docs/source/parallelz/parallel_multiengine.txt

.. _parallelmultiengine:

===============================
IPython's multiengine interface
===============================

The multiengine interface represents one possible way of working with a set of
IPython engines. The basic idea behind the multiengine interface is that the
capabilities of each engine are directly and explicitly exposed to the user.
Thus, in the multiengine interface, each engine is given an id that is used to
identify the engine and give it work to do. This interface is very intuitive
and is designed with interactive usage in mind, and is thus the best place for
new users of IPython to begin.

Starting the IPython controller and engines
===========================================

To follow along with this tutorial, you will need to start the IPython
controller and four IPython engines. The simplest way of doing this is to use
the :command:`ipclusterz` command::

    $ ipclusterz -n 4
    
For more detailed information about starting the controller and engines, see
our :ref:`introduction <ip1par>` to using IPython for parallel computing.

Creating a ``Client`` instance
==============================

The first step is to import the IPython :mod:`IPython.zmq.parallel.client`
module and then create a :class:`.Client` instance:

.. sourcecode:: ipython

    In [1]: from IPython.zmq.parallel import client
    
    In [2]: rc = client.Client()

This form assumes that the controller was started on localhost with default
configuration. If not, the location of the controller must be given as an
argument to the constructor:

.. sourcecode:: ipython

    # for a visible LAN controller listening on an external port:
    In [2]: rc = client.Client('tcp://192.168.1.16:10101')
    # for a remote controller at my.server.com listening on localhost:
    In [3]: rc = client.Client(sshserver='my.server.com')
    

To make sure there are engines connected to the controller, use can get a list
of engine ids:

.. sourcecode:: ipython

    In [3]: rc.ids
    Out[3]: set([0, 1, 2, 3])

Here we see that there are four engines ready to do work for us.

Quick and easy parallelism
==========================

In many cases, you simply want to apply a Python function to a sequence of
objects, but *in parallel*. The client interface provides a simple way
of accomplishing this: useing the builtin :func:`map` and the ``@remote``
function decorator.

Parallel map
------------

Python's builtin :func:`map` functions allows a function to be applied to a
sequence element-by-element. This type of code is typically trivial to
parallelize. In fact, since IPython's interface is all about functions anyway, you can just use the builtin :func:`map`, or a client's :map: method:

.. sourcecode:: ipython

    In [62]: serial_result = map(lambda x:x**10, range(32))

    In [66]: parallel_result = rc.map(lambda x: x**10, range(32))

    In [67]: serial_result==parallel_result
    Out[67]: True


.. note::

    The client's own version of :meth:`map` or that of :class:`.DirectView` do
    not do any load balancing. For a load balanced version, use a
    :class:`LoadBalancedView`, or a :class:`ParallelFunction` with
    `targets=None`.

.. seealso::
    
    :meth:`map` is implemented via :class:`.ParallelFunction`.

Remote function decorator
-------------------------

Remote functions are just like normal functions, but when they are called,
they execute on one or more engines, rather than locally. IPython provides
some decorators:

.. sourcecode:: ipython

    In [10]: @rc.remote(block=True)
       ....: def f(x):
       ....:     return 10.0*x**4
       ....: 

    In [11]: map(f, range(32))    # this is done in parallel
    Out[11]: 
    [0.0,10.0,160.0,...]

See the docstring for the :func:`parallel` and :func:`remote` decorators for
options.

Calling Python functions
========================

The most basic type of operation that can be performed on the engines is to
execute Python code or call Python functions. Executing Python code can be
done in blocking or non-blocking mode (non-blocking is default) using the
:meth:`execute` method, and calling functions can be done via the
:meth:`.View.apply` method.

