scholarly journals Sharing analysis in the Pawns compiler

2015 ◽  
Author(s):  
Lee Naish
Keyword(s):  
Programming Language ◽  
Data Structures ◽  
Source Code ◽  
Higher Order ◽  
Declarative Programming ◽  
Data Types ◽  
Shared Data ◽  
Algebraic Data Types ◽  
Higher Order Functions

Pawns is a programming language under development that supports algebraic data types, polymorphism, higher order functions and "pure" declarative programming. It also supports impure imperative features including destructive update of shared data structures via pointers, allowing significantly increased efficiency for some operations. A novelty of Pawns is that all impure "effects" must be made obvious in the source code and they can be safely encapsulated in pure functions in a way that is checked by the compiler. Execution of a pure function can perform destructive updates on data structures that are local to or eventually returned from the function without risking modification of the data structures passed to the function. This paper describes the sharing analysis which allows impurity to be encapsulated. Aspects of the analysis are similar to other published work, but in addition it handles explicit pointers and destructive update, higher order functions including closures and pre- and post-conditions concerning sharing for functions.

scholarly journals Sharing analysis in the Pawns compiler

2015 ◽  
Author(s):  
Lee Naish
Keyword(s):  
Programming Language ◽  
Data Structures ◽  
Source Code ◽  
Higher Order ◽  
Declarative Programming ◽  
Data Types ◽  
Shared Data ◽  
Algebraic Data Types ◽  
Higher Order Functions

Pawns is a programming language under development which supports algebraic data types, polymorphism, higher order functions and "pure" declarative programming. It also supports impure imperative features including destructive update of shared data structures via pointers, allowing significantly increased efficiency for some operations. A novelty of Pawns is that all impure "effects" must be made obvious in the source code and they can be safely encapsulated in pure functions in a way that is checked by the compiler. Execution of a pure function can perform destructive updates on data structures which are local to or eventually returned from the function without risking modification of the data structures passed to the function. This paper describes the sharing analysis which allows impurity to be encapsulated. Aspects of the analysis are similar to other published work, but in addition it handles explicit pointers and destructive update, higher order functions including closures and pre- and postconditions concerning sharing for functions.

scholarly journals Sharing analysis in the Pawns compiler

2015 ◽  
Author(s):  
Lee Naish
Keyword(s):  
Programming Language ◽  
Data Structures ◽  
Source Code ◽  
Higher Order ◽  
Declarative Programming ◽  
Data Types ◽  
Shared Data ◽  
Algebraic Data Types ◽  
Higher Order Functions

Pawns is a programming language under development that supports algebraic data types, polymorphism, higher order functions and "pure" declarative programming. It also supports impure imperative features including destructive update of shared data structures via pointers, allowing significantly increased efficiency for some operations. A novelty of Pawns is that all impure "effects" must be made obvious in the source code and they can be safely encapsulated in pure functions in a way that is checked by the compiler. Execution of a pure function can perform destructive updates on data structures that are local to or eventually returned from the function without risking modification of the data structures passed to the function. This paper describes the sharing analysis which allows impurity to be encapsulated. Aspects of the analysis are similar to other published work, but in addition it handles explicit pointers and destructive update, higher order functions including closures and pre- and post-conditions concerning sharing for functions.

scholarly journals The ins and outs of Clean I/O

1995 ◽  
Vol 5 (1) ◽  
pp. 81-110 ◽  
Author(s):  
Peter Achten ◽  
Rinus Plasmeijer
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Functional Programming ◽  
Data Types ◽  
Spreadsheet Program ◽  
Functional Programming Languages ◽  
Hard Time ◽  
Functional Programming Language ◽  
Algebraic Data Types ◽  
High Level

AbstractFunctional programming languages have banned assignment because of its undesirable properties. The reward of this rigorous decision is that functional programming languages are side-effect free. There is another side to the coin: because assignment plays a crucial role in Input/Output (I/O), functional languages have a hard time dealing with I/O. Functional programming languages have therefore often been stigmatised as inferior to imperative programming languages because they cannot deal with I/O very well. In this paper, we show that I/O can be incorporated in a functional programming language without loss of any of the generally accepted advantages of functional programming languages. This discussion is supported by an extensive account of the I/O system offered by the lazy, purely functional programming language Clean. Two aspects that are paramount in its I/O system make the approach novel with respect to other approaches. These aspects are the technique of explicit multiple environment passing, and the Event I/O framework to program Graphical User I/O in a highly structured and high-level way. Clean file I/O is as powerful and flexible as it is in common imperative languages (one can read, write, and seek directly in a file). Clean Event I/O provides programmers with a high-level framework to specify complex Graphical User I/O. It has been used to write applications such as a window-based text editor, an object based drawing program, a relational database, and a spreadsheet program. These graphical interactive programs are completely machine independent, but still obey the look-and-feel of the concrete window environment being used. The specifications are completely functional and make extensive use of uniqueness typing, higher-order functions, and algebraic data types. Efficient implementations are present on the Macintosh, Sun (X Windows under Open Look) and PC (OS/2).

