Usage

Nomenclature

Keyword	Description
Future(s)	The Future class encapsulates the asynchronous execution of a callable.
Broker	Process dispatching Futures.
Worker	Process executing Futures.
Root	The worker executing the root Future, your main program.

Architecture diagram

The future(s) distribution over workers is done by a variation of the Broker pattern. In such a pattern, workers act as independant elements that interact with a broker to mediate their communications.

Mapping API

The philosophy of SCOOP is loosely built around the futures module proposed by PEP 3148. It primarily defines a map() and a submit() function allowing asynchroneous computation that SCOOP will propagate to its workers.

Map

A map() function applies multiple parameters to a single function. For example, if you want to apply the abs() function to every number of a list:

import random
data = [random.randint(-1000, 1000) for r in range(1000)]

# Without Map
result = []
for i in data:
  result.append(abs(i))

# Using a Map
result = list(map(abs, data))

SCOOP’s map() returns a generator iterating over the results in the same order as its inputs. It can thus act as a parallel substitute to the standard map(), for instance:

# Script to be launched with: python -m scoop scriptName.py
import random
from scoop import futures
data = [random.randint(-1000, 1000) for r in range(1000)]

if __name__ == '__main__':
    # Python's standard serial function
    dataSerial = list(map(abs, data))

    # SCOOP's parallel function
    dataParallel = list(futures.map(abs, data))

    assert dataSerial == dataParallel

Warning

In your root program, you must check if __name__ == __main__ as shown above. Failure to do so will result in every worker trying to run their own instance of the program. This ensures that every worker waits for parallelized tasks spawned by the root worker.

Note

Your callable function passed to SCOOP must be picklable in its entirety.

Note that the pickle module is limited to top level functions and classes as stated in the documentation.

Note

Functions executed using SCOOP must return a value.

Note

Keep in mind that objects are not shared between workers and that changes made to an object in a function are not seen by other workers.

Submit

SCOOP’s submit() returns a Future instance. This allows a finer control over the Futures, such as out-of-order results retrieval.

Reduction API

mapReduce

The mapReduce() function of SCOOP allows to parallelize a reduction function after applying the aforementionned map() function. It returns a single value.

A reduction function takes the map results and applies a function cumulatively to it. For example, applying reduce(lambda x, y: x+y, [“a”, “b”, “c”, “d”]) would execute ((((“a”)+”b”)+”c”)+”d”) give you the result “abcd”

Read the standard Python reduce function for more information.

A common reduction usage consist of a sum as the following example:

# Script to be launched with: python -m scoop scriptName.py
import random
import operator
from scoop import futures
data = [random.randint(-1000, 1000) for r in range(1000)]


if __name__ == '__main__':
    # Python's standard serial function
    serialSum = sum(map(abs, data))

    # SCOOP's parallel function
    parallelSum = futures.mapReduce(abs, operator.add, data)

    assert serialSum == parallelSum

Note

You can pass any arbitrary reduction function, not only operator ones.

Object sharing API

Sharing constant objects between workers is available using the shared module.

Its functionnalities are summarised as such:

from scoop import futures, shared

def myParallelFunc(inValue):
    myValue = shared.getConst('myValue')
    return inValue + myValue


if __name__ == '__main__':
    shared.setCont(myValue=5)
    print(list(futures.map(myParallelFunc, range(10))))

Note

A constant can only be defined once on the entire pool of workers.

Examples

Examples are available in the examples/ directory of SCOOP.

Please refer to the Examples page where detailed explanations are available.

How to launch SCOOP programs

The scoop module spawns the needed broker and workers on a given list of computers, including remote ones via ssh.

Programs using SCOOP need to be launched with the -m scoop parameter passed to Python, as such:

cd scoop/examples/
python -m scoop fullTree.py

Here is a list of the parameters that can be passed to SCOOP:

python -m scoop --help
usage: python -m scoop [-h]
                       [--hosts [HOSTS [HOSTS ...]] | --hostfile HOSTFILE]
                       [--path PATH] [--nice NICE]
                       [--verbose] [--log LOG] [-n N]
                       [-e] [--broker-hostname BROKER_HOSTNAME]
                       [--python-executable PYTHON_EXECUTABLE]
                       [--pythonpath PYTHONPATH]
                       executable ...

