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Achieving Parallelism in Intel Distribution for Python with Numba

The rapid growth in popularity of Python as a programming language for mathematics, science, and engineering applications has been amazing. Not only is it easy to learn, but there is a vast treasure of packaged open source libraries out there targeted at just about every computational domain imaginable. This sponsored post from Intel highlights how today’s enterprises can achieve high levels of parallelism in large scale Python applications using the Intel Distribution for Python with Numba. 

Making Python Fly: Accelerate Performance Without Recoding

Developers are increasingly besieged by the big data deluge. Intel Distribution for Python uses tried-and-true libraries like the Intel Math Kernel Library (Intel MKL)and the Intel Data Analytics Acceleration Library to make Python code scream right out of the box – no recoding required. Intel highlights some of the benefits dev teams can expect in this sponsored post.

Accelerated Python for Data Science

The Intel Distribution for Python takes advantage of the Intel® Advanced Vector Extensions (Intel® AVX) and multiple cores in the latest Intel architectures. By utilizing the highly optimized Intel MKL BLAS and LAPACK routines, key functions run up to 200 times faster on servers and 10 times faster on desktop systems. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.

HPC Podcast Looks at Intel’s Pending Distribution of Python

In this HPC Podcast, Don Kinhorn and Chris Stevens from Puget Systems discuss the boom in FPGAs at SC15 as well as Intel’s announcement that the company is going to maintain a build of Python. “Python is a pretty important programming language. It has a large and growing number of useful libraries for mathematical/scientific computing and machine learning, NumPy, SciPy, pandas, Scikit-learn, PySpark, theano, and more.”