KafKa: Gradual Typing for Objects

A wide range of gradual type systems have been proposed, providing many languages with the ability to mix typed and untyped code. However, hiding under language details, these gradual type systems embody fundamentally different ideas of what it means to be well-typed. In this paper, we show that four of the most common gradual type systems provide distinct guarantees, and we give a formal framework for comparing gradual type systems for object-oriented languages. First, we show that the different gradual type systems are practically distinguishable via a three-part litmus test. We present a formal framework for defining and comparing gradual type systems. Within this framework, different gradual type systems become translations between a common source and target language, allowing for direct comparison of semantics and guarantees

Transient Typechecks are (Almost) Free

Transient gradual typing imposes run-time type tests that typically cause a linear slowdown in programs’ performance. This performance impact discourages the use of type annotations because adding types to a program makes the program slower. A virtual machine can employ standard justin-time optimizations to reduce the overhead of transient checks to near zero. These optimizations can give gradually-typed languages performance comparable to state-of-the-art dynamic languages, so programmers can add types to their code without affecting their programs’ performance

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)

Theory and Practice of Action Semantics

Action Semantics is a framework for the formal descriptionof programming languages. Its main advantage over other frameworksis pragmatic: action-semantic descriptions (ASDs) scale up smoothly torealistic programming languages. This is due to the inherent extensibilityand modifiability of ASDs, ensuring that extensions and changes tothe described language require only proportionate changes in its description.(In denotational or operational semantics, adding an unforeseenconstruct to a language may require a reformulation of the entire description.)After sketching the background for the development of action semantics,we summarize the main ideas of the framework, and provide a simpleillustrative example of an ASD. We identify which features of ASDsare crucial for good pragmatics. Then we explain the foundations ofaction semantics, and survey recent advances in its theory and practicalapplications. Finally, we assess the prospects for further developmentand use of action semantics.The action semantics framework was initially developed at the Universityof Aarhus by the present author, in collaboration with David Watt(University of Glasgow). Groups and individuals scattered around fivecontinents have since contributed to its theory and practice

A Type System for Julia

The Julia programming language was designed to fill the needs of scientific computing by combining the benefits of productivity and performance languages. Julia allows users to write untyped scripts easily without needing to worry about many implementation details, as do other productivity languages. If one just wants to get the work done-regardless of how efficient or general the program might be, such a paradigm is ideal. Simultaneously, Julia also allows library developers to write efficient generic code that can run as fast as implementations in performance languages such as C or Fortran. This combination of user-facing ease and library developer-facing performance has proven quite attractive, and the language has increasing adoption. With adoption comes combinatorial challenges to correctness. Multiple dispatch -- Julia's key mechanism for abstraction -- allows many libraries to compose "out of the box." However, it creates bugs where one library's requirements do not match what another provides. Typing could address this at the cost of Julia's flexibility for scripting. I developed a "best of both worlds" solution: gradual typing for Julia. My system forms the core of a gradual type system for Julia, laying the foundation for improving the correctness of Julia programs while not getting in the way of script writers. My framework allows methods to be individually typed or untyped, allowing users to write untyped code that interacts with typed library code and vice versa. Typed methods then get a soundness guarantee that is robust in the presence of both dynamically typed code and dynamically generated definitions. I additionally describe protocols, a mechanism for typing abstraction over concrete implementation that accommodates one common pattern in Julia libraries, and describe its implementation into my typed Julia framework.Comment: PhD thesi

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

TOWARDS EFFICIENT GRADUAL TYPING VIA MONOTONIC REFERENCES AND COERCIONS

Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Integrating static and dynamic typing into a single programming language enables programmers to choose which discipline to use in each code region. Different approaches for this integration have been studied and put into use at large scale, e.g. TypeScript for JavaScript and adding the dynamic type to C#. Gradual typing is one approach to this integration that preserves type soundness by performing type-checking at run-time using casts. For higher order values such as functions and mutable references, a cast typically wraps the value in a proxy that performs type-checking when the value is used. This approach suffers from two problems: (1) chains of proxies can grow and consume unbounded space, and (2) statically typed code regions need to check whether values are proxied. Monotonic references solve both problems for mutable references by directly casting the heap cell instead of wrapping the reference in a proxy. In this dissertation, an integration is proposed of monotonic references with the coercion-based solution to the problem of chains of proxies for other values such as functions. Furthermore, the prior semantics for monotonic references involved storing and evaluating cast expressions (not yet values) in the heap and it is not obvious how to implement this behavior efficiently in a compiler and run-time system. This dissertation proposes novel dynamic semantics where only values are written to the heap, making the semantics straightforward to implement. The approach is implemented in Grift, a compiler for a gradually typed programming language, and a few key optimizations are proposed. Finally, the proposed performance evaluation methodology shows that the proposed approach eliminates all overheads associated with gradually typed references in statically typed code regions without introducing significant average-case overhead