Results 1  10
of
39
Adaptive Functional Programming
 IN PROCEEDINGS OF THE 29TH ANNUAL ACM SYMPOSIUM ON PRINCIPLES OF PROGRAMMING LANGUAGES
, 2001
"... An adaptive computation maintains the relationship between its input and output as the input changes. Although various techniques for adaptive computing have been proposed, they remain limited in their scope of applicability. We propose a general mechanism for adaptive computing that enables one to ..."
Abstract

Cited by 64 (23 self)
 Add to MetaCart
An adaptive computation maintains the relationship between its input and output as the input changes. Although various techniques for adaptive computing have been proposed, they remain limited in their scope of applicability. We propose a general mechanism for adaptive computing that enables one to make any purelyfunctional program adaptive. We show
Selective Memoization
"... We present a framework for applying memoization selectively. The framework provides programmer control over equality, space usage, and identification of precise dependences so that memoization can be applied according to the needs of an application. Two key properties of the framework are that it ..."
Abstract

Cited by 44 (19 self)
 Add to MetaCart
We present a framework for applying memoization selectively. The framework provides programmer control over equality, space usage, and identification of precise dependences so that memoization can be applied according to the needs of an application. Two key properties of the framework are that it is efficient and yields programs whose performance can be analyzed using standard techniques. We describe the framework in the context of a functional language and an implementation as an SML library. The language is based on a modal type system and allows the programmer to express programs that reveal their true data dependences when executed. The SML implementation cannot support this modal type system statically, but instead employs runtime checks to ensure correct usage of primitives.
Automatic Accurate TimeBound Analysis for HighLevel Languages
 In Proceedings of the ACM SIGPLAN 1998 Workshop on Languages, Compilers, and Tools for Embedded Systems, volume 1474 of Lecture Notes in Computer Science
, 1998
"... This paper describes a general approach for automatic and accurate timebound analysis. The approach consists of transformations for building timebound functions in the presence of partially known input structures, symbolic evaluation of the timebound function based on input parameters, optimizati ..."
Abstract

Cited by 38 (9 self)
 Add to MetaCart
This paper describes a general approach for automatic and accurate timebound analysis. The approach consists of transformations for building timebound functions in the presence of partially known input structures, symbolic evaluation of the timebound function based on input parameters, optimizations to make the overall analysis efficient as well as accurate, and measurements of primitive parameters, all at the sourcelanguage level. We have implemented this approach and performed a number of experiments for analyzing Scheme programs. The measured worstcase times are closely bounded by the calculated bounds. 1 Introduction Analysis of program running time is important for realtime systems, interactive environments, compiler optimizations, performance evaluation, and many other computer applications. It has been extensively studied in many fields of computer science: algorithms [20, 12, 13, 41], programming languages [38, 21, 30, 33], and systems [35, 28, 32, 31]. It is particularl...
An experimental analysis of selfadjusting computation
 In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI
, 2006
"... Selfadjusting computation uses a combination of dynamic dependence graphs and memoization to efficiently update the output of a program as the input changes incrementally or dynamically over time. Related work showed various theoretical results, indicating that the approach can be effective for a r ..."
Abstract

Cited by 35 (19 self)
 Add to MetaCart
Selfadjusting computation uses a combination of dynamic dependence graphs and memoization to efficiently update the output of a program as the input changes incrementally or dynamically over time. Related work showed various theoretical results, indicating that the approach can be effective for a reasonably broad range of applications. In this article, we describe algorithms and implementation techniques to realize selfadjusting computation and present an experimental evaluation of the proposed approach on a variety of applications, ranging from simple list primitives to more sophisticated computational geometry algorithms. The results of the experiments show that the approach is effective in practice, often offering orders of magnitude speedup from recomputing the output from scratch. We believe this is the first experimental evidence that incremental computation of any type is effective in practice for a reasonably broad set of applications.
From Datalog rules to efficient programs with time and space guarantees
 In PPDP ’03: Proceedings of the 5th ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming
, 2003
"... This paper describes a method for transforming any given set of Datalog rules into an efficient specialized implementation with guaranteed worstcase time and space complexities, and for computing the complexities from the rules. The running time is optimal in the sense that only useful combinations ..."
Abstract

Cited by 30 (12 self)
 Add to MetaCart
This paper describes a method for transforming any given set of Datalog rules into an efficient specialized implementation with guaranteed worstcase time and space complexities, and for computing the complexities from the rules. The running time is optimal in the sense that only useful combinations of facts that lead to all hypotheses of a rule being simultaneously true are considered, and each such combination is considered exactly once. The associated space usage is optimal in that it is the minimum space needed for such consideration modulo scheduling optimizations that may eliminate some summands in the space usage formula. The transformation is based on a general method for algorithm design that exploits fixedpoint computation, incremental maintenance of invariants, and combinations of indexed and linked data structures. We apply the method to a number of analysis problems, some with improved algorithm complexities and all with greatly improved algorithm understanding and greatly simplified complexity analysis.
Imperative selfadjusting computation
 In POPL ’08: Proceedings of the 35th annual ACM SIGPLANSIGACT symposium on Principles of programming languages
, 2008
"... Recent work on selfadjusting computation showed how to systematically write programs that respond efficiently to incremental changes in their inputs. The idea is to represent changeable data using modifiable references, i.e., a special data structure that keeps track of dependencies between read an ..."
Abstract

