Sharing of Computations

Amtoft, Torben

doi:10.7146/dpb.v22i453.6771

Cited by 18 publications

(26 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have also identified that the sharing of dynamic computations as achieved with the traditional déjà-vu list [28] is not enough; static computations must also be shared. The concepts involved-staging, i.e., binding-time separation, and sharing of computations as in dynamic programming [6]-have long been recognized as key ones in partial evaluation [4,33]. They are, however, not sufficient to obtain linear-time string matchers in linear time.…”

Section: Discussionmentioning

confidence: 99%

“…Examples of the methods used are Dijkstra's invariants [17], Bird's recursion introduction and tabulation [7], Takeichi and Akama's equational reasoning [41], Colussi's Hoare logic [14], and Hernández and Rosenblueth's logicprogram derivation [25]. Variants of the Boyer-Moore have also been reconstructed: both by partial evaluation [4,18], and by logic-program derivation [24].…”

Section: Related Workmentioning

confidence: 99%

“…Given a quadratic string matcher that searches for the first occurrence of a pattern in a text, a partial evaluator specializes this string matcher with respect to a pattern and yields a residual program that traverses the text in linear time. The problem was first stated by Yoshihiko Futamura in 1987 [20] and since then, it has served as a catalyst for the development of partial evaluators, giving rise to a variety of solutions [3,4,15,18,19,20,22,26,32,35,38,40,41].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Partial Evaluation of Pattern Matching in Strings

Ager¹,

Danvy²,

Rohde³

2004

BRICS

View full text Add to dashboard Cite

We show how to obtain all of Knuth, Morris, and Pratt's linear-time string matcher by specializing a quadratic-time string matcher with respect to a pattern string. Although it has been known for 15 years how to obtain this linear matcher by partial evaluation of a quadratic one, how to obtain it in linear time has remained an open problem.Obtaining a linear matcher by partial evaluation of a quadratic one is achieved by performing its backtracking at specialization time and memoizing its results. We show (1) how to rewrite the source matcher such that its static intermediate computations can be shared at specialization time and (2) how to extend the memoization capabilities of a partial evaluator to static functions. Such an extended partial evaluator, if its memoization is implemented efficiently, specializes the rewritten source matcher in linear time.Finally, we show that the method also applies to a variant of Boyer and Moore's string matcher.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Partial Evaluation of Pattern Matching in Strings

Ager¹,

Danvy²,

Rohde³

2004

BRICS

View full text Add to dashboard Cite

show abstract

“…Previous works [1,2,13,20,26] have noted that the unfold/fold transformation methodology is incomplete; some programs cannot be synthesized from each other. This is partly due to the linear relationship that must exist between the original and transformed programs.…”

Section: Conclusion and Related Workmentioning

confidence: 99%

Distillation with labelled transition systems

Hamilton

Jones

2012

Proceedings of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation

View full text Add to dashboard Cite

In this paper, we provide an improved basis for the "distillation" program transformation. It is known that superlinear speedups can be obtained using distillation, but cannot be obtained by other earlier automatic program transformation techniques such as deforestation, positive supercompilation and partial evaluation. We give distillation an improved semantic basis, and explain how superlinear speedups can occur.

show abstract

“…: string->int (txt) = match (''abaa",0,4) (txt,0,length(txt)) match (''abaa",0,4) : string*int*int->int (txt,k,lt) = if k=lt then -1 else compare (''abaa",0,4) (txt,k,lt) compare (''abaa",0,4) : string*int*int->int (txt,k,lt) = if 'a'=ref(txt,k) then match (''abaa",1,4) (txt,k+1,lt) else match (''abaa",0,4) (txt,k+1,lt) match (''abaa", 1,4) : string*int*int->int (txt,k,lt) = if k=lt then -1 else compare (''abaa",1,4) (txt,k,lt) compare (''abaa",1,4) : string*int*int->int (txt,k,lt) = if 'b'=ref(txt,k) then match (''abaa",2,4) (txt,k+1,lt) else compare (''abaa",0,4) (txt,k,lt) match (''abaa",2,4) : string*int*int->int (txt,k,lt) = if k=lt then -1 else compare (''abaa",2,4) (txt,k,lt) compare (''abaa",2,4) : string*int*int->int (txt,k,lt) = if 'a'=ref(txt,k) then match (''abaa",3,4) (txt,k+1,lt) else match (''abaa",0,4) (txt,k+1,lt) match (''abaa", 3,4) : string*int*int->int (txt,k,lt) = if k=lt then -1 else compare (''abaa",3,4) (txt,k,lt) compare (''abaa", 3,4) : string*int*int->int (txt,k,lt) = if 'a'=ref(txt,k) then match (''abaa",4,4) (txt,k+1,lt) else compare (''abaa",1,4) (txt,k,lt) match (''abaa",4,4) : string*int*int->int (txt,k,lt) = k-4 in main (''abaa") We will now show that for all patterns, our polyvariant specializer transforms the inefficient string matcher, STAGED, into a program with the above structure. The proof consists of two parts: termination of the specializer and properties of the residual programs.…”

Section: Application: the Knuth-morris-pratt Algorithmmentioning

confidence: 99%