Provided by: libmce-perl_1.879-1_all 
NAME
MCE::Flow - Parallel flow model for building creative applications
VERSION
This document describes MCE::Flow version 1.879
DESCRIPTION
MCE::Flow is great for writing custom apps to maximize on all available cores. This
module was created to help one harness user_tasks within MCE.
It is trivial to parallelize with mce_stream shown below.
## Native map function
my @a = map { $_ * 4 } map { $_ * 3 } map { $_ * 2 } 1..10000;
## Same as with MCE::Stream (processing from right to left)
@a = mce_stream
sub { $_ * 4 }, sub { $_ * 3 }, sub { $_ * 2 }, 1..10000;
## Pass an array reference to have writes occur simultaneously
mce_stream \@a,
sub { $_ * 4 }, sub { $_ * 3 }, sub { $_ * 2 }, 1..10000;
However, let's have MCE::Flow compute the same in parallel. MCE::Queue will be used for
data flow among the sub-tasks.
use MCE::Flow;
use MCE::Queue;
This calls for preserving output order.
sub preserve_order {
my %tmp; my $order_id = 1; my $gather_ref = $_[0];
@{ $gather_ref } = (); ## clear the array (optional)
return sub {
my ($data_ref, $chunk_id) = @_;
$tmp{$chunk_id} = $data_ref;
while (1) {
last unless exists $tmp{$order_id};
push @{ $gather_ref }, @{ delete $tmp{$order_id++} };
}
return;
};
}
Two queues are needed for data flow between the 3 sub-tasks. Notice task_end and how the
value from $task_name is used for determining which task has ended.
my $b = MCE::Queue->new;
my $c = MCE::Queue->new;
sub task_end {
my ($mce, $task_id, $task_name) = @_;
if (defined $mce->{user_tasks}->[$task_id + 1]) {
my $n_workers = $mce->{user_tasks}->[$task_id + 1]->{max_workers};
if ($task_name eq 'a') {
$b->enqueue((undef) x $n_workers);
}
elsif ($task_name eq 'b') {
$c->enqueue((undef) x $n_workers);
}
}
return;
}
Next are the 3 sub-tasks. The first one reads input and begins the flow. The 2nd task
dequeues, performs the calculation, and enqueues into the next. Finally, the last task
calls the gather method.
Although serialization is done for you automatically, it is done here to save from double
serialization. This is the fastest approach for passing data between sub-tasks. Thus, the
least overhead.
sub task_a {
my @ans; my ($mce, $chunk_ref, $chunk_id) = @_;
push @ans, map { $_ * 2 } @{ $chunk_ref };
$b->enqueue(MCE->freeze([ \@ans, $chunk_id ]));
return;
}
sub task_b {
my ($mce) = @_;
while (1) {
my @ans; my $chunk = $b->dequeue;
last unless defined $chunk;
$chunk = MCE->thaw($chunk);
push @ans, map { $_ * 3 } @{ $chunk->[0] };
$c->enqueue(MCE->freeze([ \@ans, $chunk->[1] ]));
}
return;
}
sub task_c {
my ($mce) = @_;
while (1) {
my @ans; my $chunk = $c->dequeue;
last unless defined $chunk;
$chunk = MCE->thaw($chunk);
push @ans, map { $_ * 4 } @{ $chunk->[0] };
MCE->gather(\@ans, $chunk->[1]);
}
return;
}
In summary, MCE::Flow builds out a MCE instance behind the scene and starts running. The
task_name (shown), max_workers, and use_threads options can take an anonymous array for
specifying the values uniquely per each sub-task.
my @a;
mce_flow {
task_name => [ 'a', 'b', 'c' ], task_end => \&task_end,
gather => preserve_order(\@a)
}, \&task_a, \&task_b, \&task_c, 1..10000;
print "@a\n";
If speed is not a concern and wanting to rid of all the MCE->freeze and MCE->thaw
statements, simply enqueue and dequeue 2 items at a time. Or better yet, see MCE::Step
introduced in MCE 1.506.
