High-Performance Computing with Python 3.x [Video]
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Free ChapterGetting Started with Faster and Efficient Python Code
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Parallel Programming in Python
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Using NumPy and SciPy to Speedup Computations
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Optimizing Python Code Using Cython
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Speeding Up Your Python Code Using Numba
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Distributed Computing Using Python
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Distributed Programming Using Dask
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Reactive Programming Using Python
About this
video
Python is a versatile programming language. Many industries are now using Python for high-performance computing projects.
This course will teach you how to use Python on parallel architectures. You'll learn to use the power of NumPy, SciPy, and Cython to speed up computation. Then you will get to grips with optimizing critical parts of the kernel using various tools. You will also learn how to optimize your programmer using Numba. You'll learn how to perform large-scale computations using Dask and implement distributed applications in Python; finally, you'll construct robust and responsive apps using Reactive programming.
By the end, you will have gained a solid knowledge of the most common tools to get you started on HPC with Python.
All code files are located on GitHub at this link https://github.com/PacktPublishing/High-Performance-Computing-with-Python-3.x
- Publication date:
- February 2019
- Publisher
- Packt
- Duration
- 4 hours 12 minutes
- ISBN
- 9781789956252