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Lively Linear Lisp  'Look Ma, No Garbage!'
 ACM Sigplan Notices
, 1992
"... Linear logic has been proposed as one solution to the problem of garbage collection and providing efficient "updatein place" capabilities within a more functional language. Linear logic conserves accessibility, and hence provides a mechanical metaphor which is more appropriate for a distributedme ..."
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Cited by 92 (6 self)
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Linear logic has been proposed as one solution to the problem of garbage collection and providing efficient "updatein place" capabilities within a more functional language. Linear logic conserves accessibility, and hence provides a mechanical metaphor which is more appropriate for a distributedmemory parallel processor in which copying is explicit. However, linear logic's lack of sharing may introduce significant inefficiencies of its own. We show an efficient implementation of linear logic called Linear Lisp that runs within a constant factor of nonlinear logic. This Linear Lisp allows RPLACX operations, and manages storage as safely as a nonlinear Lisp, but does not need a garbage collector. Since it offers assignments but no sharing, it occupies a twilight zone between functional languages and imperative languages. Our Linear Lisp Machine offers many of the same capabilities as combinator/graph reduction machines, but without their copying and garbage collection problems. Intr...
Single Assignment C  efficient support for highlevel array operations in a functional setting
, 2003
"... ..."
Delimited Dynamic Binding
, 2006
"... Dynamic binding and delimited control are useful together in many settings, including Web applications, database cursors, and mobile code. We examine this pair of language features to show that the semantics of their interaction is illdefined yet not expressive enough for these uses. We solve this ..."
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Cited by 31 (11 self)
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Dynamic binding and delimited control are useful together in many settings, including Web applications, database cursors, and mobile code. We examine this pair of language features to show that the semantics of their interaction is illdefined yet not expressive enough for these uses. We solve this open and subtle problem. We formalise a typed language DB+DC that combines a calculus DB of dynamic binding and a calculus DC of delimited control. We argue from theoretical and practical points of view that its semantics should be based on delimited dynamic binding: capturing a delimited continuation closes over part of the dynamic environment, rather than all or none of it; reinstating the captured continuation supplements the dynamic environment, rather than replacing or inheriting it. We introduce a type and reductionpreserving translation from DB + DC to DC, which proves that delimited control macroexpresses dynamic binding. We use this translation to implement DB + DC in Scheme, OCaml, and Haskell. We extend DB + DC with mutable dynamic variables and a facility to obtain not only the latest binding of a dynamic variable but also older bindings. This facility provides for stack inspection and (more generally) folding over the execution context as an inductive data structure.
Equal Rights for Functional Objects or, The More Things Change, The More They Are the Same
, 1993
"... DATA TYPES A. Comparing Type Objects There has been as much confusion over type identity as there has been over object identity, although the type identity problem is usually referred to as the type equivalence problem [Aho86,s.6.3] [Wegbreit74] [Welsh77]. The type identity problem is to determine ..."
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Cited by 22 (7 self)
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DATA TYPES A. Comparing Type Objects There has been as much confusion over type identity as there has been over object identity, although the type identity problem is usually referred to as the type equivalence problem [Aho86,s.6.3] [Wegbreit74] [Welsh77]. The type identity problem is to determine when two types are equal, so that type checking can be done in a programming language. 22 Algol68 takes the point of view of "structural" equivalence, in which nonrecursive types that are built up from primitive types using the same type constructors in the same order should compare equal, while Ada takes the point of view of "name" equivalence, in which types are equivalent if and only if they have the same name. We will ignore the software engineering issues of which kind of type equivalence makes for betterengineered programs, and focus on the basic issue of type equivalence itself. We note that if a type system offers the type TYPEi.e., it offers firstclass representations of typ...
Purely Functional RandomAccess Lists
 In Functional Programming Languages and Computer Architecture
, 1995
"... We present a new data structure, called a randomaccess list, that supports array lookup and update operations in O(log n) time, while simultaneously providing O(1) time list operations (cons, head, tail). A closer analysis of the array operations improves the bound to O(minfi; log ng) in the wor ..."
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Cited by 17 (2 self)
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We present a new data structure, called a randomaccess list, that supports array lookup and update operations in O(log n) time, while simultaneously providing O(1) time list operations (cons, head, tail). A closer analysis of the array operations improves the bound to O(minfi; log ng) in the worst case and O(log i) in the expected case, where i is the index of the desired element. Empirical evidence suggests that this data structure should be quite efficient in practice. 1 Introduction Lists are the primary data structure in every functional programmer 's toolbox. They are simple, convenient, and usually quite efficient. The main drawback of lists is that accessing the ith element requires O(i) time. In such situations, functional programmers often find themselves longing for the efficient random access of arrays. Unfortunately, arrays can be quite awkward to implement in a functional setting, where previous versions of the array must be available even after an update. Since arra...