First, task_end must be updated. The number of undef(s) must match the number of workers
times the dequeue count. Otherwise, the script will stall.
sub task_end {
...
if ($task_name eq 'a') {
# $b->enqueue((undef) x $n_workers);
$b->enqueue((undef) x ($n_workers * 2));
}
elsif ($task_name eq 'b') {
# $c->enqueue((undef) x $n_workers);
$c->enqueue((undef) x ($n_workers * 2));
}
...
}
Next, the 3 sub-tasks enqueuing and dequeuing 2 elements at a time.
sub task_a {
my @ans; my ($mce, $chunk_ref, $chunk_id) = @_;
push @ans, map { $_ * 2 } @{ $chunk_ref };
$b->enqueue(\@ans, $chunk_id);
return;
}
sub task_b {
my ($mce) = @_;
while (1) {
my @ans; my ($chunk_ref, $chunk_id) = $b->dequeue(2);
last unless defined $chunk_ref;
push @ans, map { $_ * 3 } @{ $chunk_ref };
$c->enqueue(\@ans, $chunk_id);
}
return;
}
sub task_c {
my ($mce) = @_;
while (1) {
my @ans; my ($chunk_ref, $chunk_id) = $c->dequeue(2);
last unless defined $chunk_ref;
push @ans, map { $_ * 4 } @{ $chunk_ref };
MCE->gather(\@ans, $chunk_id);
}
return;
}
Finally, run as usual.
my @a;
mce_flow {
task_name => [ 'a', 'b', 'c' ], task_end => \&task_end,
gather => preserve_order(\@a)
}, \&task_a, \&task_b, \&task_c, 1..10000;
print "@a\n";
SYNOPSIS when CHUNK_SIZE EQUALS 1
Although MCE::Loop may be preferred for running using a single code block, the text below
also applies to this module, particularly for the first block.
All models in MCE default to 'auto' for chunk_size. The arguments for the block are the
same as writing a user_func block using the Core API.
Beginning with MCE 1.5, the next input item is placed into the input scalar variable $_
when chunk_size equals 1. Otherwise, $_ points to $chunk_ref containing many items.
Basically, line 2 below may be omitted from your code when using $_. One can call
MCE->chunk_id to obtain the current chunk id.
line 1: user_func => sub {
line 2: my ($mce, $chunk_ref, $chunk_id) = @_;
line 3:
line 4: $_ points to $chunk_ref->[0]
line 5: in MCE 1.5 when chunk_size == 1
line 6:
line 7: $_ points to $chunk_ref
line 8: in MCE 1.5 when chunk_size > 1
line 9: }
Follow this synopsis when chunk_size equals one. Looping is not required from inside the
first block. Hence, the block is called once per each item.
## Exports mce_flow, mce_flow_f, and mce_flow_s
use MCE::Flow;
MCE::Flow->init(
chunk_size => 1
);
## Array or array_ref
mce_flow sub { do_work($_) }, 1..10000;
mce_flow sub { do_work($_) }, \@list;
## Important; pass an array_ref for deeply input data
mce_flow sub { do_work($_) }, [ [ 0, 1 ], [ 0, 2 ], ... ];
mce_flow sub { do_work($_) }, \@deeply_list;
## File path, glob ref, IO::All::{ File, Pipe, STDIO } obj, or scalar ref
## Workers read directly and not involve the manager process
mce_flow_f sub { chomp; do_work($_) }, "/path/to/file"; # efficient
## Involves the manager process, therefore slower
mce_flow_f sub { chomp; do_work($_) }, $file_handle;
mce_flow_f sub { chomp; do_work($_) }, $io;
mce_flow_f sub { chomp; do_work($_) }, \$scalar;
## Sequence of numbers (begin, end [, step, format])
mce_flow_s sub { do_work($_) }, 1, 10000, 5;
mce_flow_s sub { do_work($_) }, [ 1, 10000, 5 ];
mce_flow_s sub { do_work($_) }, {
begin => 1, end => 10000, step => 5, format => undef
};
SYNOPSIS when CHUNK_SIZE is GREATER THAN 1
Follow this synopsis when chunk_size equals 'auto' or greater than 1. This means having
to loop through the chunk from inside the first block.
use MCE::Flow;
MCE::Flow->init( ## Chunk_size defaults to 'auto' when
chunk_size => 'auto' ## not specified. Therefore, the init
); ## function may be omitted.
