Getting science done in the Julia language

Abstract: In this casual tutorial, I will introduce Julia — a programming language for scientific computing that can provide C/Fortran performance, while remaining dynamic and interactive. I will provide some live demonstrations, and then discuss advantages and disadvantages of Julia compared to the traditional two-language approach of Python + C/Fortran. I will also highlight some of the productive features of Julia for science, such as robust parallel CPU/GPU support, automatic differentiation, excellent package management, and multiple dispatch. I will provide a few examples from my own research, such as speedups of 2-4 over a similar Fortran code, and more than an order of magnitude improvement over a numpy-based implementation. Is Julia the right tool for your next problem?