docs/source/parallelz/parallel_details.txt

.. _parallel_details:

==========================================
Details of Parallel Computing with IPython
==========================================

.. note::

    There are still many sections to fill out


Caveats
=======

First, some caveats about the detailed workings of parallel computing with 0MQ and IPython.

Non-copying sends and numpy arrays
----------------------------------

When numpy arrays are passed as arguments to apply or via data-movement methods, they are not
copied. This means that you must be careful if you are sending an array that you intend to work
on. PyZMQ does allow you to track when a message has been sent so you can know when it is safe
to edit the buffer, but IPython only allows for this.

It is also important to note that the non-copying receive of a message is *read-only*. That
means that if you intend to work in-place on an array that you have sent or received, you must
copy it. This is true for both numpy arrays sent to engines and numpy arrays retrieved as
results.

The following will fail:

.. sourcecode:: ipython

    In [3]: A = numpy.zeros(2)
    
    In [4]: def setter(a):
       ...: a[0]=1
       ...: return a

    In [5]: rc[0].apply_sync(setter, A)
    ---------------------------------------------------------------------------
    RemoteError                               Traceback (most recent call last)
    ...
    RemoteError: RuntimeError(array is not writeable)
    Traceback (most recent call last):
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/streamkernel.py", line 329, in apply_request
        exec code in working, working
      File "<string>", line 1, in <module>
      File "<ipython-input-14-736187483856>", line 2, in setter
    RuntimeError: array is not writeable

If you do need to edit the array in-place, just remember to copy the array if it's read-only.
The :attr:`ndarray.flags.writeable` flag will tell you if you can write to an array.

.. sourcecode:: ipython

    In [3]: A = numpy.zeros(2)
    
    In [4]: def setter(a):
       ...:     """only copy read-only arrays"""
       ...:     if not a.flags.writeable:
       ...:         a=a.copy()
       ...:     a[0]=1
       ...:     return a

    In [5]: rc[0].apply_sync(setter, A)
    Out[5]: array([ 1.,  0.])
    
    # note that results will also be read-only:
    In [6]: _.flags.writeable
    Out[6]: False

If you want to safely edit an array in-place after *sending* it, you must use the `track=True` flag.  IPython always performs non-copying sends of arrays, which return immediately.  You
must instruct IPython track those messages *at send time* in order to know for sure that the send has completed.  AsyncResults have a :attr:`sent` property, and :meth:`wait_on_send` method
for checking and waiting for 0MQ to finish with a buffer.

.. sourcecode:: ipython

    In [5]: A = numpy.random.random((1024,1024))
    
    In [6]: view.track=True
    
    In [7]: ar = view.apply_async(lambda x: 2*x, A)
    
    In [8]: ar.sent
    Out[8]: False
    
    In [9]: ar.wait_on_send() # blocks until sent is True


What is sendable?
-----------------

If IPython doesn't know what to do with an object, it will pickle it. There is a short list of
objects that are not pickled: ``buffers``, ``str/bytes`` objects, and ``numpy``
arrays. These are handled specially by IPython in order to prevent the copying of data. Sending
bytes or numpy arrays will result in exactly zero in-memory copies of your data (unless the data
is very small).

If you have an object that provides a Python buffer interface, then you can always send that
buffer without copying - and reconstruct the object on the other side in your own code. It is
possible that the object reconstruction will become extensible, so you can add your own
non-copying types, but this does not yet exist.

Closures
********

Just about anything in Python is pickleable. The one notable exception is objects (generally
functions) with *closures*. Closures can be a complicated topic, but the basic principal is that
functions that refer to variables in their parent scope have closures.

An example of a function that uses a closure:

.. sourcecode:: python

    def f(a):
        def inner():
            # inner will have a closure
            return a
        return echo
    
    f1 = f(1)
    f2 = f(2)
    f1() # returns 1
    f2() # returns 2

f1 and f2 will have closures referring to the scope in which `inner` was defined, because they
use the variable 'a'. As a result, you would not be able to send ``f1`` or ``f2`` with IPython.
Note that you *would* be able to send `f`. This is only true for interactively defined
functions (as are often used in decorators), and only when there are variables used inside the
inner function, that are defined in the outer function. If the names are *not* in the outer
function, then there will not be a closure, and the generated function will look in
``globals()`` for the name:

.. sourcecode:: python

    def g(b):
        # note that `b` is not referenced in inner's scope
        def inner():
            # this inner will *not* have a closure
            return a
        return echo
    g1 = g(1)
    g2 = g(2)
    g1() # raises NameError on 'a'
    a=5
    g2() # returns 5

`g1` and `g2` *will* be sendable with IPython, and will treat the engine's namespace as
globals().  The :meth:`pull` method is implemented based on this principal.  If we did not
provide pull, you could implement it yourself with `apply`, by simply returning objects out
of the global namespace:

.. sourcecode:: ipython

    In [10]: view.apply(lambda : a)
    
    # is equivalent to
    In [11]: view.pull('a')

Running Code
============

There are two principal units of execution in Python: strings of Python code (e.g. 'a=5'),
and Python functions.  IPython is designed around the use of functions via the core
Client method, called `apply`.