## Syntax is shown for mce_flow for demonstration purposes.
## Looping inside the block is the same for mce_flow_f and
## mce_flow_s.
## Array or array_ref
mce_flow sub { do_work($_) for (@{ $_ }) }, 1..10000;
mce_flow sub { do_work($_) for (@{ $_ }) }, \@list;
## Important; pass an array_ref for deeply input data
mce_flow sub { do_work($_) for (@{ $_ }) }, [ [ 0, 1 ], [ 0, 2 ], ... ];
mce_flow sub { do_work($_) for (@{ $_ }) }, \@deeply_list;
## Resembles code using the core MCE API
mce_flow sub {
my ($mce, $chunk_ref, $chunk_id) = @_;
for (@{ $chunk_ref }) {
do_work($_);
}
}, 1..10000;
Chunking reduces the number of IPC calls behind the scene. Think in terms of chunks
whenever processing a large amount of data. For relatively small data, choosing 1 for
chunk_size is fine.
OVERRIDING DEFAULTS
The following list options which may be overridden when loading the module.
use Sereal qw( encode_sereal decode_sereal );
use CBOR::XS qw( encode_cbor decode_cbor );
use JSON::XS qw( encode_json decode_json );
use MCE::Flow
max_workers => 8, # Default 'auto'
chunk_size => 500, # Default 'auto'
tmp_dir => "/path/to/app/tmp", # $MCE::Signal::tmp_dir
freeze => \&encode_sereal, # \&Storable::freeze
thaw => \&decode_sereal # \&Storable::thaw
;
From MCE 1.8 onwards, Sereal 3.015+ is loaded automatically if available. Specify "Sereal
=> 0" to use Storable instead.
use MCE::Flow Sereal => 0;
CUSTOMIZING MCE
MCE::Flow->init ( options )
MCE::Flow::init { options }
The init function accepts a hash of MCE options. Unlike with MCE::Stream, both gather and
bounds_only options may be specified when calling init (not shown below).
use MCE::Flow;
MCE::Flow->init(
chunk_size => 1, max_workers => 4,
user_begin => sub {
print "## ", MCE->wid, " started\n";
},
user_end => sub {
print "## ", MCE->wid, " completed\n";
}
);
my %a = mce_flow sub { MCE->gather($_, $_ * $_) }, 1..100;
print "\n", "@a{1..100}", "\n";
-- Output
## 3 started
## 2 started
## 4 started
## 1 started
## 2 completed
## 4 completed
## 3 completed
## 1 completed
1 4 9 16 25 36 49 64 81 100 121 144 169 196 225 256 289 324 361
400 441 484 529 576 625 676 729 784 841 900 961 1024 1089 1156
1225 1296 1369 1444 1521 1600 1681 1764 1849 1936 2025 2116 2209
2304 2401 2500 2601 2704 2809 2916 3025 3136 3249 3364 3481 3600
3721 3844 3969 4096 4225 4356 4489 4624 4761 4900 5041 5184 5329
5476 5625 5776 5929 6084 6241 6400 6561 6724 6889 7056 7225 7396
7569 7744 7921 8100 8281 8464 8649 8836 9025 9216 9409 9604 9801
10000
Like with MCE::Flow->init above, MCE options may be specified using an anonymous hash for
the first argument. Notice how task_name, max_workers, and use_threads can take an
anonymous array for setting uniquely per each code block.
