Efficient and General On-Stack Replacement for Aggressive Program Specialization

Efficient invalidation and dynamic replacement of executing code – on-stack replacement (OSR), is necessary to facilitate effective, aggressive, specialization of object-oriented programs that are dynamically loaded, incrementally compiled, and garbage collected. Extant OSR mechanisms restrict the performance potential of program specialization since their implementations are special-purpose and restrict compiler optimization. In this paper, we present a novel, general-purpose OSR mechanism that is more amenable to optimization than prior approaches. In particular, we decouple the OSR implementation from the optimization process and update the program state information incrementally during optimization. Our OSR implementation efficiently enables the use of code specializations that are invalidated by any event – including those external to program code execution. We improve code quality over the extant, state-of-the-art, resulting in performance gains of 1-31%, and 9 % on average

Optimizing JavaScript Engines for Modern-day Workloads

In modern times, we have seen tremendous increase in popularity and usage of web-based applications. Applications such as presentation softwareand word processors, which were traditionally considered desktop applications are being ported to the web by compiling them to JavaScript. Since JavaScript is the de facto language of the web, JavaScript engine performance significantly affects the overall web application experience. JavaScript, initially intended solely as a client-side scripting language for web browsers, is now being used to implement server-side web applications (node.js) that traditionally have been written in languages like Java. Web application developers expect "C"-like performance out of their applications. Thus, there is a need to reevaluate the optimization strategies implemented in the modern day engines.Thesis statement: I propose that by using run-time and ahead-of-time profiling and type specialization techniques it is possible to improve the performance of JavaScript engines to cater to the needs of modern-day workloads.In this dissertation, we present an improved synergistic type specialization strategy for optimized JavaScript code execution, implemented on top of a research JavaScript engine called MuscalietJS. Our technique combines type feedback and type inference to reinforce and augment each other in a unique way. We then present a novel deoptimization strategy that enables type specialized code generation on top of typed, stack-based virtual machines like CLR. We also describe a server-side offline profiling technique to collect profile information for web application which helps client JavaScript engines (running in the browser) avoid deoptimizations and improve performance of the applications. Finally, we describe a technique to improve the performance of server-side JavaScript code by making use of intelligent profile caching and two new type stability heuristics