Programming languages that are executed by virtual machines face significant performance
 challenges beyond those confronted by traditional languages. First, portable program 
representations and dynamic language features, force the deferral of most optimizations 
until runtime, inducing runtime optimization overhead. Second, modular program representations 
preclude many forms of whole-program interprocedural optimization. Third, virtual machines 
incur additional costs for runtime services such as security guarantees and automatic memory 
management. To address these challenges, mainstream virtual machine implementations include 
substantial infrastructure for online profiling, dynamic compilation, and feedback-directed 
optimization. This talk will survey the state-of-the-art in the areas of dynamic compilation 
and adaptive optimization in virtual machines by debunking several misconceptions about these two topics.