Cited by 27 (16 self)
 Add to MetaCart
Recent work on selfadjusting computation showed how to systematically write programs that respond efficiently to incremental changes in their inputs. The idea is to represent changeable data using modifiable references, i.e., a special data structure that keeps track of dependencies between read and writeoperations, and to let computations construct traces that later, after changes have occurred, can drive a change propagation algorithm. The approach has been shown to be effective for a variety of algorithmic problems, including some for which adhoc solutions had previously remained elusive. All previous work on selfadjusting computation, however, relied on a purely functional programming model. In this paper, we show that it is possible to remove this limitation and support modifiable references that can be written multiple times. We formalize this using a language AIL for which we define evaluation and changepropagation semantics. AIL closely resembles a traditional higherorder imperative programming language. For AIL we state and prove consistency, i.e., the property that although the semantics is inherently nondeterministic, different evaluation paths will still give observationally equivalent results. In the imperative setting where pointer graphs in the store can form cycles, our previous proof techniques do not apply. Instead, we make use of a novel form of a stepindexed logical relation that handles modifiable references. We show that AIL can be realized efficiently by describing implementation strategies whose overhead is provably constanttime per primitive. When the number of reads and writes per modifiable is bounded by a constant, we can show that change propagation becomes as efficient as it was in the pure case. The general case incurs a slowdown that is logarithmic in the maximum number of such operations. We use DFS and related algorithms on graphs as our running examples and prove that they respond to insertions and deletions of edges efficiently. 1.
Dynamic programming via static incrementalization
 In Proceedings of the 8th European Symposium on Programming
, 1999
"... Dynamic programming is an important algorithm design technique. It is used for solving problems whose solutions involve recursively solving subproblems that share subsubproblems. While a straightforward recursive program solves common subsubproblems repeatedly and often takes exponential time, a dyn ..."
Abstract

Cited by 26 (12 self)
 Add to MetaCart
Dynamic programming is an important algorithm design technique. It is used for solving problems whose solutions involve recursively solving subproblems that share subsubproblems. While a straightforward recursive program solves common subsubproblems repeatedly and often takes exponential time, a dynamic programming algorithm solves every subsubproblem just once, saves the result, reuses it when the subsubproblem is encountered again, and takes polynomial time. This paper describes a systematic method for transforming programs written as straightforward recursions into programs that use dynamic programming. The method extends the original program to cache all possibly computed values, incrementalizes the extended program with respect to an input increment to use and maintain all cached results, prunes out cached results that are not used in the incremental computation, and uses the resulting incremental program to form an optimized new program. Incrementalization statically exploits semantics of both control structures and data structures and maintains as invariants equalities characterizing cached results. The principle underlying incrementalization is general for achieving drastic program speedups. Compared with previous methods that perform memoization or tabulation, the method based on incrementalization is more powerful and systematic. It has been implemented and applied to numerous problems and succeeded on all of them. 1
Automatic timebound analysis for a higherorder language
 In Proceedings of the ACM SIGPLAN 2002 Workshop on Partial Evaluation and SemanticsBased Program Manipulation
, 2002
"... Analysis of program running time is important for reactive systems, interactive environments, compiler optimizations, performance evaluation, and many other computer applications. It has been extensively studied in many elds of computer science: algorithms [21, 12, 13,40], programming languages [38, ..."
Abstract

Cited by 24 (5 self)
 Add to MetaCart
Analysis of program running time is important for reactive systems, interactive environments, compiler optimizations, performance evaluation, and many other computer applications. It has been extensively studied in many elds of computer science: algorithms [21, 12, 13,40], programming languages [38, 22,31, 35, 34], and systems [36, 29,33,32]. Being able to predict accurate time bounds automatically and e ciently
Incremental algorithms for interprocedural analysis of safety properties
, 2005
"... Automatonbased static program analysis has proved to be an effective tool for bug finding. Current tools generally reanalyze a program from scratch in response to a change in the code, which can result in much duplicated effort. We present an interprocedural algorithm that analyzes incrementally ..."
Abstract

Cited by 20 (0 self)
 Add to MetaCart
Automatonbased static program analysis has proved to be an effective tool for bug finding. Current tools generally reanalyze a program from scratch in response to a change in the code, which can result in much duplicated effort. We present an interprocedural algorithm that analyzes incrementally in response to program changes and present experiments for a nullpointer dereference analysis. It shows a substantial speedup over reanalysis from scratch, with a manageable amount of disk space used to store information between analysis runs.