Blocking execution
------------------

In blocking mode, the :class:`.DirectView` object (called ``dview`` in
these examples) submits the command to the controller, which places the
command in the engines' queues for execution. The :meth:`apply` call then
blocks until the engines are done executing the command:

.. sourcecode:: ipython

    In [2]: rc.block=True
    In [3]: dview = rc[:] # A DirectView of all engines
    In [4]: dview['a'] = 5
    

    In [5]: dview['b'] = 10

    In [6]: dview.apply_bound(lambda x: a+b+x, 27)
    Out[6]: {0: 42, 1: 42, 2: 42, 3: 42}

Python commands can be executed on specific engines by calling execute using
the ``targets`` keyword argument, or creating a :class:`DirectView` instance
by index-access to the client:

.. sourcecode:: ipython

    In [6]: rc.execute('c=a+b',targets=[0,2])

    In [7]: rc.execute('c=a-b',targets=[1,3])

    In [8]: rc[:]['c']
    Out[8]: {0: 15, 1: -5, 2: 15, 3: -5}

.. note::

    Note that every call to ``rc.<meth>(...,targets=x)`` can be made via
    ``rc[<x>].<meth>(...)``, which constructs a View object. The only place
    where this differs in in :meth:`apply`. The :class:`Client` takes many
    arguments to apply, so it requires `args` and `kwargs` to be passed as
    individual arguments. Extended options such as `bound`,`targets`, and
    `block` are controlled by the attributes of the :class:`View` objects, so
    they can provide the much more convenient
    :meth:`View.apply(f,*args,**kwargs)`, which simply calls
    ``f(*args,**kwargs)`` remotely.

This example also shows one of the most important things about the IPython
engines: they have a persistent user namespaces. The :meth:`apply` method can
be run in either a bound or unbound way. The default for a View is to be
unbound, unless called by the :meth:`apply_bound` method:

.. sourcecode:: ipython

    In [9]: rc[:]['b'] = 5 # assign b to 5 everywhere
    
    In [10]: v0 = rc[0]
    
    In [12]: v0.apply_bound(lambda : b)
    Out[12]: 5
    
    In [13]: v0.apply(lambda : b)
    ---------------------------------------------------------------------------
    RemoteError                               Traceback (most recent call last)
    /home/you/<ipython-input-34-21a468eb10f0> in <module>()
    ----> 1 v0.apply(lambda : b)
    ...
    RemoteError: NameError(global name 'b' is not defined)
    Traceback (most recent call last):
      File "/Users/minrk/dev/ip/mine/IPython/zmq/parallel/streamkernel.py", line 294, in apply_request
        exec code in working, working
      File "<string>", line 1, in <module>
      File "<ipython-input-34-21a468eb10f0>", line 1, in <lambda>
    NameError: global name 'b' is not defined
    

Specifically, `bound=True` specifies that the engine's namespace is to be used
for execution, and `bound=False` specifies that the engine's namespace is not
to be used (hence, 'b' is undefined during unbound execution, since the
function is called in an empty namespace). Unbound execution is often useful
for large numbers of atomic tasks, which prevents bloating the engine's
memory, while bound execution lets you build on your previous work.


Non-blocking execution
----------------------

In non-blocking mode, :meth:`apply` submits the command to be executed and
then returns a :class:`AsyncResult` object immediately. The
:class:`AsyncResult` object gives you a way of getting a result at a later
time through its :meth:`get` method. 

.. Note::

    The :class:`AsyncResult` object provides the exact same interface as 
    :py:class:`multiprocessing.pool.AsyncResult`.  See the 
    `official Python documentation <http://docs.python.org/library/multiprocessing#multiprocessing.pool.AsyncResult>`_
    for more.


This allows you to quickly submit long running commands without blocking your
local Python/IPython session:

.. sourcecode:: ipython
    
    # define our function
    In [35]: def wait(t):
       ....:     import time
       ....:     tic = time.time()
       ....:     time.sleep(t)
       ....:     return time.time()-tic
    
    # In blocking mode
    In [6]: rc.apply('import time')

    # In non-blocking mode
    In [7]: pr = rc[:].apply_async(wait, 2)

    # Now block for the result
    In [8]: pr.get()
    Out[8]: [2.0006198883056641, 1.9997570514678955, 1.9996809959411621, 2.0003249645233154]

