use Benchmark;

my @array = (6252,8278, ...array of 200 4 digit integers...;

timethese(0, {
     sorty => \&sorty,
     space => \&spaceship,
     comp  => \&comp
});

sub sorty {
  return sort @array
}

sub comp {
  return sort {$a cmp $b} @array
}

sub spaceship {
  return sort {$a <=> $b} @array
}

## gives:
Benchmark: running comp, sorty, space, each for at least 3 CPU seconds
+...
      comp:  2 wallclock secs ( 3.05 usr +  0.02 sys =  3.07CPU) @ 617
+076.87/s (n=1894426)
     sorty:  1 wallclock secs ( 3.06 usr + -0.00 sys =  3.06CPU) @ 650
+267.65/s (n=1989819)
     space:  3 wallclock secs ( 3.38 usr +  0.02 sys =  3.40CPU) @ 590
+187.65/s (n=2006638)
[download]

Your results can vary greatly depending on your data. Check out this code that gives you a pretty evenly distributed sample.

use Benchmark;

# Get 200 random 4-digit integers
my @array = (grep {/\d{4}/} map { 10000 * (sprintf "%0.4f", rand) } (1
+..400))[1..200];
print "N = " . @array . "\n\n"; # Be sure we have the right sample siz
+e

timethese(0, {
     sorty => \&sorty,
     space => \&spaceship,
     comp  => \&comp
});

sub sorty {
  sort @array
}

sub comp {
  sort {$a cmp $b} @array
}

sub spaceship {
  sort {$a <=> $b} @array
}
[download]

PRINTS:

N = 200

Benchmark: running comp, sorty, space, each for at least 3 CPU seconds
+...
comp:  3 wallclock secs ( 3.15 usr +  0.01 sys =  3.16 CPU) @ 1064618.
+52/s (n=3368453)
sorty:  3 wallclock secs ( 3.11 usr +  0.00 sys =  3.11 CPU) @ 1096416
+.69/s (n=3415338)
space:  3 wallclock secs ( 3.11 usr +  0.00 sys =  3.11 CPU) @ 1064940
+.23/s (n=3314094)
[download]

Looks like a pretty even race to me!

Well, you are not measuring anything but how fast Perl can call a subroutine. The very high iterations/per second for sorting a 200 element array should have tipped you off.

One should realize that Benchmark calls your function in void context. And sorting in void context is optimized to not do the sorting. So, you haven't benchmark anything. Benchmarking is an art, and you shouldn't test something with one size of data and draw conclusions.

Here's a better benchmark:

use strict;
use Benchmark;


for (my $size = 10; $size <= 100_000; $size *= 10) {
    @main::array = map {int rand $size} 1 .. $size;

    timethese -3 => {
        "[$size] <=>"  =>  'my @array2 = sort {$a <=> $b} @main::array
+',
        "[$size] cmp"  =>  'my @array2 = sort {$a cmp $b} @main::array
+',
    }
}
[download]

Name "main::array" used only once: possible typo at ./sort_bench.pl line 9.
Benchmark: running 10 <=>, 10 cmp, each for at least 3 CPU seconds...
  10 <=>:  4 wallclock secs ( 3.13 usr +  0.01 sys =  3.14 CPU) @ 45414.01/s (n=142600)
  10 cmp:  3 wallclock secs ( 3.01 usr +  0.00 sys =  3.01 CPU) @ 44175.08/s (n=132967)
Benchmark: running 100 <=>, 100 cmp, each for at least 3 CPU seconds...
 100 <=>:  3 wallclock secs ( 3.23 usr +  0.01 sys =  3.24 CPU) @ 4453.70/s (n=14430)
 100 cmp:  3 wallclock secs ( 3.23 usr +  0.00 sys =  3.23 CPU) @ 4095.67/s (n=13229)
Benchmark: running 1000 <=>, 1000 cmp, each for at least 3 CPU seconds...
1000 <=>:  3 wallclock secs ( 3.22 usr +  0.01 sys =  3.23 CPU) @ 324.77/s (n=1049)
1000 cmp:  3 wallclock secs ( 3.26 usr +  0.00 sys =  3.26 CPU) @ 292.64/s (n=954)
Benchmark: running 10000 <=>, 10000 cmp, each for at least 3 CPU seconds...
10000 <=>:  3 wallclock secs ( 3.09 usr +  0.01 sys =  3.10 CPU) @ 17.74/s (n=55)
10000 cmp:  3 wallclock secs ( 3.08 usr +  0.00 sys =  3.08 CPU) @ 13.96/s (n=43)
Benchmark: running 100000 <=>, 100000 cmp, each for at least 3 CPU seconds...
100000 <=>:  4 wallclock secs ( 3.42 usr +  0.01 sys =  3.43 CPU) @  1.17/s (n=4)
100000 cmp:  3 wallclock secs ( 3.30 usr +  0.01 sys =  3.31 CPU) @  0.91/s (n=3)
            (warning: too few iterations for a reliable count)