Strange Loop

Fast and Dynamic

Dynamic programming languages have long had a reputation for being slow and inefficient. The perception was that more expressive semantics necessarily require a cost in execution time. This is beginning to change as the performance gap between static and dynamic languages is visibly closing with the work being done on optimizing JIT compilers for JavaScript, Python and Lua. What does it take to make dynamic languages fast? What can we do to make them even faster? Follow me on a tour of dynamic language optimization from Smalltalk and the LISP machine to Google V8 and beyond.

Maxime Chevalier-Boisvert

Maxime Chevalier-Boisvert

Universite de Montreal

Maxime Cheva­lier-Boisvert holds a mas­ter's de­gree from McGill Uni­ver­sity and is cur­rently pur­su­ing a PhD at Uni­ver­sité de Montréal as part of the Dy­namic Lan­guage Team. Her area of study is compiler de­sign and op­ti­miza­tion, with a focus on dy­namic pro­gram­ming lan­guages, JIT com­pil­ers and type analy­sis.