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digitalmars.D.bugs - [Issue 5636] New: Array ops broken for comparisons

reply d-bugmail puremagic.com writes:
http://d.puremagic.com/issues/show_bug.cgi?id=5636

           Summary: Array ops broken for comparisons
           Product: D
           Version: D2
          Platform: Other
        OS/Version: Windows
            Status: NEW
          Keywords: wrong-code
          Severity: normal
          Priority: P2
         Component: DMD
        AssignedTo: nobody puremagic.com
        ReportedBy: simen.kjaras gmail.com


--- Comment #0 from Simen Kjaeraas <simen.kjaras gmail.com> 2011-02-21 14:13:02
PST ---
void main( ) {
    auto a = [1,2,3,4,5];
    auto b = [5,4,3,2,1];

    bool[] c = new bool[a.length];
    foreach ( i, ref e; c ) {
        e = a[i] < b[i];
    }
    assert( c == [true, true, false, false, false] );

    c[] = a[] < b[];
    assert( c != [true, true, false, false, false] );
}

One would believe the two results should be equal, but they're not.

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next sibling parent d-bugmail puremagic.com writes:
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--- Comment #1 from Simen Kjaeraas <simen.kjaras gmail.com> 2011-02-21 14:27:41
PST ---
Actually, thinking about this some more, I see what is wrong. It is actually
doing:

bool tmp = a[] < b[];
c[] = tmp;

I am not sure about the details of array comparison like that, a fact I feel
makes this bug report more correct.

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prev sibling next sibling parent d-bugmail puremagic.com writes:
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bearophile_hugs eml.cc changed:

           What    |Removed                     |Added
----------------------------------------------------------------------------
                 CC|                            |bearophile_hugs eml.cc


--- Comment #2 from bearophile_hugs eml.cc 2011-02-21 15:17:46 PST ---
More context, links to examples from NumPy and AVX instructions that make some
vectors operations even more useful: 

http://www.digitalmars.com/webnews/newsgroups.php?art_group=digitalmars.D&article_id=130273

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--- Comment #3 from bearophile_hugs eml.cc 2011-02-21 15:18:58 PST ---
Thank you for adding this bug report/enhancement request, I was going to add it
myself in a day or two :-)

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yebblies <yebblies gmail.com> changed:

           What    |Removed                     |Added
----------------------------------------------------------------------------
                 CC|                            |yebblies gmail.com
           Platform|Other                       |All
         OS/Version|Windows                     |All
           Severity|normal                      |enhancement


--- Comment #4 from yebblies <yebblies gmail.com> 2012-02-05 15:45:14 EST ---
This is working as designed, although it's not particularly intuitive.

It might be worth bringing this up on the newsgroup to see what kind of support
it has.

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--- Comment #5 from bearophile_hugs 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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