Unleash a faster Python on your data

Tags: Data Analytics, HPC, Intel, Performance, Python

Get real performance results and download the free Intel Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more.

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Download the Free Intel Python Distribution for Better Performance

If you use Python* for HPC, data analytics, or data science, you love the productivity features and the large ecosystem of packages available to quickly build applications. However, getting faster performance from Python that's close to native code is difficult, requiring software development expertise and tremendous patience.

Well, good news! Intel has done it for you. Get real performance results and download the free Intel^® Distribution for Python* that includes everything you need for blazing-fast computing, analytics, machine learning, and more. Use Intel Python with existing code, and you're all set for a significant performance boost. Easy. Imagine that!

Why is Intel Distribution so fast? Here are three reasons:

1.	The core computing packages, Numpy, SciPy, and scikit-learn, are accelerated under the hood with powerful, multithreaded native performance libraries such as Intel^® Math Kernel Library, Intel^® Data Analytics Acceleration Library, and others, to deliver native code-like performance results to Python.
2.	We leverage Intel® hardware capabilities using multiple cores and the latest Intel® Advanced Vector Extensions (Intel® AVX) instructions, including Intel® AVX-512.
3.	The Intel Python team reimplemented select algorithms to dramatically improve their performance. Examples include NumPy FFT and random number generation, SciPy FFT, and more.

All this is available out of the box with the free Intel Distribution for Python. Intel Python is available via conda, pip, or docker, and our all-in-one installer.

Check out the webinar Is Python* Almost as Fast as Native Code? Believe It! to see the Intel Python Distribution in action. We take an application to the next level of performance using native libraries, performance analyzers, and optimization of Python/C/C++ code.