A Need for Speed: Accelerating your math with vectorization and NumPy
by Kyle Kotowick, Ph.D.
While Python is an extremely versatile language, it isn't exactly known for its blazing performance. When developing math-intensive applications, particularly on low-power devices such as single-board computers, this can become a real issue. This talk provides an introduction to vectorization and libraries designed to support it (such as Numpy), giving you the tools you need to eliminate those pesky for loops and achieve a drastic performance boost.
About the Author
Dr. Kotowick is the founder of a Canadian consulting and development firm focusing on prototype and POC development for complex systems. He holds a Ph.D. in Human Systems Integration from MIT's Department of Aeronautics and Astronautics, which he completed in the Computer Science and Artificial Intelligence Laboratory. He is currently the Director of Information Technology for an ambulance service. Past roles have spanned serving as a consultant, architect, and developer for global firms, startups, and universities as well as researcher for military navigation systems and for life support systems in space. He specializes in working with enterprise clients to define requirements and explore possible solutions, as well as in leading the development of project architecture, cloud services, and back-end software. He volunteers as a team leader and technology specialist for World Health Organization Emergency Medical Teams deployed to disaster zones, and has a passion for exploring the uses of technology in high-risk environments.