Slides from my PyConUK 2016 session on not using Python fpr computation. Chapel is introduced as an option in a microservices approach. The codes displayed were from https://github.com/russel/Pi_Quadrature

1. Copyright © 2016 Russel Winder 1
Fast Python? Don’t Bother
Russel Winder
russel@winder.org.uk
https://www.russel.org.uk
@russel_winder

2. Copyright © 2016 Russel Winder 2
There is a rumour I only come to PyCon UK to
talk about languages other than Python…

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…this is not true…

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…well not entirely.

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A gentle rant, but…

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…a bit of a rant nonetheless.

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Python is…
● A dynamically typed (or not) language.
● Quite popular, in data science, and data visualisation.
● Quite popular for sys. admin.
● Quite popular for Web-y stuff.
● Having it’s development driven by IDEs.
Type signatures.

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Slow

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Python is not…
● A high performance computational language.
● A language in which you can write parallel programs.

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Maybe the GIL is a good thing,
it stops us using Python when we shouldn’t.

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Why?

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Parallelism

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So how to do computation?
● Cython
● Numba
● NumPy

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Cython
● Write Python and annotate the code to make it appear
like C so it is actually C by source-to-source translation.
Hats off to Pyrex.

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Why not just write C if you want to write C?

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Because C is a programming language for
writing operating systems, not applications?

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What we want is Fortran or even FORTRAN.

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Or not.

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Numba
● Write Python code and decorate with decorator so as to
perform non-Python translation via LLVM to native code.

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Does Python really have
native code semantics?

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Does RPython really have
native code semantics?

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No

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NumPy
● And the whole SciPy, Pandas, etc. kit and caboodle.
● A C subsystem, providing opaque data types and
operations well integrated with Python as an API.

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It’s just a C system with a nice API.
Lots of niceness to the API.

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Is C good enough for computation
in the modern era?

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No

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Why?

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Parallelism

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Multi-multicore processors
GPGPU

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OpenCL
Vulkan
CUDA

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Actors
CSP
Dataflow
Data Parallelism

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Parallel Processing
● Threads and thread pools:
– C – but very low level.
– C++ – better than C.
– Java: Akka, Quasar,
Hadoop, Apache Spark,
GPars.
– D – actors and data
parallelism.
● Partitioned Global
Address Space (PGAS):
– Chapel
– X10
There are man, many more:
Pony, Nim, etc.

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Where does Python fit in?

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It doesn’t for the computation…

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…but there is a way forward:

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Microservices Architecture

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Processes communicating over a network.
Processes on same computer communicating using
inter-process communication.

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Processes communicating is the only way pure Python
code can create parallelism so it must be Pythonic.

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Intermix Python processes with non-Python ones.

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Intermix:
● Python ● C
● C++
● Rust
● D
● Chapel

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Peek at D and Chapel.
https://github.com/russel/Pi_Quadrature

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Follow Up…
Short hands-on workshop:
Monday 2016-09-19T14:30+01:00
Room A

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Fast Python? Don’t Bother
Russel Winder
russel@winder.org.uk
https://www.russel.org.uk
@russel_winder