Apply
-----

The principal method of remote execution is :meth:`apply`, of View objects. The Client provides
the full execution and communication API for engines via its low-level
:meth:`send_apply_message` method.

f : function
    The fuction to be called remotely
args : tuple/list
    The positional arguments passed to `f`
kwargs : dict
    The keyword arguments passed to `f`
block : bool (default: self.block)
    Whether to wait for the result, or return immediately.
    False:
        returns AsyncResult
    True:
        returns actual result(s) of f(*args, **kwargs)
        if multiple targets:
            list of results, matching `targets`
track : bool
    whether to track non-copying sends.
    [default False]

targets : int,list of ints, 'all', None
    Specify the destination of the job.
    if None:
        Submit via Task queue for load-balancing.
    if 'all':
        Run on all active engines
    if list:
        Run on each specified engine
    if int:
        Run on single engine
    Not eht

balanced : bool, default None
    whether to load-balance.  This will default to True
    if targets is unspecified, or False if targets is specified.
    
    If `balanced` and `targets` are both specified, the task will
    be assigne to *one* of the targets by the scheduler.
    
after : Dependency or collection of msg_ids
    Only for load-balanced execution (targets=None)
    Specify a list of msg_ids as a time-based dependency.
    This job will only be run *after* the dependencies
    have been met.
    
follow : Dependency or collection of msg_ids
    Only for load-balanced execution (targets=None)
    Specify a list of msg_ids as a location-based dependency.
    This job will only be run on an engine where this dependency
    is met.

timeout : float/int or None
    Only for load-balanced execution (targets=None)
    Specify an amount of time (in seconds) for the scheduler to
    wait for dependencies to be met before failing with a
    DependencyTimeout.

execute and run
---------------

For executing strings of Python code, :class:`DirectView`s also provide an :meth:`execute` and a
:meth:`run` method, which rather than take functions and arguments, take simple strings.
`execute` simply takes a string of Python code to execute, and sends it to the Engine(s). `run`
is the same as `execute`, but for a *file*, rather than a string. It is simply a wrapper that
does something very similar to ``execute(open(f).read())``.

.. note::

    TODO: Example

Views
=====

The principal extension of the :class:`~parallel.client.Client` is the
:class:`~parallel.view.View` class. The client

Two of apply's keyword arguments are set at the construction of the View, and are immutable for
a given View: `balanced` and `targets`. `balanced` determines whether the View will be a
:class:`.LoadBalancedView` or a :class:`.DirectView`, and `targets` will be the View's `targets`
attribute.  Attempts to change this will raise errors.

Views are cached by targets/class, so requesting a view multiple times will always return the
*same object*, not create a new one:

.. sourcecode:: ipython

    In [3]: v1 = rc.load_balanced_view([1,2,3])
    In [4]: v2 = rc.load_balanced_view([1,2,3])

    In [5]: v2 is v1
    Out[5]: True


DirectView
----------

The :class:`.DirectView` is the class for the IPython :ref:`Multiplexing Interface
<parallel_multiengine>`.

Creating a DirectView
*********************

DirectViews can be created in two ways, by index access to a client, or by a client's
:meth:`view` method.  Index access to a Client works in a few ways.  First, you can create
DirectViews to single engines simply by accessing the client by engine id:

.. sourcecode:: ipython

    In [2]: rc[0]
    Out[2]: <DirectView 0>

You can also create a DirectView with a list of engines:

.. sourcecode:: ipython

    In [2]: rc[0,1,2]
    Out[2]: <DirectView [0,1,2]>

Other methods for accessing elements, such as slicing and negative indexing, work by passing
the index directly to the client's :attr:`ids` list, so:

.. sourcecode:: ipython

    # negative index
    In [2]: rc[-1]
    Out[2]: <DirectView 3>
    
    # or slicing:
    In [3]: rc[::2]
    Out[3]: <DirectView [0,2]>

are always the same as:

.. sourcecode:: ipython

    In [2]: rc[rc.ids[-1]]
    Out[2]: <DirectView 3>
    
    In [3]: rc[rc.ids[::2]]
    Out[3]: <DirectView [0,2]>

Also note that the slice is evaluated at the time of construction of the DirectView, so the 
targets will not change over time if engines are added/removed from the cluster.  Requesting
two views with the same slice at different times will *not* necessarily return the same View
if the number of engines has changed.

