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=head1 NAME

perlthrtut - tutorial on threads in Perl

=head1 DESCRIPTION

B<NOTE>: this tutorial describes the new Perl threading flavour
introduced in Perl 5.6.0 called interpreter threads, or ithreads
for short.  There is another older perl threading flavour called
the 5.005 model, unsurprisingly for 5.005 versions of Perl.

You can see which (or neither) threading flavour you have by
running C<perl -V> and looking at the C<Platform> section.
If you have C<useithreads=define> you have ithreads, if you
have C<use5005threads=define> you have 5.005 threads.
If you have neither, you don't have any thread support built in.
If you have both, you are in trouble.


=head1 What Is A Thread Anyway?

A thread is a flow of control through a program with a single
execution point.

Sounds an awful lot like a process, doesn't it? Well, it should.
Threads are one of the pieces of a process.  Every process has at least
one thread and, up until now, every process running Perl had only one
thread.  With 5.8, though, you can create extra threads.  We're going
to show you how, when, and why.

=head1 Threaded Program Models

There are three basic ways that you can structure a threaded
program.  Which model you choose depends on what you need your program
to do.  For many non-trivial threaded programs you'll need to choose
different models for different pieces of your program.

=head2 Boss/Worker

The boss/worker model usually has one `boss' thread and one or more
`worker' threads.  The boss thread gathers or generates tasks that need
to be done, then parcels those tasks out to the appropriate worker
thread.

This model is common in GUI and server programs, where a main thread
waits for some event and then passes that event to the appropriate
worker threads for processing.  Once the event has been passed on, the
boss thread goes back to waiting for another event.

The boss thread does relatively little work.  While tasks aren't
necessarily performed faster than with any other method, it tends to
have the best user-response times.

=head2 Work Crew

In the work crew model, several threads are created that do
essentially the same thing to different pieces of data.  It closely
mirrors classical parallel processing and vector processors, where a
large array of processors do the exact same thing to many pieces of
data.

This model is particularly useful if the system running the program
will distribute multiple threads across different processors.  It can
also be useful in ray tracing or rendering engines, where the
individual threads can pass on interim results to give the user visual
feedback.

=head2 Pipeline

The pipeline model divides up a task into a series of steps, and
passes the results of one step on to the thread processing the
next.  Each thread does one thing to each piece of data and passes the
results to the next thread in line.

This model makes the most sense if you have multiple processors so two
or more threads will be executing in parallel, though it can often
make sense in other contexts as well.  It tends to keep the individual
tasks small and simple, as well as allowing some parts of the pipeline
to block (on I/O or system calls, for example) while other parts keep
going.  If you're running different parts of the pipeline on different
processors you may also take advantage of the caches on each
processor.

This model is also handy for a form of recursive programming where,
rather than having a subroutine call itself, it instead creates
another thread.  Prime and Fibonacci generators both map well to this
form of the pipeline model. (A version of a prime number generator is
presented later on.)

=head1 Native threads

There are several different ways to implement threads on a system.  How
threads are implemented depends both on the vendor and, in some cases,
the version of the operating system.  Often the first implementation
will be relatively simple, but later versions of the OS will be more
sophisticated.

While the information in this section is useful, it's not necessary,
so you can skip it if you don't feel up to it.

There are three basic categories of threads: user-mode threads, kernel
threads, and multiprocessor kernel threads.

User-mode threads are threads that live entirely within a program and
its libraries.  In this model, the OS knows nothing about threads.  As
far as it's concerned, your process is just a process.

This is the easiest way to implement threads, and the way most OSes
start.  The big disadvantage is that, since the OS knows nothing about
threads, if one thread blocks they all do.  Typical blocking activities
include most system calls, most I/O, and things like sleep().

Kernel threads are the next step in thread evolution.  The OS knows
about kernel threads, and makes allowances for them.  The main
difference between a kernel thread and a user-mode thread is
blocking.  With kernel threads, things that block a single thread don't
block other threads.  This is not the case with user-mode threads,
where the kernel blocks at the process level and not the thread level.

