Python* is a popular open-source scripting language known for its easy-to-learn syntax and active developer community. Performance, however, remains a key drawback due to Python being an interpreted language and the implementation of the GIL lock.
This joint webinar with Continuum Analytics explores tools and techniques to boost the performance of your Python applications, that can be easily accessed by all Python developers and users.
The talk will accelerate numerical computations in numpy, scipy, & scikit-learn with native libraries like Intel® MKL and Intel® DAAL and how to write high performance Python with JIT compilers like Numba. Intel® Distribution for Python* is an easy-to-install, optimized Python distribution that includes the popular NumPy* and SciPy* stack packages used for scientific, engineering, and data analysis. It tunes and leverages the powerful Intel® Math Kernel Library to offer significant performance gains, enhancing the performance profile of your application.
For example, DGEMM functions deliver 3x speed-ups on single-core and show impressive scalability on multiple cores. The easy, out-of-the box installation saves you time and effort, so even a novice Python user can focus on the application at hand, rather than setting up the Python infrastructure.
This joint webinar with Continuum Analytics explores tools and techniques to boost the performance of your Python applications, that can be easily accessed by all Python developers and users.
The talk will accelerate numerical computations in numpy, scipy, & scikit-learn with native libraries like Intel® MKL and Intel® DAAL and how to write high performance Python with JIT compilers like Numba. Intel® Distribution for Python* is an easy-to-install, optimized Python distribution that includes the popular NumPy* and SciPy* stack packages used for scientific, engineering, and data analysis. It tunes and leverages the powerful Intel® Math Kernel Library to offer significant performance gains, enhancing the performance profile of your application.
For example, DGEMM functions deliver 3x speed-ups on single-core and show impressive scalability on multiple cores. The easy, out-of-the box installation saves you time and effort, so even a novice Python user can focus on the application at hand, rather than setting up the Python infrastructure.
When: Tue, May 3, 2016 9:00 AM - 10:00 AM PDT