September 2014
Intermediate to advanced
512 pages
12h 39m
English
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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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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.