This is a big step forward, and can give a threaded program quite a
performance boost over non-threaded programs.  Threads that block
performing I/O, for example, won't block threads that are doing other
things.  Each process still has only one thread running at once,
though, regardless of how many CPUs a system might have.

Since kernel threading can interrupt a thread at any time, they will
uncover some of the implicit locking assumptions you may make in your
program.  For example, something as simple as C<$a = $a + 2> can behave
unpredictably with kernel threads if $a is visible to other
threads, as another thread may have changed $a between the time it
was fetched on the right hand side and the time the new value is
stored.

Multiprocessor kernel threads are the final step in thread
support.  With multiprocessor kernel threads on a machine with multiple
CPUs, the OS may schedule two or more threads to run simultaneously on
different CPUs.

This can give a serious performance boost to your threaded program,
since more than one thread will be executing at the same time.  As a
tradeoff, though, any of those nagging synchronization issues that
might not have shown with basic kernel threads will appear with a
vengeance.

In addition to the different levels of OS involvement in threads,
different OSes (and different thread implementations for a particular
OS) allocate CPU cycles to threads in different ways.

Cooperative multitasking systems have running threads give up control
if one of two things happen.  If a thread calls a yield function, it
gives up control.  It also gives up control if the thread does
something that would cause it to block, such as perform I/O.  In a
cooperative multitasking implementation, one thread can starve all the
others for CPU time if it so chooses.

Preemptive multitasking systems interrupt threads at regular intervals
while the system decides which thread should run next.  In a preemptive
multitasking system, one thread usually won't monopolize the CPU.

On some systems, there can be cooperative and preemptive threads
running simultaneously. (Threads running with realtime priorities
often behave cooperatively, for example, while threads running at
normal priorities behave preemptively.)

=head1 What kind of threads are perl threads?

If you have experience with other thread implementations, you might
find that things aren't quite what you expect.  It's very important to
remember when dealing with Perl threads that Perl Threads Are Not X
Threads, for all values of X.  They aren't POSIX threads, or
DecThreads, or Java's Green threads, or Win32 threads.  There are
similarities, and the broad concepts are the same, but if you start
looking for implementation details you're going to be either
disappointed or confused.  Possibly both.

This is not to say that Perl threads are completely different from
everything that's ever come before--they're not.  Perl's threading
model owes a lot to other thread models, especially POSIX.  Just as
Perl is not C, though, Perl threads are not POSIX threads.  So if you
find yourself looking for mutexes, or thread priorities, it's time to
step back a bit and think about what you want to do and how Perl can
do it.

However it is important to remember that Perl threads cannot magically
do things unless your operating systems threads allows it. So if your
system blocks the entire process on sleep(), perl usually will as well.

=head1 Threadsafe Modules

The addition of threads has changed Perl's internals 
substantially. There are implications for people who write
modules with XS code or external libraries. However, since the threads
do not share data, pure Perl modules that don't interact with external
systems should be safe. Modules that are not tagged as thread-safe should
be tested or code reviewed before being used in production code.

Not all modules that you might use are thread-safe, and you should
always assume a module is unsafe unless the documentation says
otherwise.  This includes modules that are distributed as part of the
core.  Threads are a new feature, and even some of the standard
modules aren't thread-safe. (*** I think ActiveState checked this for
psuedofork, check with GSAR)

Even if a module is threadsafe, it doesn't mean that the module is optimized
to work well with threads. A module could possibly be rewritten to utilize
the new features in threaded Perl to increase performance in a threaded
environment.

If you're using a module that's not thread-safe for some reason, you
can protect yourself by using semaphores and lots of programming
discipline to control access to the module.  Semaphores are covered
later in the article.  Perl Threads Are Different

=head1 Thread Basics

The core L<threads> module provides the basic functions you need to write
threaded programs.  In the following sections we'll cover the basics,
showing you what you need to do to create a threaded program.   After
that, we'll go over some of the features of the L<threads> module that
make threaded programming easier.

