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Update parallelism.rst #366

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8 changes: 4 additions & 4 deletions doc/source/parallelism.rst
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Expand Up @@ -6,7 +6,7 @@ by ensuring easy serialization of ``toolz`` functions and providing
architecture-agnostic parallel algorithms.

In practice ``toolz`` is developed against ``multiprocessing`` and
``IPython.parallel``.
``ipyparallel``.


Serialization
Expand All @@ -28,7 +28,7 @@ Example with parallel map

Most parallel processing tasks may be significantly accelerated using only a
parallel map operation. A number of high quality parallel map operations exist
in other libraries, notably ``multiprocessing``, ``IPython.parallel``, and
in other libraries, notably ``multiprocessing``, ``ipyparallel``, and
``threading`` (if your operation is not processor bound).

In the example below we extend our wordcounting solution with a parallel map.
Expand Down Expand Up @@ -66,7 +66,7 @@ multiprocessing, to distributed computation all with the same domain code.
# pmap = p.map

# Finish with distributed parallel map for big data
from IPython.parallel import Client
from ipyparallel import Client
p = Client()[:]
pmap = p.map_sync

Expand All @@ -92,4 +92,4 @@ parallel map function.

In particular we provide a parallel ``fold`` in ``toolz.sandbox.parallel.fold``.
This fold can work equally well with ``multiprocessing.Pool.map``
``threading.Pool.map`` or ``IPython.parallel``'s ``map_async``.
``threading.Pool.map`` or ``ipyparallel``'s ``map_async``.