    # Again in non-blocking mode
    In [9]: pr = rc[:].apply_async(wait, 10)

    # Poll to see if the result is ready
    In [10]: pr.ready()
    Out[10]: False
    
    # ask for the result, but wait a maximum of 1 second:
    In [45]: pr.get(1)
    ---------------------------------------------------------------------------
    TimeoutError                              Traceback (most recent call last)
    /home/you/<ipython-input-45-7cd858bbb8e0> in <module>()
    ----> 1 pr.get(1)

    /path/to/site-packages/IPython/zmq/parallel/asyncresult.pyc in get(self, timeout)
         62                 raise self._exception
         63         else:
    ---> 64             raise error.TimeoutError("Result not ready.")
         65 
         66     def ready(self):

    TimeoutError: Result not ready.

.. Note::

    Note the import inside the function. This is a common model, to ensure
    that the appropriate modules are imported where the task is run.

Often, it is desirable to wait until a set of :class:`AsyncResult` objects
are done. For this, there is a the method :meth:`barrier`. This method takes a
tuple of :class:`AsyncResult` objects (or `msg_ids`) and blocks until all of the associated
results are ready:

.. sourcecode:: ipython

    In [72]: rc.block=False

    # A trivial list of AsyncResults objects
    In [73]: pr_list = [rc[:].apply_async(wait, 3) for i in range(10)]

    # Wait until all of them are done
    In [74]: rc.barrier(pr_list)

    # Then, their results are ready using get_result or the r attribute
    In [75]: pr_list[0].get()
    Out[75]: [2.9982571601867676, 2.9982588291168213, 2.9987530708312988, 2.9990990161895752]
    


The ``block`` and ``targets`` keyword arguments and attributes
--------------------------------------------------------------

.. warning::

    This is different now, I haven't updated this section.
    -MinRK

Most methods(like :meth:`apply`) accept
``block`` and ``targets`` as keyword arguments. As we have seen above, these
keyword arguments control the blocking mode and which engines the command is
applied to. The :class:`Client` class also has :attr:`block` and
:attr:`targets` attributes that control the default behavior when the keyword
arguments are not provided. Thus the following logic is used for :attr:`block`
and :attr:`targets`:

* If no keyword argument is provided, the instance attributes are used.
* Keyword argument, if provided override the instance attributes.

The following examples demonstrate how to use the instance attributes:

.. sourcecode:: ipython

    In [16]: rc.targets = [0,2]

    In [17]: rc.block = False

    In [18]: pr = rc.execute('a=5')

    In [19]: pr.r
    Out[19]: 
    <Results List>
    [0] In [6]: a=5
    [2] In [6]: a=5

    # Note targets='all' means all engines
    In [20]: rc.targets = 'all'

    In [21]: rc.block = True

    In [22]: rc.execute('b=10; print b')
    Out[22]: 
    <Results List>
    [0] In [7]: b=10; print b
    [0] Out[7]: 10

    [1] In [6]: b=10; print b
    [1] Out[6]: 10

    [2] In [7]: b=10; print b
    [2] Out[7]: 10

    [3] In [6]: b=10; print b
    [3] Out[6]: 10

The :attr:`block` and :attr:`targets` instance attributes also determine the
behavior of the parallel magic commands.


Parallel magic commands
-----------------------

.. warning::

    The magics have not been changed to work with the zeromq system. ``%px``
    and ``%autopx`` do work, but ``%result`` does not. %px and %autopx *do
    not* print stdin/out.