=head2 Basic Thread Support

Thread support is a Perl compile-time option - it's something that's
turned on or off when Perl is built at your site, rather than when
your programs are compiled. If your Perl wasn't compiled with thread
support enabled, then any attempt to use threads will fail.

Your programs can use the Config module to check whether threads are
enabled. If your program can't run without them, you can say something
like:

  $Config{useithreads} or die "Recompile Perl with threads to run this program.";

A possibly-threaded program using a possibly-threaded module might
have code like this:

    use Config; 
    use MyMod; 

    if ($Config{useithreads}) { 
        # We have threads 
        require MyMod_threaded; 
        import MyMod_threaded; 
    } else { 
        require MyMod_unthreaded; 
        import MyMod_unthreaded; 
    } 

Since code that runs both with and without threads is usually pretty
messy, it's best to isolate the thread-specific code in its own
module.  In our example above, that's what MyMod_threaded is, and it's
only imported if we're running on a threaded Perl.

=head2 Creating Threads

The L<threads> package provides the tools you need to create new
threads.  Like any other module, you need to tell Perl you want to use
it; C<use threads> imports all the pieces you need to create basic
threads.

The simplest, straightforward way to create a thread is with new():

    use threads; 

    $thr = threads->new(\&sub1);

    sub sub1 { 
        print "In the thread\n"; 
    }

The new() method takes a reference to a subroutine and creates a new
thread, which starts executing in the referenced subroutine.  Control
then passes both to the subroutine and the caller.

If you need to, your program can pass parameters to the subroutine as
part of the thread startup.  Just include the list of parameters as
part of the C<threads::new> call, like this:

    use threads; 
    $Param3 = "foo"; 
    $thr = threads->new(\&sub1, "Param 1", "Param 2", $Param3); 
    $thr = threads->new(\&sub1, @ParamList); 
    $thr = threads->new(\&sub1, qw(Param1 Param2 $Param3));

    sub sub1 { 
        my @InboundParameters = @_; 
        print "In the thread\n"; 
        print "got parameters >", join("<>", @InboundParameters), "<\n"; 
    }


The last example illustrates another feature of threads.  You can spawn
off several threads using the same subroutine.  Each thread executes
the same subroutine, but in a separate thread with a separate
environment and potentially separate arguments.

=head2 Giving up control

There are times when you may find it useful to have a thread
explicitly give up the CPU to another thread.  Your threading package
might not support preemptive multitasking for threads, for example, or
you may be doing something compute-intensive and want to make sure
that the user-interface thread gets called frequently.  Regardless,
there are times that you might want a thread to give up the processor.

Perl's threading package provides the yield() function that does
this. yield() is pretty straightforward, and works like this:

    use threads; 
        
        sub loop {
                my $thread = shift;
                my $foo = 50;
                while($foo--) { print "in thread $thread\n" }
                threads->yield();
                $foo = 50;
                while($foo--) { print "in thread $thread\n" }
        }

        my $thread1 = threads->new(\&loop, 'first');
        my $thread2 = threads->new(\&loop, 'second');
        my $thread3 = threads->new(\&loop, 'third');
        
It is important to remember that yield() is only a hint to give up the CPU,
it depends on your hardware, OS and threading libraries what actually happens.
Therefore it is important to note that one should not build the scheduling of 
the threads around yield() calls. It might work on your platform but it won't
work on another platform.

