An official website of the United States government
Gradually typed programming languages permit the incremental addition of static types to untyped programs. To remain sound, languages insert run-time checks at the boundaries between typed and untyped code. Unfortunately, performance studies have shown that the overhead of these checks can be disastrously high, calling into question the viability of sound gradual typing. In this paper, we show that by building on existing work on soft contract verification, we can reduce or eliminate this overhead. Our key insight is that while untyped code cannot be trusted by a gradual type system, there is no need to consider only the worst case when optimizing a gradually typed program. Instead, we statically analyze the untyped portions of a gradually typed program to prove that almost all of the dynamic checks implied by gradual type boundaries cannot fail, and can be eliminated at compile time. Our analysis is modular, and can be applied to any portion of a program. We evaluate this approach on a dozen existing gradually typed programs previously shown to have prohibitive performance overhead—with a median overhead of 2.5× and up to 80.6× in the worst case—and eliminate all overhead in most cases, suffering only 1.5× overhead in the worst case.
more »
« less
This content will become publicly available on July 14, 2025
Gradual Typing Performance, Micro Configurations and Macro Perspectives
Static typing and dynamic typing have respective strengths and weaknesses, and a language often commits to one typing discipline and inherits the qualities, good or bad. Gradual typing has been developed to reconcile these typing disciplines, allowing a single program to mix both static and dynamic typing. It protects soundness of typed regions with runtime checks when values flown into them do not have required static types. One issue with gradual typing is that such checks can incur significant performance overhead. Previous work on performance has focused on coarse-grained gradual typing where each module (file) has to be fully typed or untyped. In contrast, the performance of fine-grained gradual typing where each single parameter can be partially-typed (such as specifying the parameter as a list without giving element type) has not been investigated. Motivated by this situation, this paper systematically investigates performance of fine-grained gradual typing by studying the performance of more than 1 million programs. These programs are drawn from seven commonly-used benchmarks with different types for parameters: some parameters are untyped, some are statically typed, and others are partially statically typed. The paper observes many interesting phenomena that were previously unknown to the research community. They provide insights into future research directions of understanding, predicting, and optimizing gradual typing performance as well as migrating gradual programs towards more static
more »
« less
- Award ID(s):
- 1750886
- PAR ID:
- 10569815
- Publisher / Repository:
- Springer, Cham
- Date Published:
- Volume:
- 14777
- ISBN:
- 978-3-031-64626-3
- Subject(s) / Keyword(s):
- Gradual typing performance fine-grained type annotations type migration
- Format(s):
- Medium: X Other: pdf
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Gradual typing has emerged as a popular design point in programming languages, attracting significant interests from both academia and industry. Programmers in gradually typed languages are free to utilize static and dynamic typing as needed. To make such languages sound, runtime checks mediate the boundary of typed and untyped code. Unfortunately, such checks can incur significant runtime overhead on programs that heavily mix static and dynamic typing. To combat this overhead without necessitating changes to the underlying implementations of languages, we present discriminative typing. Discriminative typing works by optimistically inferring types for functions and implementing an optimized version of the function based on this type. To preserve safety it also implements an un-optimized version of the function based purely on the provided annotations. With two versions of each function in hand, discriminative typing translates programs so that the optimized functions are called as frequently as possible while also preserving program behaviors. We have implemented discriminative typing in Reticulated Python and have evaluated its performance compared to guarded Reticulated Python. Our results show that discriminative typing improves the performance across 95% of tested programs, when compared to Reticulated, and achieves more than 4× speedup in more than 56% of these programs. We also compare its performance against a previous optimization approach and find that discriminative typing improved performance across 93% of tested programs, with 30% of these programs receiving speedups between 4 to 25 times. Finally, our evaluation shows that discriminative typing remarkably reduces the overhead of gradual typing on many mixed type configurations of programs. In addition, we have implemented discriminative typing in Grift and evaluated its performance. Our evaluation demonstrations that DT significantly improves performance of Griftmore » « less
-
Abstract Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically typed to more statically typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared. In this paper, we address this problem by developing migrational typing , which efficiently types all possible ways of replacing dynamic types with fully static types for a gradually typed program. The typing result supports automatically migrating a program to be as static as possible or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2–4 times longer than plain gradual typing.more » « less
-
Morales, J.F.; Orchard, D. (Ed.)In this paper, we describe our experience incorporating gradual types in a statically typed functional language with Hindley-Milner style type inference. Where most gradually typed systems aim to improve static checking in a dynamically typed language, we approach it from the opposite perspective and promote dynamic checking in a statically typed language. Our approach provides a glimpse into how languages like SML and OCaml might handle gradual typing. We discuss our implementation and challenges faced—specifically how gradual typing rules apply to our representation of composite and recursive types. We review the various implementations that add dynamic typing to a statically typed language in order to highlight the different ways of mixing static and dynamic typing and examine possible inspirations while maintaining the gradual nature of our type system. This paper also discusses our motivation for adding gradual types to our language, and the practical benefits of doing so in our industrial setting.more » « less
-
Aldrich, Jonathan; Salvaneschi, Guido (Ed.)Gradual typing has emerged as a promising typing discipline for reconciling static and dynamic typing, which have respective strengths and shortcomings. Thanks to its promises, gradual typing has gained tremendous momentum in both industry and academia. A main challenge in gradual typing is that, however, the performance of its programs can often be unpredictable, and adding or removing the type of a a single parameter may lead to wild performance swings. Many approaches have been proposed to optimize gradual typing performance, but little work has been done to aid the understanding of the performance landscape of gradual typing and navigating the migration process (which adds type annotations to make programs more static) to avert performance slowdowns. Motivated by this situation, this work develops a machine-learning-based approach to predict the performance of each possible way of adding type annotations to a program. On top of that, many supports for program migrations could be developed, such as finding the most performant neighbor of any given configuration. Our approach gauges runtime overheads of dynamic type checks inserted by gradual typing and uses that information to train a machine learning model, which is used to predict the running time of gradual programs. We have evaluated our approach on 12 Python benchmarks for both guarded and transient semantics. For guarded semantics, our evaluation results indicate that with only 40 training instances generated from each benchmark, the predicted times for all other instances differ on average by 4% from the measured times. For transient semantics, the time difference ratio is higher but the time difference is often within 0.1 seconds.more » « less