Starts a parallel program using SCOOP.

positional arguments:
  executable            The executable to start with SCOOP
  args                  The arguments to pass to the executable

optional arguments:
  -h, --help            show this help message and exit
  --hosts [HOSTS [HOSTS ...]], --host [HOSTS [HOSTS ...]]
                        The list of hosts. The first host will execute the
                        root program. (default is 127.0.0.1)
  --hostfile HOSTFILE   The hostfile name
  --path PATH, -p PATH  The path to the executable on remote hosts (default
                        is local directory)
  --nice NICE           *nix niceness level (-20 to 19) to run the executable
  --verbose, -v         Verbosity level of this launch script (-vv for more)
  --log LOG             The file to log the output (default is stdout)
  -n N                  Total number of workers to launch on the hosts.
                        Workers are spawned sequentially over the hosts.
                        (ie. -n 3 with 2 hosts will spawn 2 workers on the
                        first host and 1 on the second.) (default: Number of
                        CPUs on current machine)
  -e                    Activate ssh tunnels to route toward the broker
                        sockets over remote connections (may eliminate routing
                        problems and activate encryption but slows down
                        communications)
  --broker-hostname BROKER_HOSTNAME
                        The externally routable broker hostname / ip (defaults
                        to the local hostname)
  --python-executable PYTHON_EXECUTABLE
                        The python executable with which to execute the script
  --pythonpath PYTHONPATH
                        The PYTHONPATH environment variable (default is
                        current PYTHONPATH)

A remote workers example may be as follow:

python -m scoop --hostfile hosts -vv -n 6 your_program.py [your arguments]

Argument	Meaning
-m scoop	Mandatory Uses SCOOP to run program.
–hostfile	hosts is a file containing a list of host to launch SCOOP
-vv	Double verbosity flag.
-n 6	Launch a total of 6 workers.
your_program.py	The program to be launched.
[your arguments]	The arguments that needs to be passed to your program.

Note

Your local hostname must be externally routable for remote hosts to be able to connect to it. If you don’t have a DNS properly set up on your local network or a system hosts file, consider using the --broker-hostname argument to provide your externally routable IP or DNS name to SCOOP. You may as well be interested in the -e argument for testing purposes.

Hostfile format

You can specify the hosts with a hostfile and pass it to SCOOP using the --hostfile argument. The hostfile should use the following syntax:

hostname_or_ip 4
other_hostname 5
third_hostname 2

The name being the system hostname and the number being the number of workers to launch on this host.

Using a list of host

You can also use a list of host with the --host [...] flag. In this case, you must put every host separated by a space the number of time you wish to have a worker on each of the node. For example:

python -m scoop --host machine_a machine_a machine_b machine_b your_program.py

This example would start two workers on machine_a and two workers on machine_b.

Choosing the number of workers

The number of workers started should be equal to the number of cores you have on each machine. If you wish to start more or less workers than specified in your hostfile or in your hostlist, you can use the -n parameter.

Note

The -n parameter overrides any previously specified worker amount.

If -n is less than the sum of workers specified in the hostfile or hostlist, the workers are launched in batch by host until the parameter is reached. This behavior may ignore latters hosts.

If -n is more than the sum of workers specified in the hostfile or hostlist, the remaining workers are distributed using a Round-Robin algorithm. Each host will increment its worker amount until the parameter is reached.

Be aware that tinkering with this parameter may hinder performances. The default value choosen by SCOOP (one worker by physical core) is generaly a good value.

Startup scripts (cluster or grid)

You must provide a startup script on systems using a scheduler. Here are some example startup scripts using different grid task managers. They are available in the examples/submitFiles directory.

Note

Please note that these are only examples. Refer to the documentation of your scheduler for the list of arguments needed to run the task on your grid or cluster.