=head2 Waiting For A Thread To Exit

Since threads are also subroutines, they can return values.  To wait
for a thread to exit and extract any values it might return, you can
use the join() method:

    use threads; 
    $thr = threads->new(\&sub1);

    @ReturnData = $thr->join; 
    print "Thread returned @ReturnData"; 

    sub sub1 { return "Fifty-six", "foo", 2; }

In the example above, the join() method returns as soon as the thread
ends.  In addition to waiting for a thread to finish and gathering up
any values that the thread might have returned, join() also performs
any OS cleanup necessary for the thread.  That cleanup might be
important, especially for long-running programs that spawn lots of
threads.  If you don't want the return values and don't want to wait
for the thread to finish, you should call the detach() method
instead. detach() is covered later in the article.

=head2 Ignoring A Thread

join() does three things: it waits for a thread to exit, cleans up
after it, and returns any data the thread may have produced.  But what
if you're not interested in the thread's return values, and you don't
really care when the thread finishes? All you want is for the thread
to get cleaned up after when it's done.

In this case, you use the detach() method.  Once a thread is detached,
it'll run until it's finished, then Perl will clean up after it
automatically.

    use threads; 
    $thr = threads->new(\&sub1); # Spawn the thread

    $thr->detach; # Now we officially don't care any more

    sub sub1 { 
        $a = 0; 
        while (1) { 
            $a++; 
            print "\$a is $a\n"; 
            sleep 1; 
        } 
    }


Once a thread is detached, it may not be joined, and any output that
it might have produced (if it was done and waiting for a join) is
lost.

=head1 Threads And Data

Now that we've covered the basics of threads, it's time for our next
topic: data.  Threading introduces a couple of complications to data
access that non-threaded programs never need to worry about.

=head2 Shared And Unshared Data

The biggest difference between perl threading and the old 5.005 style
threading, or most other threading systems out there, is that all data
is not shared. When a new perl thread is created all data is cloned 
and is private to that thread!

To make use of threading however, one usually want the threads to share
data between each other. This is done with the L<threads::shared> module
and the C< : shared> attribute:

        use threads;
        use threads::shared;
        my $foo : shared = 1;
        my $bar = 1;
        threads->new(sub { $foo++; $bar++ })->join;
        
        print "$foo\n";  #prints 2 since $foo is shared
        print "$bar\n";  #prints 1 since $bar is not shared

=head2 Thread Pitfalls: Races

While threads bring a new set of useful tools, they also bring a
number of pitfalls.  One pitfall is the race condition:

    use threads; 
    use threads::shared;
    my $a : shared = 1; 
    $thr1 = threads->new(\&sub1); 
    $thr2 = threads->new(\&sub2); 

    $thr1->join;
    $thr2->join;
    print "$a\n";

    sub sub1 { $foo = $a; $a = $foo + 1; }
    sub sub2 { $bar = $a; $a = $bar + 1; }

What do you think $a will be? The answer, unfortunately, is "it
depends." Both sub1() and sub2() access the global variable $a, once
to read and once to write.  Depending on factors ranging from your
thread implementation's scheduling algorithm to the phase of the moon,
$a can be 2 or 3.

Race conditions are caused by unsynchronized access to shared
data.  Without explicit synchronization, there's no way to be sure that
nothing has happened to the shared data between the time you access it
and the time you update it.  Even this simple code fragment has the
possibility of error:

    use threads; 
    my $a : shared = 2;
    my $b : shared;
    my $c : shared;
    my $thr1 = threads->create(sub { $b = $a; $a = $b + 1; }); 
    my $thr2 = threads->create(sub { $c = $a; $a = $c + 1; });
    $thr1->join();
    $thr2->join();

Two threads both access $a.  Each thread can potentially be interrupted
at any point, or be executed in any order.  At the end, $a could be 3
or 4, and both $b and $c could be 2 or 3.

Whenever your program accesses data or resources that can be accessed
by other threads, you must take steps to coordinate access or risk
data corruption and race conditions.

