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draft thinking hybrid section added

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[SVN r17242]
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Ralf W. Grosse-Kunstleve
2003-02-05 19:58:43 +00:00
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doc/PyConDC_2003/bpl.txt
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@@ -740,6 +740,60 @@ manipulation of these high-level types from C++::
d["lucky_number"] = 13;
list l = d.keys();
=================
Thinking hybrid
=================
For many applications runtime performance considerations are very
important. This is particularly true for most scientific applications.
Often the performance considerations dictate the use of a compiled
language for the core algorithms. Traditionally the decision to use a
particular programming language is an exclusive one. Because of the
practical and mental difficulties of combining different languages many
systems are written in just one language. This is quite unfortunate
because the price payed for runtime performance is typically a
significant overhead due to static typing. For example, our experience
shows that developing maintainable C++ code is typically much more
time-consuming and requires much more hard-earned working experience
than developing useful Python code. A related observation is that many
compiled packages are augmented by some type of rudimentary scripting
layer. These ad hoc solutions clearly show that many times a compiled
language alone does not get the job done. On the other hand it is also
clear that a pure Python implementation is too slow for numerically
intensive production code.
Boost.Python enables us to *think hybrid* when developing new
applications. For example, Python can be used for rapidly prototyping a
new application. Python's ease of use and the large pool of standard
libraries give us a head start on the way to a first working system. If
necessary, the working procedure can be used to discover the
rate-limiting algorithms. To maximize performance these can be
reimplemented in C++, together with the Boost.Python bindings needed to
tie them back into the existing higher-level procedure.
Of course, this *top-down* approach is less attractive if it is clear
from the start that many algorithms will eventually have to be
implemented in a compiled language. Fortunately Boost.Python also
enables us to pursue a *bottom-up* approach. We have used this approach
very successfully in the development of a toolbox for scientific
applications (scitbx) that we will describe elsewhere. The toolbox
started out mainly as a library of C++ classes with Boost.Python
bindings, and for a while the growth was mainly concentrated on the C++
parts. However, as the toolbox is becoming more complete, more and more
newly added functionality can be implemented in Python. We expect this
trend to continue, as illustrated qualitatively in this figure:
.. image:: python_cpp_mix.png
This figure shows the ratio of newly added C++ and Python code over
time as new algorithms are implemented. We expect this ratio to level
out near 70% Python. The increasing ability to solve new problems
mostly with the easy-to-use Python language rather than a necessarily
more arcane statically typed language is the return on the investment
of learning how to use Boost.Python. The ability to solve some problems
entirely using only Python will enable a larger group of people to
participate in the rapid development of new applications.
=============
Conclusions
=============