Torque (Moab & Maui)

Here is an example of a submit file for Torque:

#!/bin/bash
## Please refer to your grid documentation for available flags. This is only an example.
#PBS -l procs=16
#PBS -V
#PBS -N SCOOPJob

# Path to your executable. For example, if you extracted SCOOP to $HOME/downloads/scoop
cd $HOME/downloads/scoop/examples

# Add any addition to your environment variables like PATH. For example, if your local python installation is in $HOME/python
export PATH=$HOME/python/bin:$PATH

# If, instead, you are using the python offered by the system, you can stipulate it's library path via PYTHONPATH
#export PYTHONPATH=$HOME/wanted/path/lib/python+version/site-packages/:$PYTHONPATH
# Or use VirtualEnv via virtualenvwrapper here:
#workon yourenvironment

# Launch SCOOP using the hosts
python -m scoop -vv fullTree.py

Sun Grid Engine (SGE)

Here is an example of a submit file for SGE:

#!/bin/bash
## Please refer to your grid documentation for available flags. This is only an example.
#$ -l h_rt=300
#$ -pe test 16
#$ -S /bin/bash
#$ -cwd
#$ -notify

# Path to your executable. For example, if you extracted SCOOP to $HOME/downloads/scoop
cd $HOME/downloads/scoop/examples

# Add any addition to your environment variables like PATH. For example, if your local python installation is in $HOME/python
export PATH=$HOME/python/bin:$PATH

# If, instead, you are using the python offered by the system, you can stipulate it's library path via PYTHONPATH
#export PYTHONPATH=$HOME/wanted/path/lib/python+version/site-packages/:$PYTHONPATH
# Or use VirtualEnv via virtualenvwrapper here:
#workon yourenvironment

# Launch the remotes workers
python -m scoop -vv fullTree.py

Pitfalls

Program scope

As a good Python practice (see PEP 395), you should always wrap the executable part of your program using:

if __name__ == '__main__':

This is mandatory when using parallel frameworks such as multiprocessing or SCOOP. Every worker execute your main module with a __name__ variable different than __main__ then awaits orders given by the root node to execute available functions.

Also, only functions or classes declared at the top level of your program are picklables. This is a limitation of Python’s pickle module. Here are some examples of non-working map invocations:

from scoop import futures


class myClass(object):
    @staticmethod
    def myFunction(x):
        return x


if __name__ == '__main__':
    def mySecondFunction(x):
        return x

    # Both of these calls won't work because Python pickle won't be able to
    # pickle or unpickle the function references.
    wrongCall1 = futures.map(myClass.myFunction, [1, 2, 3, 4, 5])
    wrongCall2 = futures.map(mySecondFunction, [1, 2, 3, 4, 5])

Evaluation laziness

The map() and submit() will distribute their Futures both locally and remotely. Futures executed locally will be computed upon access (iteration for the map() and result() for submit()). Futures distributed remotely will be executed right away.

Large datasets

Every parameter sent to a function by a map() or submit() gets serialized and sent within the Future to its worker. It results in slow speeds and network overload when sending large elements as a parameter to your function(s).

You should consider using a global variable in your module scope for passing large elements; it will then be loaded on launch by every worker and won’t overload your network.

Incorrect:

from scoop import futures


def mySum(inData):
    """The worker will receive all its data from network."""
    return sum(inData)

if __name__ == '__main__':
    data = [[i for i in range(x, x + 1000)] for x in range(0, 8001, 1000)]
    results = list(futures.map(mySum, data))

Better:

from scoop import futures

data = [[i for i in range(x, x + 1000)] for x in range(0, 8001, 1000)]


def mySum(inIndex):
    """The worker will only receive an index from network."""
    return sum(data[inIndex])

if __name__ == '__main__':
    results = list(futures.map(mySum, range(len(data))))

SCOOP and greenlets

Warning

Since SCOOP uses greenlets to schedule and run futures. Programs that use their own greenlets won’t work with SCOOP. However, you should consider replacing the greenlets in your code by SCOOP functions.