Unlike MCE::Stream which processes from right-to-left, MCE::Flow begins with the first
code block, thus processing from left-to-right.
use threads;
use MCE::Flow;
my @a = mce_flow {
task_name => [ 'a', 'b', 'c' ],
max_workers => [ 3, 4, 2, ],
use_threads => [ 1, 0, 0, ],
user_end => sub {
my ($mce, $task_id, $task_name) = @_;
MCE->print("$task_id - $task_name completed\n");
},
task_end => sub {
my ($mce, $task_id, $task_name) = @_;
MCE->print("$task_id - $task_name ended\n");
}
},
sub { sleep 1; }, ## 3 workers, named a
sub { sleep 2; }, ## 4 workers, named b
sub { sleep 3; }; ## 2 workers, named c
-- Output
0 - a completed
0 - a completed
0 - a completed
0 - a ended
1 - b completed
1 - b completed
1 - b completed
1 - b completed
1 - b ended
2 - c completed
2 - c completed
2 - c ended
API DOCUMENTATION
Although input data is optional for MCE::Flow, the following assumes chunk_size equals 1
in order to demonstrate all the possibilities for providing input data.
MCE::Flow->run ( sub { code }, list )
mce_flow sub { code }, list
Input data may be defined using a list, an array ref, or a hash ref.
Unlike MCE::Loop, Map, and Grep which take a block as "{ ... }", Flow takes a "sub { ...
}" or a code reference. The other difference is that the comma is needed after the block.
# $_ contains the item when chunk_size => 1
mce_flow sub { do_work($_) }, 1..1000;
mce_flow sub { do_work($_) }, \@list;
# Important; pass an array_ref for deeply input data
mce_flow sub { do_work($_) }, [ [ 0, 1 ], [ 0, 2 ], ... ];
mce_flow sub { do_work($_) }, \@deeply_list;
# Chunking; any chunk_size => 1 or greater
my %res = mce_flow sub {
my ($mce, $chunk_ref, $chunk_id) = @_;
my %ret;
for my $item (@{ $chunk_ref }) {
$ret{$item} = $item * 2;
}
MCE->gather(%ret);
},
\@list;
# Input hash; current API available since 1.828
my %res = mce_flow sub {
my ($mce, $chunk_ref, $chunk_id) = @_;
my %ret;
for my $key (keys %{ $chunk_ref }) {
$ret{$key} = $chunk_ref->{$key} * 2;
}
MCE->gather(%ret);
},
\%hash;
# Unlike MCE::Loop, MCE::Flow doesn't need input to run
mce_flow { max_workers => 4 }, sub {
MCE->say( MCE->wid );
};
# ... and can run multiple tasks
mce_flow {
max_workers => [ 1, 3 ],
task_name => [ 'p', 'c' ]
},
sub {
# 1 producer
MCE->say( "producer: ", MCE->wid );
},
sub {
# 3 consumers
MCE->say( "consumer: ", MCE->wid );
};
# Here, options are specified via init
MCE::Flow->init(
max_workers => [ 1, 3 ],
task_name => [ 'p', 'c' ]
);
mce_flow \&producer, \&consumers;
MCE::Flow->run_file ( sub { code }, file )
mce_flow_f sub { code }, file
The fastest of these is the /path/to/file. Workers communicate the next offset position
among themselves with zero interaction by the manager process.
"IO::All" { File, Pipe, STDIO } is supported since MCE 1.845.
# $_ contains the line when chunk_size => 1
mce_flow_f sub { $_ }, "/path/to/file"; # faster
mce_flow_f sub { $_ }, $file_handle;
mce_flow_f sub { $_ }, $io; # IO::All
mce_flow_f sub { $_ }, \$scalar;
# chunking, any chunk_size => 1 or greater
my %res = mce_flow_f sub {
my ($mce, $chunk_ref, $chunk_id) = @_;
my $buf = '';
for my $line (@{ $chunk_ref }) {
$buf .= $line;
}
MCE->gather($chunk_id, $buf);
},
"/path/to/file";
MCE::Flow->run_seq ( sub { code }, $beg, $end [, $step, $fmt ] )
mce_flow_s sub { code }, $beg, $end [, $step, $fmt ]
Sequence may be defined as a list, an array reference, or a hash reference. The functions
require both begin and end values to run. Step and format are optional. The format is
passed to sprintf (% may be omitted below).
