Comparing the performance of programming languages is difficult because they differ in many aspects including preferred programming abstractions, available frameworks, and their runtime systems. Nonetheless, the question about relative performance comes up repeatedly in the research community, industry, and wider audience of enthusiasts.This paper presents 14 benchmarks and a novel methodology to assess the compiler effectiveness across language implementations. Using a set of common language abstractions, the benchmarks are implemented in Java, JavaScript, Ruby, Crystal, Newspeak, and Smalltalk. We show that the benchmarks exhibit a wide range of characteristics using language-agnostic metrics. Using four different languages on top of the same compiler, we show that the benchmarks perform similarly and therefore allow for a comparison of compiler effectiveness across languages. Based on anecdotes, we argue that these benchmarks help language implementers to identify performance bugs and optimization potential by comparing to other language implementations.

We are in the multi-core era. Dynamically-typed languages are in widespread use, but their support for multithreading still lags behind. One of the reasons is that the sophisticated techniques they use to efficiently represent their dynamic object models are often unsafe in multithreaded environments. This paper defines safety requirements for dynamic object models in multithreaded environments. Based on these requirements, a language-agnostic and thread-safe object model is designed that maintains the efficiency of sequential approaches. This is achieved by ensuring that field reads do not require synchronization and field updates only need to synchronize on objects shared between threads. Basing our work on JRuby+Truffle, we show that our safe object model has zero overhead on peak performance for thread-local objects and only 3% average overhead on parallel benchmarks where field updates require synchronization. Thus, it can be a foundation for safe and efficient multithreaded VMs for a wide range of dynamic languages.