In this RCE Podcast, Brock Palen and Jeff Squyres discuss the Numba just-in-time compiler with Stanley Seibert from Continuum Analytics.
Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.
Stanley Seibert is a software developer for Continuum Analytics working on the Numba project. He received a Ph.D. in experimental high energy physics from the University of Texas at Austin, and worked as a postdoc at Los Alamos National Laboratory and University of Pennsylvania. His research interests include Monte Carlo algorithms, Bayesian methods, optical simulation of particle detectors, and promoting the use of Python and GPUs in scientific computing.
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