We provide a few IPython magic commands (``%px``, ``%autopx`` and ``%result``)
that make it more pleasant to execute Python commands on the engines
interactively. These are simply shortcuts to :meth:`execute` and
:meth:`get_result`. The ``%px`` magic executes a single Python command on the
engines specified by the :attr:`targets` attribute of the
:class:`MultiEngineClient` instance (by default this is ``'all'``):

.. sourcecode:: ipython

    # Create a DirectView for all targets
    In [22]: dv = rc[:]
    
    # Make this DirectView active for parallel magic commands
    In [23]: dv.activate()

    In [24]: dv.block=True

    In [25]: import numpy

    In [26]: %px import numpy
    Parallel execution on engines: [0, 1, 2, 3]
    Out[26]:{0: None, 1: None, 2: None, 3: None}

    In [27]: %px a = numpy.random.rand(2,2)
    Parallel execution on engines: [0, 1, 2, 3]

    In [28]: %px ev = numpy.linalg.eigvals(a)
    Parallel execution on engines: [0, 1, 2, 3]

    In [28]: dv['ev']
    Out[44]: {0: array([ 1.09522024, -0.09645227]), 
            1: array([ 1.21435496, -0.35546712]), 
            2: array([ 0.72180653,  0.07133042]), 
            3: array([  1.46384341e+00,   1.04353244e-04])}

.. Note::

    ``%result`` doesn't work

The ``%result`` magic gets and prints the stdin/stdout/stderr of the last
command executed on each engine. It is simply a shortcut to the
:meth:`get_result` method:

.. sourcecode:: ipython

    In [29]: %result
    Out[29]:
    <Results List>
    [0] In [10]: print numpy.linalg.eigvals(a)
    [0] Out[10]: [ 1.28167017  0.14197338]

    [1] In [9]: print numpy.linalg.eigvals(a)
    [1] Out[9]: [-0.14093616  1.27877273]

    [2] In [10]: print numpy.linalg.eigvals(a)
    [2] Out[10]: [-0.37023573  1.06779409]

    [3] In [9]: print numpy.linalg.eigvals(a)
    [3] Out[9]: [ 0.83664764 -0.25602658]

The ``%autopx`` magic switches to a mode where everything you type is executed
on the engines given by the :attr:`targets` attribute:

.. sourcecode:: ipython

    In [30]: dv.block=False

    In [31]: %autopx
    Auto Parallel Enabled
    Type %autopx to disable

    In [32]: max_evals = []
    <IPython.zmq.parallel.asyncresult.AsyncResult object at 0x17b8a70>

    In [33]: for i in range(100):
       ....:     a = numpy.random.rand(10,10)
       ....:     a = a+a.transpose()
       ....:     evals = numpy.linalg.eigvals(a)
       ....:     max_evals.append(evals[0].real)
       ....:
       ....:
    <IPython.zmq.parallel.asyncresult.AsyncResult object at 0x17af8f0>

    In [34]: %autopx
    Auto Parallel Disabled

    In [35]: dv.block=True

    In [36]: px ans= "Average max eigenvalue is: %f"%(sum(max_evals)/len(max_evals))
    Parallel execution on engines: [0, 1, 2, 3]
    
    In [37]: dv['ans']
    Out[37]: {0 : 'Average max eigenvalue is:  10.1387247332',
              1 : 'Average max eigenvalue is:  10.2076902286',
              2 : 'Average max eigenvalue is:  10.1891484655',
              3 : 'Average max eigenvalue is:  10.1158837784',}


.. Note::

    Multiline ``%autpx`` gets fouled up by NameErrors, because IPython
    currently introspects too much.


Moving Python objects around
============================

In addition to calling functions and executing code on engines, you can
transfer Python objects to and from your IPython session and the engines. In
IPython, these operations are called :meth:`push` (sending an object to the
engines) and :meth:`pull` (getting an object from the engines). 

Basic push and pull
-------------------

Here are some examples of how you use :meth:`push` and :meth:`pull`:

.. sourcecode:: ipython

    In [38]: rc.push(dict(a=1.03234,b=3453))
    Out[38]: {0: None, 1: None, 2: None, 3: None}

    In [39]: rc.pull('a')
    Out[39]: {0: 1.03234, 1: 1.03234, 2: 1.03234, 3: 1.03234}

    In [40]: rc.pull('b',targets=0)
    Out[40]: 3453

    In [41]: rc.pull(('a','b'))
    Out[41]: {0: [1.03234, 3453], 1: [1.03234, 3453], 2: [1.03234, 3453], 3:[1.03234, 3453]}
    