=head2 Controlling access: lock()

The lock() function takes a shared variable and puts a lock on it.  
No other thread may lock the variable until the locking thread exits
the innermost block containing the lock.  
Using lock() is straightforward:

    use threads; 
    my $a : shared = 4; 
    $thr1 = threads->new(sub { 
        $foo = 12; 
        { 
            lock ($a); # Block until we get access to $a 
            $b = $a; 
            $a = $b * $foo; 
        } 
        print "\$foo was $foo\n";
    }); 
    $thr2 = threads->new(sub { 
        $bar = 7; 
        { 
            lock ($a); # Block until we can get access to $a
            $c = $a; 
            $a = $c * $bar; 
        } 
        print "\$bar was $bar\n";
    }); 
    $thr1->join; 
    $thr2->join; 
    print "\$a is $a\n";

lock() blocks the thread until the variable being locked is
available.  When lock() returns, your thread can be sure that no other
thread can lock that variable until the innermost block containing the
lock exits.

It's important to note that locks don't prevent access to the variable
in question, only lock attempts.  This is in keeping with Perl's
longstanding tradition of courteous programming, and the advisory file
locking that flock() gives you.  

You may lock arrays and hashes as well as scalars.  Locking an array,
though, will not block subsequent locks on array elements, just lock
attempts on the array itself.

Finally, locks are recursive, which means it's okay for a thread to
lock a variable more than once.  The lock will last until the outermost
lock() on the variable goes out of scope.

=head2 Thread Pitfall: Deadlocks

Locks are a handy tool to synchronize access to data.  Using them
properly is the key to safe shared data.  Unfortunately, locks aren't
without their dangers.  Consider the following code:

    use threads; 
    my $a : shared = 4; 
    my $b : shared = "foo"; 
    my $thr1 = threads->new(sub { 
        lock($a); 
        yield; 
        sleep 20; 
        lock ($b); 
    }); 
    my $thr2 = threads->new(sub { 
        lock($b); 
        yield; 
        sleep 20; 
        lock ($a); 
    });

This program will probably hang until you kill it.  The only way it
won't hang is if one of the two async() routines acquires both locks
first.  A guaranteed-to-hang version is more complicated, but the
principle is the same.

The first thread spawned by async() will grab a lock on $a then, a
second or two later, try to grab a lock on $b.  Meanwhile, the second
thread grabs a lock on $b, then later tries to grab a lock on $a.  The
second lock attempt for both threads will block, each waiting for the
other to release its lock.

This condition is called a deadlock, and it occurs whenever two or
more threads are trying to get locks on resources that the others
own.  Each thread will block, waiting for the other to release a lock
on a resource.  That never happens, though, since the thread with the
resource is itself waiting for a lock to be released.

There are a number of ways to handle this sort of problem.  The best
way is to always have all threads acquire locks in the exact same
order.  If, for example, you lock variables $a, $b, and $c, always lock
$a before $b, and $b before $c.  It's also best to hold on to locks for
as short a period of time to minimize the risks of deadlock.

=head2 Queues: Passing Data Around

A queue is a special thread-safe object that lets you put data in one
end and take it out the other without having to worry about
synchronization issues.  They're pretty straightforward, and look like
this:

    use threads; 
    use threads::shared::queue;

    my $DataQueue = new threads::shared::queue; 
    $thr = threads->new(sub { 
        while ($DataElement = $DataQueue->dequeue) { 
            print "Popped $DataElement off the queue\n";
        } 
    }); 

    $DataQueue->enqueue(12); 
    $DataQueue->enqueue("A", "B", "C"); 
    $DataQueue->enqueue(\$thr); 
    sleep 10; 
    $DataQueue->enqueue(undef);
    $thr->join();

You create the queue with C<new threads::shared::queue>.  Then you can
add lists of scalars onto the end with enqueue(), and pop scalars off
the front of it with dequeue().  A queue has no fixed size, and can grow
as needed to hold everything pushed on to it.

If a queue is empty, dequeue() blocks until another thread enqueues
something.  This makes queues ideal for event loops and other
communications between threads.


