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Performance Python for Numerical Algorithms

Summary

This talk is about several approaches to implement high performing numerical algorithms and applications in Python. It introduces into approaches like vectorization, multi-threading, parallelization (CPU/GPU), dynamic compiling, high throughput IO operations. The approach is a practical one in that every approach is illustrated by specific Python examples.

Description

This talk is about several approaches to implement high performing numerical algorithms and applications in Python. It introduces into approaches like multi-threading, parallelization (CPU/GPU), dynamic compiling, high throughput IO operations.

The approach is a practical one in that every approach is illustrated by specific Python examples.

The talk uses, among others, the following libraries:

  • NumPy
  • numexpr
  • IPython.Parallel
  • Numba
  • NumbaPro
  • PyTables

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