A Probabilistic Approach to the Problem of Automatic Selection of Data Representations
 In Proceedings of the 1996 ACM SIGPLAN International Conference on Functional Programming
, 1996
"... The design and implementation of efficient aggregate data structures has been an important issue in functional programming. It is not clear how to select a good representation for an aggregate when access patterns to the aggregate are highly variant, or even unpredictable. Previous approaches rely o ..."
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Cited by 6 (3 self)
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The design and implementation of efficient aggregate data structures has been an important issue in functional programming. It is not clear how to select a good representation for an aggregate when access patterns to the aggregate are highly variant, or even unpredictable. Previous approaches rely on compiletime analyses or programmer annotations. These methods can be unreliable because they try to predict program behaviors before they are executed. We propose a probabilistic approach, which is based on Markov processes, for automatic selection of data representations. The selection is modeled as a random process moving in a graph with weighted edges. The proposed approach employs coin tossing at runtime to aid choosing suitable data representations. The transition probability function used by the coin tossing is constructed in a simple and common way from a measured cost function. We show that, under this setting, random selection of data representations can be quite effective. Th...
Fully Persistent Arrays for Efficient Incremental Updates and Voluminous Reads
 4th European Symposium on Programming
, 1992
"... The array update problem in a purely functional language is the following: once an array is updated, both the original array and the newly updated one must be preserved to maintain referential transparency. We devise a very simple, fully persistent data structure to tackle this problem such that ..."
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Cited by 5 (2 self)
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The array update problem in a purely functional language is the following: once an array is updated, both the original array and the newly updated one must be preserved to maintain referential transparency. We devise a very simple, fully persistent data structure to tackle this problem such that ffl each incremental update costs O(1) worstcase time, ffl a voluminous sequence of r reads cost in total O(r) amortized time, and ffl the data structure use O(n + u) space, where n is the size of the array and u is the total number of updates. A sequence of r reads is voluminous if r is \Omega\Gamma n) and the sequence of arrays being read forms a path of length O(r) in the version tree. A voluminous sequence of reads may be mixed with updates without affecting either the performance of reads or updates. An immediate consequence of the above result is that if a functional program is singlethreaded, then the data structure provides a simple and efficient implementation of funct...
A Randomized Implementation of Multiple Functional Arrays
 In Proceedings of 1994 ACM Conference on Lisp and Functional Programming
, 1994
"... The array update problem in the implementation of a purely functional language is the following: once an array is updated, both the original array and the newly updated one must be preserved to maintain referential transparency. Previous approaches have mainly based on the detection or enforcement o ..."
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Cited by 5 (1 self)
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The array update problem in the implementation of a purely functional language is the following: once an array is updated, both the original array and the newly updated one must be preserved to maintain referential transparency. Previous approaches have mainly based on the detection or enforcement of singlethreaded accesses to an aggregate, by means of compilertime analyses or language restrictions. These approaches cannot deal with aggregates which are updated in a multithreaded manner. Baker's shallow binding scheme can be used to implement multithreaded functional arrays. His scheme, however, can be very expensive if there are repeated alternations between long binding paths. We design a scheme that fragments binding paths randomly. The randomization scheme is online, simple to implement, and its expected performance comparable to that of the optimal offline solutions. All this is achieved without using compilertime analyses, and without restricting the languages. The ...
SemiPersistent Data Structures
, 2007
"... A data structure is said to be persistent when any update operation returns a new structure without altering the old version. This paper introduces a new notion of persistence, called semipersistence, where only ancestors of the most recent version can be accessed or updated. Making a data structur ..."
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Cited by 4 (1 self)
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A data structure is said to be persistent when any update operation returns a new structure without altering the old version. This paper introduces a new notion of persistence, called semipersistence, where only ancestors of the most recent version can be accessed or updated. Making a data structure semipersistent may improve its time and space complexity. This is of particular interest in backtracking algorithms manipulating persistent data structures, where this property is usually satisfied. We propose a proof system to statically check the valid use of semipersistent data structures. It requires a few annotations from the user and then generates proof obligations that are automatically discharged by a dedicated decision procedure. Additionally, we give some examples of semipersistent data structures (arrays, lists and hash tables). 1
unknown title
"... destructive unifications of nongeneralized type variables must be undone when typing errors are encountered. • Decision procedures: Another example of a unionfind data structure used within a backtracking algorithm are decision procedures where the treatment of equality is usually based on such a ..."
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destructive unifications of nongeneralized type variables must be undone when typing errors are encountered. • Decision procedures: Another example of a unionfind data structure used within a backtracking algorithm are decision procedures where the treatment of equality is usually based on such a structure and where the boolean part of formulas is mostly processed using backtracking [13, 16]. A standard solution to the issue of backtracking in data structures is to turn to persistent data structures (as opposed to ephemeral data structures) [12]. In this case, theunion operation now returns a new partition and leaves the previous one unchanged. The signature of such a persistent data structure could be the following: module type PersistentUnionFind = sig type t val create: int → t