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Solving Google Code Jam problems with PyPy

Coding contests have been historically the playground of C++ and JAVA programmers, but with the advent of competitions like the Google Code Jam and the Facebook Hacker Cup many more languages, including Python, are making to the popularity charts with good results.

In the last couple of years the PyPy interpreter has given a serious boost to the competitiveness of Python thanks to its optimizing Just-In-Time compiler that makes tight loops and low-level memory management not just feasible but even close in efficiency to C++.

Coding for extreme performance with PyPy feels quite different from usual Python coding, but several general lessons can be learned about speed and memory efficiency, that can be applied to real world scenarios, especially in numerical and scientific computing.

The program of the training is:

  • set up a competition-ready PyPy environment
  • install competition grade libraries: NumPyPy, NeworkX, SciPy, ParallelPython
  • present the algorithmic solutions for a few Qualification Round and Round 1 problems
  • present notable JIT friendly implementation patterns
  • hands-on implementation of the solutions for the presented problems
  • lessons learned.

It is important for prospective attendees to familiarise with the Google Code Jam platform and learn how to submit the solutions to problems before the start of the training.

in on Wednesday 3 July at 09:00 See schedule

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240 minutes (inc Q&A)

Tagged as

numpy JIT networkx performance scientific-computing pypy
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