my ($beg, $end, $step, $fmt) = (10, 20, 0.1, "%4.1f");
# $_ contains the sequence number when chunk_size => 1
mce_flow_s sub { $_ }, $beg, $end, $step, $fmt;
mce_flow_s sub { $_ }, [ $beg, $end, $step, $fmt ];
mce_flow_s sub { $_ }, {
begin => $beg, end => $end,
step => $step, format => $fmt
};
# chunking, any chunk_size => 1 or greater
my %res = mce_flow_s sub {
my ($mce, $chunk_ref, $chunk_id) = @_;
my $buf = '';
for my $seq (@{ $chunk_ref }) {
$buf .= "$seq\n";
}
MCE->gather($chunk_id, $buf);
},
[ $beg, $end ];
The sequence engine can compute 'begin' and 'end' items only, for the chunk, and not the
items in between (hence boundaries only). This option applies to sequence only and has no
effect when chunk_size equals 1.
The time to run is 0.006s below. This becomes 0.827s without the bounds_only option due to
computing all items in between, thus creating a very large array. Basically, specify
bounds_only => 1 when boundaries is all you need for looping inside the block; e.g. Monte
Carlo simulations.
Time was measured using 1 worker to emphasize the difference.
use MCE::Flow;
MCE::Flow->init(
max_workers => 1, chunk_size => 1_250_000,
bounds_only => 1
);
# Typically, the input scalar $_ contains the sequence number
# when chunk_size => 1, unless the bounds_only option is set
# which is the case here. Thus, $_ points to $chunk_ref.
mce_flow_s sub {
my ($mce, $chunk_ref, $chunk_id) = @_;
# $chunk_ref contains 2 items, not 1_250_000
# my ( $begin, $end ) = ( $_->[0], $_->[1] );
my $begin = $chunk_ref->[0];
my $end = $chunk_ref->[1];
# for my $seq ( $begin .. $end ) {
# ...
# }
MCE->printf("%7d .. %8d\n", $begin, $end);
},
[ 1, 10_000_000 ];
-- Output
1 .. 1250000
1250001 .. 2500000
2500001 .. 3750000
3750001 .. 5000000
5000001 .. 6250000
6250001 .. 7500000
7500001 .. 8750000
8750001 .. 10000000
MCE::Flow->run ( { input_data => iterator }, sub { code } )
mce_flow { input_data => iterator }, sub { code }
An iterator reference may be specified for input_data. The only other way is to specify
input_data via MCE::Flow->init. This prevents MCE::Flow from configuring the iterator
reference as another user task which will not work.
Iterators are described under section "SYNTAX for INPUT_DATA" at MCE::Core.
MCE::Flow->init(
input_data => iterator
);
mce_flow sub { $_ };
GATHERING DATA
Unlike MCE::Map where gather and output order are done for you automatically, the gather
method is used to have results sent back to the manager process.
use MCE::Flow chunk_size => 1;
## Output order is not guaranteed.
my @a1 = mce_flow sub { MCE->gather($_ * 2) }, 1..100;
print "@a1\n\n";
## Outputs to a hash instead (key, value).
my %h1 = mce_flow sub { MCE->gather($_, $_ * 2) }, 1..100;
print "@h1{1..100}\n\n";
## This does the same thing due to chunk_id starting at one.
my %h2 = mce_flow sub { MCE->gather(MCE->chunk_id, $_ * 2) }, 1..100;
print "@h2{1..100}\n\n";
The gather method may be called multiple times within the block unlike return which would
leave the block. Therefore, think of gather as yielding results immediately to the manager
process without actually leaving the block.
