The HipHop Virtual Machine (HHVM) is a JIT compiler and runtime for PHP. While PHP values are dynamically typed, real programs often have latent types that are useful for optimization once discovered. Some types can be proven through static analysis, but limitations in the aheadof-time approach leave some types to be discovered at run time. And even though many values have latent types, PHP programs can also contain polymorphic variables and expressions, which must be handled without catastrophic slowdown. HHVM discovers latent types by structuring its JIT around the concept of a tracelet. A tracelet is approximately a basic block specialized for a particular set of run-time types for its input values. Tracelets allow HHVM to exactly and efficiently learn the types observed by the program, while using a simple compiler. This paper shows that this approach enables HHVM to achieve high levels of performance, without sacrificing compatibility or interactivity.

Optionally typed languages enable direct performance comparisons between untyped and type annotated source code. We present a comprehensive performance evaluation of two different JIT compilers in the context of ActionScript, a production-quality optionally typed language. One JIT compiler is optimized for quick compilation rather than JIT compiled code performance. The second JIT compiler is a more aggressively optimizing compiler, performing both high-level and low-level optimizations.We evaluate both JIT compilers directly on the same benchmark suite, measuring their performance changes across fully typed, partially typed, and untyped code. Such evaluations are especially relevant to dynamically typed languages such as JavaScript, which are currently evaluating the idea of adding optional type annotations. We demonstrate that low-level optimizations rarely accelerate the program enough to pay back the investment into performing them in an optionally typed language. Our experiments and data demonstrate that high-level optimizations are required to improve performance by any significant amount.