Parallelize a job with Parallel::Forker

In Opsview, Master and Slave monitoring servers collect perfdata from servers and parse and write to rrd files.

but I encountered rrd file writing timeout(12 seconds) as the number of rrd files increased.
So I started investigating where the performance bottleneck could be occurring ( We have multi-core CPU and there are no cpu or I/O bottlenecks ) and parallelized the time-consuming job in insert.pl like this with Parallel::Forker module.

Performance increases almost linearly with increasing N until you meet the limit of I/O bandwidth.

Original

JOB FLOW

perfdata in array -> iteration ( parse, write )
 [ @perfdata ]       [ processdata(@perfdata) ]

CODE

processdata(@perfdata);
debug( 5, 'INSERT nagiosgraph exited' );

Parallelized

JOB FLOW

perfdata in array -> split array into N -+-> array 0  -> iteration ( parse, write ) 
                                         +-> array 1  -> iteration ( parse, write ) 
                                         +->    .     -> iteration ( parse, write ) 
                                         +-> array N-1-> iteration ( parse, write ) 

CODE

use Parallel::Forker;

my $fork = Parallel::Forker->new;
$SIG{CHLD} = sub { Parallel::Forker::sig_child($fork); };
$SIG{TERM} = sub { $fork->kill_tree_all('TERM') if $fork && $fork->in_parent; die "Quitting...\n"; };

my $n = 4;
my @n_perfdata;

my $size = int(@perfdata/$n);
foreach ( 0 .. $n-1 ){
    $n_perfdata[$_] = [ splice @perfdata, 0, $size ];
}
push @{$n_perfdata[$n-1]}, @perfdata;

foreach ( 0..$#n_perfdata ) {
    $fork->schedule ( name => $_, run_on_start => \&child_sub )->run;
}

$fork->wait_all();

debug( 5, 'INSERT nagiosgraph exited' );

sub child_sub {
    processdata( @{ $n_perfdata[ $_[0]{name} ] } );
}