Dynamic languages such as JavaScript are more difficult to compile than statically typed ones. Since no concrete type information is available, traditional compilers need to emit generic code that can handle all possible type combinations at runtime. We present an alternative compilation technique for dynamically-typed languages that identifies frequently executed loop traces at run-time and then generates machine code on the fly that is specialized for the actual dynamic types occurring on each path through the loop. Our method provides cheap inter-procedural type specialization, and an elegant and efficient way of incrementally compiling lazily discovered alternative paths through nested loops. We have implemented a dynamic compiler for JavaScript based on our technique and we have measured speedups of 10x and more for certain benchmark programs.

Dynamic languages such as JavaScript are more difficult to compile than statically typed ones. Since no concrete type information is available, traditional compilers need to emit generic code that can handle all possible type combinations at runtime. We present an alternative compilation technique for dynamically-typed languages that identifies frequently executed loop traces at run-time and then generates machine code on the fly that is specialized for the actual dynamic types occurring on each path through the loop. Our method provides cheap inter-procedural type specialization, and an elegant and efficient way of incrementally compiling lazily discovered alternative paths through nested loops. We have implemented a dynamic compiler for JavaScript based on our technique and we have measured speedups of 10x and more for certain benchmark programs.

How a sidekick scripting language for Java, created at Netscape in a ten-day hack, ships first as a de facto Web standard and eventually becomes the world's most widely used programming language. This paper tells the story of the creation, design, evolution, and standardization of the JavaScript language over the period of 1995--2015. But the story is not only about the technical details of the language. It is also the story of how people and organizations competed and collaborated to shape the JavaScript language which dominates the Web of 2020.

Today's web applications are pushing the limits of modern web browsers. The emergence of the browser as the platform of choice for rich client-side applications has shifted the use of in-browser JavaScript from small scripting programs to large computationally intensive application logic. For many web applications, JavaScript performance has become one of the bottlenecks preventing the development of even more interactive client side applications. While traditional just-in-time compilation is successful for statically typed virtual machine based languages like Java, compiling JavaScript turns out to be a challenging task. Many JavaScript programs and scripts are short-lived, and users expect a responsive browser during page loading. This leaves little time for compilation of JavaScript to generate machine code.We present a trace-based just-in-time compiler for JavaScript that uses run-time profiling to identify frequently executed code paths, which are compiled to executable machine code. Our approach increases execution performance by up to 116% by decomposing complex JavaScript instructions into a simple Forth-based representation, and then recording the actually executed code path through this low-level IR. Giving developers more computational horsepower enables a new generation of innovative web applications.

JavaScript is a highly dynamic language for web-based applications. Many innovative implementation techniques for improving its speed and responsiveness have been developed in recent years. Industry benchmarks such as WebKit SunSpider are often cited as a measure of the efficacy of these techniques. However, recent studies have shown that these benchmarks fail to accurately represent the dynamic nature of modern JavaScript applications, and thus may be poor predictors of real-world performance. Worse, they may lead to the development of optimizations which are unhelpful for real applications. Our goal in this work is to develop techniques to automate the creation of realistic and representative benchmarks from existing web applications. We propose a record-and-replay approach to capture JavaScript sessions which has sufficient fidelity to accurately recreate key characteristics of the original application, and at the same time is sufficiently flexible that a recording produced on one platform can be replayed on a different one. We describe JS-BENCH, a flexible tool for workload capture and benchmark generation, and demonstrate its use in creating eight benchmarks based on popular sites. Using a variety of runtime metrics collected with instrumented versions of Firefox, Internet Explorer, and Safari, we show that workloads created by JSBENCH match the behavior of web applications.