    # zmq client does not have zip_pull
    In [42]: rc.zip_pull(('a','b'))
    Out[42]: [(1.03234, 1.03234, 1.03234, 1.03234), (3453, 3453, 3453, 3453)]

    In [43]: rc.push(dict(c='speed'))
    Out[43]: {0: None, 1: None, 2: None, 3: None}

In non-blocking mode :meth:`push` and :meth:`pull` also return
:class:`AsyncResult` objects:

.. sourcecode:: ipython

    In [47]: rc.block=False

    In [48]: pr = rc.pull('a')

    In [49]: pr.get()
    Out[49]: [1.03234, 1.03234, 1.03234, 1.03234]




Dictionary interface
--------------------

Since a 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, this uses :meth:`push` and
:meth:`pull`:

.. sourcecode:: ipython

    In [50]: rc.block=True

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

    In [52]: rc[:]['a']
    Out[52]: {0: ['foo', 'bar'], 1: ['foo', 'bar'], 2: ['foo', 'bar'], 3: ['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]: rc.scatter('a',range(16))
    Out[58]: {0: None, 1: None, 2: None, 3: None}

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

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

Other things to look at
=======================

How to do parallel list comprehensions
--------------------------------------

In many cases list comprehensions are nicer than using the map function. While
we don't have fully parallel list comprehensions, it is simple to get the
basic effect using :meth:`scatter` and :meth:`gather`:

.. sourcecode:: ipython

    In [66]: rc.scatter('x',range(64))
    Out[66]: {0: None, 1: None, 2: None, 3: None}

    In [67]: px y = [i**10 for i in x]
    Executing command on Controller
    Out[67]: 

    In [68]: y = rc.gather('y')

    In [69]: print y
    [0, 1, 1024, 59049, 1048576, 9765625, 60466176, 282475249, 1073741824,...]

Parallel exceptions
-------------------

In the multiengine interface, parallel commands can raise Python exceptions,
just like serial commands. But, it is a little subtle, because a single
parallel command can actually raise multiple exceptions (one for each engine
the command was run on). To express this idea, the MultiEngine interface has a
:exc:`CompositeError` exception class that will be raised in most cases. The
:exc:`CompositeError` class is a special type of exception that wraps one or
more other types of exceptions. Here is how it works:

.. sourcecode:: ipython

    In [76]: rc.block=True

    In [77]: rc.execute('1/0')
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<ipython console> in <module>()

    /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in execute(self, lines, targets, block)
        432         targets, block = self._findTargetsAndBlock(targets, block)
        433         result = blockingCallFromThread(self.smultiengine.execute, lines,
    --> 434             targets=targets, block=block)
        435         if block:
        436             result = ResultList(result)

    /ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw)
         72             result.raiseException()
         73         except Exception, e:
    ---> 74             raise e
         75     return result
         76 

    CompositeError: one or more exceptions from call to method: execute
    [0:execute]: ZeroDivisionError: integer division or modulo by zero
    [1:execute]: ZeroDivisionError: integer division or modulo by zero
    [2:execute]: ZeroDivisionError: integer division or modulo by zero
    [3:execute]: ZeroDivisionError: integer division or modulo by zero

Notice how the error message printed when :exc:`CompositeError` is raised has
information about the individual exceptions that were raised on each engine.
If you want, you can even raise one of these original exceptions:

.. sourcecode:: ipython

    In [80]: try:
       ....:     rc.execute('1/0')
       ....: except client.CompositeError, e:
       ....:     e.raise_exception()
       ....:     
       ....:     
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<ipython console> in <module>()

    /ipython1-client-r3021/ipython1/kernel/error.pyc in raise_exception(self, excid)
        156             raise IndexError("an exception with index %i does not exist"%excid)
        157         else:
    --> 158             raise et, ev, etb
        159 
        160 def collect_exceptions(rlist, method):

    ZeroDivisionError: integer division or modulo by zero

If you are working in IPython, you can simple type ``%debug`` after one of
these :exc:`CompositeError` exceptions is raised, and inspect the exception
instance:

.. sourcecode:: ipython

    In [81]: rc.execute('1/0')
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<ipython console> in <module>()

    /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in execute(self, lines, targets, block)
        432         targets, block = self._findTargetsAndBlock(targets, block)
        433         result = blockingCallFromThread(self.smultiengine.execute, lines,
    --> 434             targets=targets, block=block)
        435         if block:
        436             result = ResultList(result)

    /ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw)
         72             result.raiseException()
         73         except Exception, e:
    ---> 74             raise e
         75     return result
         76 

    CompositeError: one or more exceptions from call to method: execute
    [0:execute]: ZeroDivisionError: integer division or modulo by zero
    [1:execute]: ZeroDivisionError: integer division or modulo by zero
    [2:execute]: ZeroDivisionError: integer division or modulo by zero
    [3:execute]: ZeroDivisionError: integer division or modulo by zero

    In [82]: %debug
    > 
    
    /ipython1-client-r3021/ipython1/kernel/twistedutil.py(74)blockingCallFromThread()
         73         except Exception, e:
    ---> 74             raise e
         75     return result

    # With the debugger running, e is the exceptions instance.  We can tab complete
    # on it and see the extra methods that are available.
    ipdb> e.
    e.__class__         e.__getitem__       e.__new__           e.__setstate__      e.args
    e.__delattr__       e.__getslice__      e.__reduce__        e.__str__           e.elist
    e.__dict__          e.__hash__          e.__reduce_ex__     e.__weakref__       e.message
    e.__doc__           e.__init__          e.__repr__          e._get_engine_str   e.print_tracebacks
    e.__getattribute__  e.__module__        e.__setattr__       e._get_traceback    e.raise_exception
    ipdb> e.print_tracebacks()
    [0:execute]: 
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<string> in <module>()

    ZeroDivisionError: integer division or modulo by zero

    [1:execute]: 
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<string> in <module>()

    ZeroDivisionError: integer division or modulo by zero

    [2:execute]: 
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<string> in <module>()

    ZeroDivisionError: integer division or modulo by zero

    [3:execute]: 
    ---------------------------------------------------------------------------
    ZeroDivisionError                         Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<string> in <module>()

    ZeroDivisionError: integer division or modulo by zero

.. note::

    The above example appears to be broken right now because of a change in 
    how we are using Twisted.

All of this same error handling magic even works in non-blocking mode:

.. sourcecode:: ipython

    In [83]: rc.block=False

    In [84]: pr = rc.execute('1/0')

    In [85]: pr.get()
    ---------------------------------------------------------------------------
    CompositeError                            Traceback (most recent call last)

    /ipython1-client-r3021/docs/examples/<ipython console> in <module>()

    /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in _get_r(self)
        170 
        171     def _get_r(self):
    --> 172         return self.get_result(block=True)
        173 
        174     r = property(_get_r)

    /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in get_result(self, default, block)
        131                 return self.result
        132         try:
    --> 133             result = self.client.get_pending_deferred(self.result_id, block)
        134         except error.ResultNotCompleted:
        135             return default

    /ipython1-client-r3021/ipython1/kernel/multiengineclient.pyc in get_pending_deferred(self, deferredID, block)
        385 
        386     def get_pending_deferred(self, deferredID, block):
    --> 387         return blockingCallFromThread(self.smultiengine.get_pending_deferred, deferredID, block)
        388 
        389     def barrier(self, pendingResults):

    /ipython1-client-r3021/ipython1/kernel/twistedutil.pyc in blockingCallFromThread(f, *a, **kw)
         72             result.raiseException()
         73         except Exception, e:
    ---> 74             raise e
         75     return result
         76 

    CompositeError: one or more exceptions from call to method: execute
    [0:execute]: ZeroDivisionError: integer division or modulo by zero
    [1:execute]: ZeroDivisionError: integer division or modulo by zero
    [2:execute]: ZeroDivisionError: integer division or modulo by zero
    [3:execute]: ZeroDivisionError: integer division or modulo by zero