=head1 Threads And Code

In addition to providing thread-safe access to data via locks and
queues, threaded Perl also provides general-purpose semaphores for
coarser synchronization than locks provide and thread-safe access to
entire subroutines.

=head2 Semaphores: Synchronizing Data Access

Semaphores are a kind of generic locking mechanism.  Unlike lock, which
gets a lock on a particular scalar, Perl doesn't associate any
particular thing with a semaphore so you can use them to control
access to anything you like.  In addition, semaphores can allow more
than one thread to access a resource at once, though by default
semaphores only allow one thread access at a time.

=over 4

=item Basic semaphores

Semaphores have two methods, down and up. down decrements the resource
count, while up increments it.  down calls will block if the
semaphore's current count would decrement below zero.  This program
gives a quick demonstration:

    use threads qw(yield); 
    use threads::shared::semaphore; 
    my $semaphore = new threads::shared::semaphore; 
    $GlobalVariable = 0;

    $thr1 = new threads \&sample_sub, 1; 
    $thr2 = new threads \&sample_sub, 2; 
    $thr3 = new threads \&sample_sub, 3;

    sub sample_sub { 
        my $SubNumber = shift @_; 
        my $TryCount = 10; 
        my $LocalCopy; 
        sleep 1; 
        while ($TryCount--) { 
            $semaphore->down; 
            $LocalCopy = $GlobalVariable; 
            print "$TryCount tries left for sub $SubNumber (\$GlobalVariable is $GlobalVariable)\n"; 
            yield; 
            sleep 2; 
            $LocalCopy++; 
            $GlobalVariable = $LocalCopy; 
            $semaphore->up; 
        } 
    }

    $thr1->join();
    $thr2->join();
    $thr3->join();

The three invocations of the subroutine all operate in sync.  The
semaphore, though, makes sure that only one thread is accessing the
global variable at once.

=item Advanced Semaphores

By default, semaphores behave like locks, letting only one thread
down() them at a time.  However, there are other uses for semaphores.

Each semaphore has a counter attached to it. By default, semaphores are
created with the counter set to one, down() decrements the counter by
one, and up() increments by one. However, we can override any or all
of these defaults simply by passing in different values:

    use threads;
    use threads::shared::semaphore;
    my $semaphore = threads::shared::semaphore->new(5);
                    # Creates a semaphore with the counter set to five

    $thr1 = threads->new(\&sub1);
    $thr2 = threads->new(\&sub1);

    sub sub1 {
        $semaphore->down(5); # Decrements the counter by five
        # Do stuff here
        $semaphore->up(5); # Increment the counter by five
    }

    $thr1->detach();
    $thr2->detach();

If down() attempts to decrement the counter below zero, it blocks until
the counter is large enough.  Note that while a semaphore can be created
with a starting count of zero, any up() or down() always changes the
counter by at least one, and so $semaphore->down(0) is the same as
$semaphore->down(1).

The question, of course, is why would you do something like this? Why
create a semaphore with a starting count that's not one, or why
decrement/increment it by more than one? The answer is resource
availability.  Many resources that you want to manage access for can be
safely used by more than one thread at once.

For example, let's take a GUI driven program.  It has a semaphore that
it uses to synchronize access to the display, so only one thread is
ever drawing at once.  Handy, but of course you don't want any thread
to start drawing until things are properly set up.  In this case, you
can create a semaphore with a counter set to zero, and up it when
things are ready for drawing.

Semaphores with counters greater than one are also useful for
establishing quotas.  Say, for example, that you have a number of
threads that can do I/O at once.  You don't want all the threads
reading or writing at once though, since that can potentially swamp
your I/O channels, or deplete your process' quota of filehandles.  You
can use a semaphore initialized to the number of concurrent I/O
requests (or open files) that you want at any one time, and have your
threads quietly block and unblock themselves.

Larger increments or decrements are handy in those cases where a
thread needs to check out or return a number of resources at once.

