Our project had the goal of developing the components of a modular virtual machine / language runtime that will make it easier for designers of programming languages, particularly dynamic languages, to get high-performing implementations off the ground. In the current state of the art, the up-front investment required to build a high- performance language runtime is so great that only a small fraction of deserving programming languages ever gain implementations beyond a simple interpreter. We largely succeeded in bringing down the effort required for making a dynamically typed programming language perform well. Of particular significance is a new technique "Iterator Peeling" that leads to significant speedups of programming languages such as Python. We believe that it will be broadly adopted by language implementors. We have also made significant progress on optimizing information-flow tracking for programming languages that support certain kinds of security type systems. Information-flow tracking could solve many of the security problems that plague current web browsers, but current browsers do not support this feature because it has too much runtime overhead. The new techniques that we have developed under this award make information-flow tracking substantially more efficient, making it practical to incorporate them directly into web browsers. If this were adopted by browser vendors, it would lead to an altogether safer web browsing experience for everyone.