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A Calculational Fusion System HYLO
, 1997
"... Fusion, one of the most useful transformation tactics for deriving efficient programs, is the process whereby separate pieces of programs are fused into a single one, leading to an efficient program with no intermediate data structures produced. In this paper, we report our ongoing investigation on ..."
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Cited by 33 (10 self)
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Fusion, one of the most useful transformation tactics for deriving efficient programs, is the process whereby separate pieces of programs are fused into a single one, leading to an efficient program with no intermediate data structures produced. In this paper, we report our ongoing investigation on the design and implementation of an automatic transformation system HYLO which performs fusion transformation in a more systematic and more general way than any other systems. The distinguished point of our system is its calculational feature based on simple application of transformation laws rather than traditional searchbased transformation.
Generic Program Transformation
 Proc. 3rd International Summer School on Advanced Functional Programming, LNCS 1608
, 1998
"... ion versus efficiency For concreteness, let us first examine a number of examples of the type of optimisation that we wish to capture, and the kind of programs on which they operate. This will give us a specific aim when developing the machinery for automating the process, and a yardstick for evalu ..."
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Cited by 30 (5 self)
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ion versus efficiency For concreteness, let us first examine a number of examples of the type of optimisation that we wish to capture, and the kind of programs on which they operate. This will give us a specific aim when developing the machinery for automating the process, and a yardstick for evaluating our results. 2.1 Minimum depth of a tree Consider the data type of leaf labelled binary trees: dataBtreea = Leaf a j Bin (Btree a)(Btree a) The minimum depth of such a tree is returned by the function mindepth :: Btree a ! Int : mindepth (Leaf a) = 0 mindepth (Bin s t) = min (mindepth s)(mindepth t) + 1 This program is clear, but rather inefficient. It traverses the whole tree, regardless of leaves that may occur at a small depth. A better program would keep track of the `minimum depth so far', and never explore subtrees beyond that current best solution. One possible implementation of that idea is mindepth t = md t 01 md (Leaf a)d m = mindm md (Bin s t)d m = if d 0 m then m...
An Accumulative Parallel Skeleton for All
, 2001
"... Parallel skeletons intend to encourage programmers to build... ..."
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Cited by 14 (11 self)
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Parallel skeletons intend to encourage programmers to build...
Towards Merging Recursion and Comonads
, 2000
"... Comonads are mathematical structures that account naturally for effects that derive from the context in which a program is executed. This paper reports ongoing work on the interaction between recursion and comonads. Two applications are shown that naturally lead to versions of a comonadic fold op ..."
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Cited by 9 (2 self)
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Comonads are mathematical structures that account naturally for effects that derive from the context in which a program is executed. This paper reports ongoing work on the interaction between recursion and comonads. Two applications are shown that naturally lead to versions of a comonadic fold operator on the product comonad. Both versions capture functions that require extra arguments for their computation and are related with the notion of strong datatype. 1 Introduction One of the main features of recursive operators derivable from datatype definitions is that they impose a structure upon programs which can be exploited for program transformation. Recursive operators structure functional programs according to the data structures they traverse or generate and come equipped with a battery of algebraic laws, also derivable from type definitions, which are used in program calculations [24, 11, 5, 15]. Some of these laws, the socalled fusion laws, are particularly interesting in p...
Calculating a New Data Mining Algorithm for Market Basket Analysis
, 2000
"... The general goal of data mining is to extract interesting correlated information from large ..."
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Cited by 5 (1 self)
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The general goal of data mining is to extract interesting correlated information from large
On Deforesting Parameters of Accumulating Maps
 In Logic Based Program Synthesis and Transformation, 11th International Workshop, LOPSTR 2001, volume 2372 of LNCS
, 2002
"... Deforestation is a wellknown program transformation technique which eliminates intermediate data structures that are passed between functions. One of its weaknesses is the inability to deforest programs using accumulating parameters. ..."
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Cited by 5 (0 self)
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Deforestation is a wellknown program transformation technique which eliminates intermediate data structures that are passed between functions. One of its weaknesses is the inability to deforest programs using accumulating parameters.
Towards a Modular Program Derivation via Fusion and Tupling
 The First ACM SIGPLAN Conference on Generators and Components, Lecture
, 2002
"... We show how programming pearls can be systematically derived via fusion, followed by tupling transformations. By focusing on the elimination of intermediate data structures (fusion) followed by the elimination of redundant calls (tupling), we systematically realise both space and time efficient algo ..."
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Cited by 2 (0 self)
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We show how programming pearls can be systematically derived via fusion, followed by tupling transformations. By focusing on the elimination of intermediate data structures (fusion) followed by the elimination of redundant calls (tupling), we systematically realise both space and time efficient algorithms from naive specifications. We illustrate our approach using a wellknown maximum segment sum (MSS) problem, and a lessknown maximum segment product (MSP) problem. While the two problems share similar specifications, their optimised codes are significantly different. This divergence in the transformed codes do not pose any difficulty. By relying on modular techniques, we are able to systematically reuse both code and transformation in our derivation.
B[count++] = A[i];
"... / * copy all bigger elements from A[1..n] into B[] */ count = 0; for (i=0; i<n; i++) { sumAfter = 0; for (j=i+1; j<n; j++) { sumAfter + = A[j]; if (A[i]> sumAfter) ..."
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/ * copy all bigger elements from A[1..n] into B[] */ count = 0; for (i=0; i<n; i++) { sumAfter = 0; for (j=i+1; j<n; j++) { sumAfter + = A[j]; if (A[i]> sumAfter)