Which of My Transient Type Checks Are Not (Almost) Free?

One form of type checking used in gradually typed language is transient type checking: whenever an object ‘flows’ through code with a type annotation, the object is dynamically checked to ensure it has the methods required by the annotation. Just-in-time compilation and optimisation in virtual machines can eliminate much of the overhead of run-time transient type checks. Unfortunately this optimisation is not uniform: some type checks will significantly decrease, or even increase, a program’s performance. In this paper, we refine the so called “Takikawa” protocol, and use it to identify which type annotations have the greatest effects on performance. In particular, we show how graphing the performance of such benchmarks when varying which type annotations are present in the source code can be used to discern potential patterns in performance. We demonstrate our approach by testing the Moth virtual machine: for many of the benchmarks where Moth’s transient type checking impacts performance, we have been able to identify one or two specific type annotations that are the likely cause. Without these type annotations, the performance impact of transient type checking becomes negligible. Using our technique programmers can optimise programs by removing expensive type checks, and VM engineers can identify new opportunities for compiler optimisation

Which of my transient type checks are not (almost) free?

One form of type checking used in gradually typed language is transient type checking: whenever an object ‘flows’ through code with a type annotation, the object is dynamically checked to ensure it has the methods required by the annotation. Just-in-time compilation and optimisation in virtual machines can eliminate much of the overhead of runtime transient type checks. Unfortunately this optimisation is not uniform: some type checks will significantly decrease, or even increase, a program’s performance. In this paper, we refine the so called “Takikawa” protocol, and use it to identify which type annotations have the greatest effects on performance. In particular, we show how graphing the performance of such benchmarks when varying which type annotations are present in the source code can be used to discern potential patterns in performance. We demonstrate our approach by testing the Moth virtual machine: for many of the benchmarks where Moth’s transient type checking impacts performance, we have been able to identify one or two specific type annotations that are the likely cause. Without these type annotations, the performance impact of transient type checking becomes negligible. Using our technique programmers can optimise programs by removing expensive type checks, and VM engineers can identify new opportunities for compiler optimisation

Naïve Transient Cast Insertion Isn’t (That) Bad

Transient gradual type systems often depend on type-based cast insertion to achieve good performance: casts are inserted whenever the static checker detects that a dynamically-typed value may flow into a statically-typed context. Transient gradually typed programs are then often executed using just-in-time compilation, and contemporary just-in-time compilers are very good at removing redundant computations. In this paper we present work-in-progress to measure the ability of just-in-time compilers to remove redundant type checks. We investigate worst-case performance and so take a na'ive approach, annotating every subexpression to insert every plausible dynamic cast. Our results indicate that the Moth VM still manages to eliminate much of the overhead, by relying on the state-of-the-art SOMns substrate and Graal just-in-time compiler. We hope these results will help language implementers evaluate the tradeoffs between dynamic optimisations (which can improve the performance of both statically and dynamically typed programs) and static optimisations (which improve only statically typed code)