=back

=head1 General Thread Utility Routines

We've covered the workhorse parts of Perl's threading package, and
with these tools you should be well on your way to writing threaded
code and packages.  There are a few useful little pieces that didn't
really fit in anyplace else.

=head2 What Thread Am I In?

The C<threads->self> method provides your program with a way to get an
object representing the thread it's currently in.  You can use this
object in the same way as the ones returned from thread creation.

=head2 Thread IDs

tid() is a thread object method that returns the thread ID of the
thread the object represents.  Thread IDs are integers, with the main
thread in a program being 0.  Currently Perl assigns a unique tid to
every thread ever created in your program, assigning the first thread
to be created a tid of 1, and increasing the tid by 1 for each new
thread that's created.

=head2 Are These Threads The Same?

The equal() method takes two thread objects and returns true 
if the objects represent the same thread, and false if they don't.

Thread objects also have an overloaded == comparison so that you can do
comparison on them as you would with normal objects.

=head2 What Threads Are Running?

threads->list returns a list of thread objects, one for each thread
that's currently running and not detached.  Handy for a number of things,
including cleaning up at the end of your program:

    # Loop through all the threads 
    foreach $thr (threads->list) { 
        # Don't join the main thread or ourselves 
        if ($thr->tid && !threads::equal($thr, threads->self)) { 
            $thr->join; 
        } 
    }

If some threads have not finished running when the main perl thread
ends, perl will warn you about it and die, since it is impossible for perl
to clean up itself while other threads are running

=head1 A Complete Example

Confused yet? It's time for an example program to show some of the
things we've covered.  This program finds prime numbers using threads.

    1  #!/usr/bin/perl -w
    2  # prime-pthread, courtesy of Tom Christiansen
    3
    4  use strict;
    5
    6  use threads;
    7  use threads::shared::queue;
    8
    9  my $stream = new threads::shared::queue;
    10 my $kid    = new threads(\&check_num, $stream, 2);
    11
    12 for my $i ( 3 .. 1000 ) {
    13     $stream->enqueue($i);
    14 } 
    15
    16 $stream->enqueue(undef);
    17 $kid->join();
    18
    19 sub check_num {
    20     my ($upstream, $cur_prime) = @_;
    21     my $kid;
    22     my $downstream = new threads::shared::queue;
    23     while (my $num = $upstream->dequeue) {
    24         next unless $num % $cur_prime;
    25         if ($kid) {
    26            $downstream->enqueue($num);
    27                  } else {
    28            print "Found prime $num\n";
    29                $kid = new threads(\&check_num, $downstream, $num);
    30         }
    31     } 
    32     $downstream->enqueue(undef) if $kid;
    33     $kid->join()         if $kid;
    34 }

This program uses the pipeline model to generate prime numbers.  Each
thread in the pipeline has an input queue that feeds numbers to be
checked, a prime number that it's responsible for, and an output queue
that into which it funnels numbers that have failed the check.  If the thread
has a number that's failed its check and there's no child thread, then
the thread must have found a new prime number.  In that case, a new
child thread is created for that prime and stuck on the end of the
pipeline.

This probably sounds a bit more confusing than it really is, so let's
go through this program piece by piece and see what it does.  (For
those of you who might be trying to remember exactly what a prime
number is, it's a number that's only evenly divisible by itself and 1)

The bulk of the work is done by the check_num() subroutine, which
takes a reference to its input queue and a prime number that it's
responsible for.  After pulling in the input queue and the prime that
the subroutine's checking (line 20), we create a new queue (line 22)
and reserve a scalar for the thread that we're likely to create later
(line 21).

The while loop from lines 23 to line 31 grabs a scalar off the input
queue and checks against the prime this thread is responsible
for.  Line 24 checks to see if there's a remainder when we modulo the
number to be checked against our prime.  If there is one, the number
must not be evenly divisible by our prime, so we need to either pass
it on to the next thread if we've created one (line 26) or create a
new thread if we haven't.

