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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 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.