use MCE::Flow chunk_size => 1, max_workers => 3;
my @hosts = qw(
hosta hostb hostc hostd hoste
);
my %h3 = mce_flow sub {
my ($output, $error, $status); my $host = $_;
## Do something with $host;
$output = "Worker ". MCE->wid .": Hello from $host";
if (MCE->chunk_id % 3 == 0) {
## Simulating an error condition
local $? = 1; $status = $?;
$error = "Error from $host"
}
else {
$status = 0;
}
## Ensure unique keys (key, value) when gathering to
## a hash.
MCE->gather("$host.out", $output);
MCE->gather("$host.err", $error) if (defined $error);
MCE->gather("$host.sta", $status);
}, @hosts;
foreach my $host (@hosts) {
print $h3{"$host.out"}, "\n";
print $h3{"$host.err"}, "\n" if (exists $h3{"$host.err"});
print "Exit status: ", $h3{"$host.sta"}, "\n\n";
}
-- Output
Worker 3: Hello from hosta
Exit status: 0
Worker 2: Hello from hostb
Exit status: 0
Worker 1: Hello from hostc
Error from hostc
Exit status: 1
Worker 3: Hello from hostd
Exit status: 0
Worker 2: Hello from hoste
Exit status: 0
The following uses an anonymous array containing 3 elements when gathering data.
Serialization is automatic behind the scene.
my %h3 = mce_flow sub {
...
MCE->gather($host, [$output, $error, $status]);
}, @hosts;
foreach my $host (@hosts) {
print $h3{$host}->[0], "\n";
print $h3{$host}->[1], "\n" if (defined $h3{$host}->[1]);
print "Exit status: ", $h3{$host}->[2], "\n\n";
}
Although MCE::Map comes to mind, one may want additional control when gathering data such
as retaining output order.
use MCE::Flow;
sub preserve_order {
my %tmp; my $order_id = 1; my $gather_ref = $_[0];
return sub {
$tmp{ (shift) } = \@_;
while (1) {
last unless exists $tmp{$order_id};
push @{ $gather_ref }, @{ delete $tmp{$order_id++} };
}
return;
};
}
## Workers persist for the most part after running. Though, not always
## the case and depends on Perl. Pass a reference to a subroutine if
## workers must persist; e.g. mce_flow { ... }, \&foo, 1..100000.
MCE::Flow->init(
chunk_size => 'auto', max_workers => 'auto'
);
for (1..2) {
my @m2;
mce_flow {
gather => preserve_order(\@m2)
},
sub {
my @a; my ($mce, $chunk_ref, $chunk_id) = @_;
## Compute the entire chunk data at once.
push @a, map { $_ * 2 } @{ $chunk_ref };
## Afterwards, invoke the gather feature, which
## will direct the data to the callback function.
MCE->gather(MCE->chunk_id, @a);
}, 1..100000;
print scalar @m2, "\n";
}
MCE::Flow->finish;
All 6 models support 'auto' for chunk_size unlike the Core API. Think of the models as the
basis for providing JIT for MCE. They create the instance, tune max_workers, and tune
chunk_size automatically regardless of the hardware.
The following does the same thing using the Core API. Workers persist after running.
use MCE;
sub preserve_order {
...
}
my $mce = MCE->new(
max_workers => 'auto', chunk_size => 8000,
user_func => sub {
my @a; my ($mce, $chunk_ref, $chunk_id) = @_;
## Compute the entire chunk data at once.
push @a, map { $_ * 2 } @{ $chunk_ref };
## Afterwards, invoke the gather feature, which
## will direct the data to the callback function.
MCE->gather(MCE->chunk_id, @a);
}
);
for (1..2) {
my @m2;
$mce->process({ gather => preserve_order(\@m2) }, [1..100000]);
print scalar @m2, "\n";
}
$mce->shutdown;
MANUAL SHUTDOWN
MCE::Flow->finish
MCE::Flow::finish
Workers remain persistent as much as possible after running. Shutdown occurs automatically
when the script terminates. Call finish when workers are no longer needed.
use MCE::Flow;
MCE::Flow->init(
chunk_size => 20, max_workers => 'auto'
);
mce_flow sub { ... }, 1..100;
MCE::Flow->finish;
INDEX
MCE, MCE::Core
AUTHOR
Mario E. Roy, <marioeroy AT gmail DOT com>