December 8, 2015

Webinar attendees learn how to speed up Python programs using the integrated GPU on AMD APUs, using Numba, an open source just-in-time compiler, to generate faster code, all with pure Python. 

This webinar is presented by Dr. Stanley Seibert from Continuum Analytics, the creators of the Numba project. This webinar is tailored to an audience with intermediate Python and basic NumPy experience. 

Webinar attendees learn how to speed up Python programs using the integrated GPU on AMD APUs, using Numba, an open source just-in-time compiler, to generate faster code, all with pure Python.

This webinar is presented by Dr. Stanley Seibert from Continuum Analytics, the creators of the Numba project. This webinar is tailored to an audience with intermediate Python and basic NumPy experience. 

Numba version 0.21 adds support for a new target architecture: the Heterogenous System Architecture (HSA). HSA is a new standard aimed at allowing CPUs and GPUs to cooperate more closely and share a common memory space. AMD has implemented this standard in their recent Application Processor Units (APUs), and now Numba can compile code to run on these devices. It is Numba’s mission to help Python developers take advantage of the full power of their computers with the help of just-in-time (JIT) compilation. We’ve seen numerous examples where compiling numerical Python code has resulted in 2-5x speed improvements over code calling NumPy functions, and more than 150x over pure Python code.


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