Generic top-down discrimination for sorting and partitioning in linear time

2012 ◽  
Vol 22 (3) ◽  
pp. 300-374 ◽  
Author(s):  
FRITZ HENGLEIN
Keyword(s):  
Linear Time ◽  
Source Code ◽  
Equivalence Relations ◽  
Asymptotic Performance ◽  
Data Types ◽  
Worst Case ◽  
First Order ◽  
Structural Recursion ◽  
Algebraic Data Types ◽  
Abstract Types

AbstractWe introduce the notion ofdiscriminationas a generalization of both sorting and partitioning, and show thatdiscriminators(discrimination functions) can be definedgenerically, by structural recursion on representations oforderingandequivalence relations. Discriminators improve the asymptotic performance of generic comparison-based sorting and partitioning, and can be implemented not to expose more information than the underlying ordering, respectively equivalence relation. For a large class of order and equivalence representations, including all standard orders for regular recursive first-order types, the discriminators execute in the worst-case linear time. The generic discriminators can be coded compactly using list comprehensions, with order and equivalence representations specified using Generalized Algebraic Data Types. We give some examples of the uses of discriminators, including the most-significant digit lexicographic sorting, type isomorphism with an associative-commutative operator, and database joins. Source code of discriminators and their applications in Haskell is included. We argue that built-in primitive types, notably pointers (references), should come with efficient discriminators, not just equality tests, since they facilitate the construction of discriminators for abstract types that are both highly efficient and representation-independent.

Functional programming with the FC++ library

2004 ◽  
Vol 14 (4) ◽  
pp. 429-472 ◽  
Author(s):  
BRIAN MCNAMARA ◽  
YANNIS SMARAGDAKIS
Keyword(s):  
Data Structures ◽  
Functional Programming ◽  
Higher Order ◽  
Performance Comparison ◽  
Type Inference ◽  
High Complexity ◽  
Time Performance ◽  
Order Of Magnitude ◽  
Supporting Functional ◽  
Higher Order Functions

We describe the FC++ library, a rich library supporting functional programming in C++. Prior approaches to encoding higher order functions in C++ have suffered with respect to polymorphic functions from either lack of expressiveness or high complexity. In contrast, FC++ offers full and concise support for higher-order polymorphic functions through a novel use of C++ type inference. The FC++ library has a number of useful features, including a generalized mechanism to implement currying in C++, a “lazy list” class which enables the creation of “infinite data structures”, a subtype polymorphism facility, and an extensive library of useful functions, including a large part of the Haskell Standard Prelude. The FC++ library has an efficient implementation. We show the results of a number of experiments which demonstrate the value of optimizations we have implemented. These optimizations have improved the run-time performance by about an order of magnitude for some benchmark programs that make heavy use of FC++ lazy lists. We also make an informal performance comparison with similar programs written in Haskell.

scholarly journals A logical analysis of aliasing in imperative higher-order functions

2007 ◽  
Vol 17 (4-5) ◽  
pp. 473-546 ◽  
Author(s):  
MARTIN BERGER ◽  
KOHEI HONDA ◽  
NOBUKO YOSHIDA
Keyword(s):  
Data Structures ◽  
Building Blocks ◽  
Higher Order ◽  
Logical Analysis ◽  
Logical Status ◽  
Open Issue ◽  
Higher Order Functions ◽  
Structural Reasoning

AbstractWe present a compositional programme logic for call-by-value imperative higher-order functions with general forms of aliasing, which can arise from the use of reference names as function parameters, return values, content of references and parts of data structures. The programme logic extends our earlier logic for alias-free imperative higher-order functions with new operators which serve as building blocks for clean structural reasoning about programms and data structures in the presence of aliasing. This has been an open issue since the pioneering work by Cartwright–Oppen and Morris twenty-five years ago. We illustrate usage of the logic for description and reasoning through concrete examples including a higher-order polymorphic Quicksort. The logical status of the new operators is clarified by translating them into (in)equalities of reference names.

scholarly journals Solving constrained Horn clauses modulo algebraic data types and recursive functions

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-29
Author(s):  
Hari Govind V K ◽  
Sharon Shoham ◽  
Arie Gurfinkel
Keyword(s):  
Data Structures ◽  
General Class ◽  
Data Types ◽  
Deductive Verification ◽  
Recursive Definition ◽  
Underlying Theory ◽  
Horn Clauses ◽  
Verification Conditions ◽  
Algebraic Data Types ◽  
Inductive Invariants

This work addresses the problem of verifying imperative programs that manipulate data structures, e.g., Rust programs. Data structures are usually modeled by Algebraic Data Types (ADTs) in verification conditions. Inductive invariants of such programs often require recursively defined functions (RDFs) to represent abstractions of data structures. From the logic perspective, this reduces to solving Constrained Horn Clauses (CHCs) modulo both ADT and RDF. The underlying logic with RDFs is undecidable. Thus, even verifying a candidate inductive invariant is undecidable. Similarly, IC3-based algorithms for solving CHCs lose their progress guarantee: they may not find counterexamples when the program is unsafe. We propose a novel IC3-inspired algorithm Racer for solving CHCs modulo ADT and RDF (i.e., automatically synthesizing inductive invariants, as opposed to only verifying them as is done in deductive verification). Racer ensures progress despite the undecidability of the underlying theory, and is guaranteed to terminate with a counterexample for unsafe programs. It works with a general class of RDFs over ADTs called catamorphisms. The key idea is to represent catamorphisms as both CHCs, via relationification , and RDFs, using novel abstractions . Encoding catamorphisms as CHCs allows learning inductive properties of catamorphisms, as well as preserving unsatisfiabilty of the original CHCs despite the use of RDF abstractions, whereas encoding catamorphisms as RDFs allows unfolding the recursive definition, and relying on it in solutions. Abstractions ensure that the underlying theory remains decidable. We implement our approach in Z3 and show that it works well in practice.

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