0,1], a[1,2], a[2,3], a[1,4]])
b = scipy.array([True, False, True, False])
a[b] # return array([a[0], a[2]]) since only b[0] and b[2] are True


Using the new CPU AVX registers you are able to perform a loop and work on the
items of an array in parallel until all the booleans of an array are false. See
this, especially Listing 5:

http://software.intel.com/en-us/articles/introduction-to-intel-advanced-vector-extensions/

http://www.cs.uaf.edu/2011/spring/cs641/lecture/04_12_AVX.html

Vector comparisons have a natural hardware implementation with AVX/AVX2
instructions like _mm256_cmp_ps.

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