Execution via DirectView
************************

The DirectView is the simplest way to work with one or more engines directly (hence the name).  


Data movement via DirectView
****************************

Since a Python namespace is just a :class:`dict`, :class:`DirectView` objects provide
dictionary-style access by key and methods such as :meth:`get` and
:meth:`update` for convenience. This make the remote namespaces of the engines
appear as a local dictionary. Underneath, these methods call :meth:`apply`:

.. sourcecode:: ipython

    In [51]: dview['a']=['foo','bar']

    In [52]: dview['a']
    Out[52]: [ ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'], ['foo', 'bar'] ]

Scatter and gather
------------------

Sometimes it is useful to partition a sequence and push the partitions to
different engines. In MPI language, this is know as scatter/gather and we
follow that terminology. However, it is important to remember that in
IPython's :class:`Client` class, :meth:`scatter` is from the
interactive IPython session to the engines and :meth:`gather` is from the
engines back to the interactive IPython session. For scatter/gather operations
between engines, MPI should be used:

.. sourcecode:: ipython

    In [58]: dview.scatter('a',range(16))
    Out[58]: [None,None,None,None]

    In [59]: dview['a']
    Out[59]: [ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], [12, 13, 14, 15] ]

    In [60]: dview.gather('a')
    Out[60]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]


    
LoadBalancedView
----------------

The :class:`.LoadBalancedView` 


Data Movement
=============

push

pull

Reference

Results
=======

AsyncResults are the primary class

get_result

results,metadata

Querying the Hub
================

The Hub sees all traffic that may pass through the schedulers between engines and clients.
It does this so that it can track state, allowing multiple clients to retrieve results of
computations submitted by their peers, as well as persisting the state to a database.

queue_status

    You can check the status of the queues of the engines with this command.

result_status

purge_results

Controlling the Engines
=======================

There are a few actions you can do with Engines that do not involve execution.  These
messages are sent via the Control socket, and bypass any long queues of waiting execution
jobs

abort

    Sometimes you may want to prevent a job you have submitted from actually running. The method
    for this is :meth:`abort`. It takes a container of msg_ids, and instructs the Engines to not
    run the jobs if they arrive. The jobs will then fail with an AbortedTask error.

clear

    You may want to purge the Engine(s) namespace of any data you have left in it.  After
    running `clear`, there will be no names in the Engine's namespace

shutdown

    You can also instruct engines (and the Controller) to terminate from a Client.  This 
    can be useful when a job is finished, since you can shutdown all the processes with a
    single command.

Synchronization
===============

Since the Client is a synchronous object, events do not automatically trigger in your
interactive session - you must poll the 0MQ sockets for incoming messages.  Note that 
this polling *does not* actually make any network requests.  It simply performs a `select`
operation, to check if messages are already in local memory, waiting to be handled.

The method that handles incoming messages is :meth:`spin`. This method flushes any waiting
messages on the various incoming sockets, and updates the state of the Client.

If you need to wait for particular results to finish, you can use the :meth:`wait` method,
which will call :meth:`spin` until the messages are no longer outstanding. Anything that
represents a collection of messages, such as a list of msg_ids or one or more AsyncResult
objects, can be passed as argument to wait. A timeout can be specified, which will prevent
the call from blocking for more than a specified time, but the default behavior is to wait
forever.



The client also has an `outstanding` attribute - a ``set`` of msg_ids that are awaiting replies.
This is the default if wait is called with no arguments - i.e. wait on *all* outstanding
messages.


.. note::

    TODO wait example

Map
===

Many parallel computing problems can be expressed as a `map`, or running a single program with a
variety of different inputs. Python has a built-in :py-func:`map`, which does exactly this, and
many parallel execution tools in Python, such as the built-in :py-class:`multiprocessing.Pool`
object provide implementations of `map`. All View objects provide a :meth:`map` method as well,
but the load-balanced and direct implementations differ.

Views' map methods can be called on any number of sequences, but they can also take the `block`
and `bound` keyword arguments, just like :meth:`~client.apply`, but *only as keywords*.

.. sourcecode:: python

    dview.map(*sequences, block=None)


* iter, map_async, reduce

Decorators and RemoteFunctions
==============================

@parallel

@remote

RemoteFunction

ParallelFunction

Dependencies
============

@depend

@require

Dependency