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IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant

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Chapter 5. High-performance Computing

In this chapter, we will cover the following topics:

  • Accelerating pure Python code with Numba and Just-In-Time compilation
  • Accelerating array computations with Numexpr
  • Wrapping a C library in Python with ctypes
  • Accelerating Python code with Cython
  • Optimizing Cython code by writing less Python and more C
  • Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP
  • Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA
  • Writing massively parallel code for heterogeneous platforms with OpenCL
  • Distributing Python code across multiple cores with IPython
  • Interacting with asynchronous parallel tasks in IPython
  • Parallelizing code with MPI in IPython
  • Trying the Julia language in the notebook ...

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