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remove obsolete FAQ

It only had questions referring to the IPython.kernel code that doesn't exist anymore.

faq should be handled on a Wiki anyway.
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1 parent b7d96c6 commit 0be704e262e3d797c197ae82b85bc04e54b58f4e @minrk minrk committed Jun 30, 2011
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-.. _faq:
-Frequently asked questions
-General questions
-Questions about parallel computing with IPython
-Will IPython speed my Python code up?
-Yes and no. When converting a serial code to run in parallel, there often many
-difficulty questions that need to be answered, such as:
-* How should data be decomposed onto the set of processors?
-* What are the data movement patterns?
-* Can the algorithm be structured to minimize data movement?
-* Is dynamic load balancing important?
-We can't answer such questions for you. This is the hard (but fun) work of parallel
-computing. But, once you understand these things IPython will make it easier for you to
-implement a good solution quickly. Most importantly, you will be able to use the
-resulting parallel code interactively.
-With that said, if your problem is trivial to parallelize, IPython has a number of
-different interfaces that will enable you to parallelize things is almost no time at
-all. A good place to start is the ``map`` method of our :class:`MultiEngineClient`.
-What is the best way to use MPI from Python?
-What about all the other parallel computing packages in Python?
-Some of the unique characteristic of IPython are:
-* IPython is the only architecture that abstracts out the notion of a
- parallel computation in such a way that new models of parallel computing
- can be explored quickly and easily. If you don't like the models we
- provide, you can simply create your own using the capabilities we provide.
-* IPython is asynchronous from the ground up (we use `Twisted`_).
-* IPython's architecture is designed to avoid subtle problems
- that emerge because of Python's global interpreter lock (GIL).
-* While IPython's architecture is designed to support a wide range
- of novel parallel computing models, it is fully interoperable with
- traditional MPI applications.
-* IPython has been used and tested extensively on modern supercomputers.
-* IPython's networking layers are completely modular. Thus, is
- straightforward to replace our existing network protocols with
- high performance alternatives (ones based upon Myranet/Infiniband).
-* IPython is designed from the ground up to support collaborative
- parallel computing. This enables multiple users to actively develop
- and run the *same* parallel computation.
-* Interactivity is a central goal for us. While IPython does not have
- to be used interactivly, it can be.
-.. _Twisted:
-Why The IPython controller a bottleneck in my parallel calculation?
-A golden rule in parallel computing is that you should only move data around if you
-absolutely need to. The main reason that the controller becomes a bottleneck is that
-too much data is being pushed and pulled to and from the engines. If your algorithm
-is structured in this way, you really should think about alternative ways of
-handling the data movement. Here are some ideas:
-1. Have the engines write data to files on the locals disks of the engines.
-2. Have the engines write data to files on a file system that is shared by
- the engines.
-3. Have the engines write data to a database that is shared by the engines.
-4. Simply keep data in the persistent memory of the engines and move the
- computation to the data (rather than the data to the computation).
-5. See if you can pass data directly between engines using MPI.
@@ -23,7 +23,6 @@ Contents
- faq.txt
.. htmlonly::

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