The new thread creation is line 29.  We pass on to it a reference to
the queue we've created, and the prime number we've found.

Finally, once the loop terminates (because we got a 0 or undef in the
queue, which serves as a note to die), we pass on the notice to our
child and wait for it to exit if we've created a child (lines 32 and
37).

Meanwhile, back in the main thread, we create a queue (line 9) and the
initial child thread (line 10), and pre-seed it with the first prime:
2.  Then we queue all the numbers from 3 to 1000 for checking (lines
12-14), then queue a die notice (line 16) and wait for the first child
thread to terminate (line 17).  Because a child won't die until its
child has died, we know that we're done once we return from the join.

That's how it works.  It's pretty simple; as with many Perl programs,
the explanation is much longer than the program.

=head1 Conclusion

A complete thread tutorial could fill a book (and has, many times),
but with what we've covered in this introduction, you should be well
on your way to becoming a threaded Perl expert.

=head1 Bibliography

Here's a short bibliography courtesy of Jürgen Christoffel:

=head2 Introductory Texts

Birrell, Andrew D. An Introduction to Programming with
Threads. Digital Equipment Corporation, 1989, DEC-SRC Research Report
#35 online as
http://gatekeeper.dec.com/pub/DEC/SRC/research-reports/abstracts/src-rr-035.html
(highly recommended)

Robbins, Kay. A., and Steven Robbins. Practical Unix Programming: A
Guide to Concurrency, Communication, and
Multithreading. Prentice-Hall, 1996.

Lewis, Bill, and Daniel J. Berg. Multithreaded Programming with
Pthreads. Prentice Hall, 1997, ISBN 0-13-443698-9 (a well-written
introduction to threads).

Nelson, Greg (editor). Systems Programming with Modula-3.  Prentice
Hall, 1991, ISBN 0-13-590464-1.

Nichols, Bradford, Dick Buttlar, and Jacqueline Proulx Farrell.
Pthreads Programming. O'Reilly & Associates, 1996, ISBN 156592-115-1
(covers POSIX threads).

=head2 OS-Related References

Boykin, Joseph, David Kirschen, Alan Langerman, and Susan
LoVerso. Programming under Mach. Addison-Wesley, 1994, ISBN
0-201-52739-1.

Tanenbaum, Andrew S. Distributed Operating Systems. Prentice Hall,
1995, ISBN 0-13-219908-4 (great textbook).

Silberschatz, Abraham, and Peter B. Galvin. Operating System Concepts,
4th ed. Addison-Wesley, 1995, ISBN 0-201-59292-4

=head2 Other References

Arnold, Ken and James Gosling. The Java Programming Language, 2nd
ed. Addison-Wesley, 1998, ISBN 0-201-31006-6.

Le Sergent, T. and B. Berthomieu. "Incremental MultiThreaded Garbage
Collection on Virtually Shared Memory Architectures" in Memory
Management: Proc. of the International Workshop IWMM 92, St. Malo,
France, September 1992, Yves Bekkers and Jacques Cohen, eds. Springer,
1992, ISBN 3540-55940-X (real-life thread applications).

=head1 Acknowledgements

Thanks (in no particular order) to Chaim Frenkel, Steve Fink, Gurusamy
Sarathy, Ilya Zakharevich, Benjamin Sugars, Jürgen Christoffel, Joshua
Pritikin, and Alan Burlison, for their help in reality-checking and
polishing this article.  Big thanks to Tom Christiansen for his rewrite
of the prime number generator.

=head1 AUTHOR

Dan Sugalski E<lt>sugalskd@ous.eduE<gt>

Slightly modified by Arthur Bergman to fit the new thread model/module.

=head1 Copyrights

This article originally appeared in The Perl Journal #10, and is
copyright 1998 The Perl Journal. It appears courtesy of Jon Orwant and
The Perl Journal.  This document may be distributed under the same terms
as Perl itself.


For more information please see L<threads> and L<threads::shared>.