An ad hoc Approach to the Implementation of Polymorphism

Morrison, Ronald; Dearle, Alan; Connor, Richard; Brown, Fred

doi:10.1007/978-3-642-59623-0_39

Cited by 6 publications

(9 citation statements)

References 5 publications

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“…Many languages use monomorphization, which produces specialized copies of code: Ada generics [17], C ++ template [24], D template [1], Pizza's heterogeneous translation [18], MLton [4], [30], and Rust [3]. Monomorphization is common for system programming languages.…”

Section: Monomorphizationmentioning

confidence: 99%

Safe Low-level Code Generation in Coq Using Monomorphization and Monadification

Tanaka

Affeldt

Garrigue

2018

Journal of Information Processing

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Our goal is the production of formally-verified pieces of low-level code. Low-level code is typically written in C, so as to enable efficient manipulation of data at the bit-level and easy access to built-in features of CPUs. Proof-assistants arguably provide the most rigorous approach to formal verification of computer programs. Unfortunately, they only allow for extraction of runnable code in high-level languages such as ML. Of course it is possible to embed C snippets into ML programs, but this results in a complicated extraction process and the performance of the output program becomes difficult to anticipate. In this paper, we propose a new code generation scheme for the Coq proof-assistant that directly generates provably-safe C code. It is implemented in the form of plugins. The generation of C source code is done by a plugin performing beforehand monomorphization of Coq programs. The correctness of monomorphization can be proved within Coq. Code generation allows for user-guided changes of data structures. It is therefore possible to do formal verification using proof-friendly data structures, while enjoying optimized C representations in the output code. In order to ensure the safety of this transformation, we propose a new customizable monadification algorithm in the form of another plugin. Using monadification, one can ensure by the insertion of the right monads the preservation of critical invariants, such as the absence of overflows or complexity properties. We provide several examples to illustrate our approach, including a realistic use-case: the rank algorithm from succinct data structures.

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Section: Monomorphizationmentioning

confidence: 99%

Safe Low-level Code Generation in Coq Using Monomorphization and Monadification

Tanaka

Affeldt

Garrigue

2018

Journal of Information Processing

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“…Heterogeneous translations include Scala specialization [21], compile-time C++ template expansion [66] and load-time template instantiation [35] as done by the .NET CLR [9]. Homogeneous translations, on the other hand, require a uniform data representation, which may be either boxed values [15,42], fixnums [77] or tagged unions [46].…”

Section: Related Workmentioning

confidence: 99%

“…Language and compiler designers are well aware of the intricacies of erased generics [15,21,30,32,35,42,46,75], one of which is requiring object-based representations for primitive types. To illustrate this, let us analyze the identity method, parameterized on the argument type, T: The low-level compiled code for identity needs to handle incoming arguments of different sizes and semantics: booleans, bytes, characters, integers, floating point numbers and references to heap-allocated objects.…”

Section: Introductionmentioning

confidence: 99%

Late data layout

Ureche

Burmako

Odersky

2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

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“…A unique design feature of the implementation of the typed objects is that their storage format may be non-uniform [12].…”

Section: Data Modellingmentioning

confidence: 99%

“…The system was also intended as, or turned out to be, a testbed for experiments in: type systems for data modelling [2][3][4][5][6][7], bulk data [8,9] and protection [10,11]; programming language implementation [12,13]; binding mechanisms [14][15][16][17]; programming environments [17][18][19][20]; system evolution [21][22][23]; concurrency control and transactions [24][25][26][27]; object stores [26,[28][29][30][31][32][33][34][35] and software engineering tools [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

The Napier88 Persistent Programming Language and Environment

Morrison

Connor

Kirby

et al. 2000

Fully Integrated Data Environments

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Abstract. Persistent programming systems are designed as an implementation technology for long lived, concurrently accessed and potentially large bodies of data and programs, known here as persistent application systems (PASs). Within a PAS the persistence concept is used to abstract over the physical properties of data such as where it is kept, how long it is kept and in what form it is kept. As such it is seen as having a number of benefits in simplifying the task of PAS programming. Here, we describe the integrated design of the Napier88 persistent programming system and how Napier88 may be used to develop PASs.

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An ad hoc Approach to the Implementation of Polymorphism

Cited by 6 publications

References 5 publications

Safe Low-level Code Generation in Coq Using Monomorphization and Monadification

Safe Low-level Code Generation in Coq Using Monomorphization and Monadification

Late data layout

The Napier88 Persistent Programming Language and Environment

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