I thought so too, but here's the benchmark for it:

#!/usr/bin/perl
use strict;
use warnings;
use Benchmark qw/cmpthese/;
use PDL::LiteF;

my @data = ( [ 0, 3, 2, 1 ], [ 1, 11, 1, 2 ], [ 5, -2, 0, 1 ], );

# It's not fair to make the conversion every time.
my $pdldata = pdl @data;


sub using_array {
    my @data = @_;
    my @sums;
    for my $i ( 0 .. $#data ) {
        $sums[0] += $data[$i][0];
        $sums[1] += $data[$i][1];
        $sums[2] += $data[$i][2];
        $sums[3] += $data[$i][3];
    }
    $sums[$_] /= @data for 0 .. 3;
    return @sums;
}

sub using_pdl {
    my $pdldata = shift;
    $pdldata /= $pdldata->getdim(1);
    return $pdldata->transpose->sumover;
}

cmpthese(
    100000,
    {   'Array-based' => sub { using_array(@data) },
        'PDL-based'   => sub { using_pdl($pdldata) },
    }
);
[download]

Result:

                Rate   PDL-based Array-based
PDL-based    36496/s          --        -67%
Array-based 111111/s        204%          --
[download]

Apparently, for a dataset of this size (3 by 4) it's not worth it to use PDL. The good thing though, is that it can be applied for a bidimensional piddle of an arbitrary size without modifications of the subroutine.

I suppose that PDL scales much better though, I've used it for multidimensional piddles of 1e7 elements with a 50-fold increase in speed over a traditional array-based implementation.

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