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Uselessvariable elimination
 In proceedings of Workshop on Static Analysis of Equational, Functional and Logic Programs, Université Bordeaux I, LaBRI
, 1990
"... happen. Most of the promised items have been covered in other papers (“The Semantics of Scheme ControlFlow Analysis ” and “DataFlow Analysis and Type Recovery in Scheme”). Uselessvariable elimination, however, is still described only in the T source code of my analysis system. I am writing this i ..."
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Cited by 4 (1 self)
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happen. Most of the promised items have been covered in other papers (“The Semantics of Scheme ControlFlow Analysis ” and “DataFlow Analysis and Type Recovery in Scheme”). Uselessvariable elimination, however, is still described only in the T source code of my analysis system. I am writing
Uselessvariable elimination
 In proceedings of Workshop on Static Analysis of Equational, Functional and Logic Programs, Université Bordeaux I, LaBRI
, 1990
"... sed in the loop. UVE is often useful to clean up after applying other code transformations, such as copy propagation or inductionvariable elimination. For example, when we introduce a new variable to track an induction function on some basic induction variable, the basic variable frequently become ..."
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sed in the loop. UVE is often useful to clean up after applying other code transformations, such as copy propagation or inductionvariable elimination. For example, when we introduce a new variable to track an induction function on some basic induction variable, the basic variable frequently
TypeBased UselessVariable Elimination
, 2000
"... Uselessvariable elimination is a transformation that eliminates variables whose values contribute nothing to the final outcome of a computation. We present a typebased method for uselessvariable elimination and prove its correctness. The algorithm is a surprisingly simple extension of the usual t ..."
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Cited by 3 (1 self)
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Uselessvariable elimination is a transformation that eliminates variables whose values contribute nothing to the final outcome of a computation. We present a typebased method for uselessvariable elimination and prove its correctness. The algorithm is a surprisingly simple extension of the usual
TypeBased Useless Variable Elimination
, 1999
"... We show a typebased method for useless variable elimination, i.e., transformation that eliminates variables whose values contribute nothing to the final outcome of a computation, and prove its correctness. The algorithm is a surprisingly simple extension of the usual type reconstruction algorithm. ..."
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Cited by 20 (4 self)
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We show a typebased method for useless variable elimination, i.e., transformation that eliminates variables whose values contribute nothing to the final outcome of a computation, and prove its correctness. The algorithm is a surprisingly simple extension of the usual type reconstruction algorithm
Constraint Systems for Useless Variable Elimination
, 1998
"... A useless variable is one whose value contributes nothing to the final outcome of a computation. Such variables are unlikely to occur in humanproduced code, but may be introduced by various program transformations. We would like to eliminate useless parameters from procedures and eliminate the corr ..."
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Cited by 23 (1 self)
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A useless variable is one whose value contributes nothing to the final outcome of a computation. Such variables are unlikely to occur in humanproduced code, but may be introduced by various program transformations. We would like to eliminate useless parameters from procedures and eliminate
Research Science Institute 1998, Intel STS 1999 Implementation of Constraint Systems for Useless Variable Elimination
"... Superfluous variables are often produced as the byproducts of program transformations, compilation, and poorly written code. These variables are irrelevant to the computational outcome of their programs. Eliminating them as a means of higherlevel optimization may increase program execution speed. Th ..."
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. This paper explores an implementation of Wand and Siveroni’s algorithm for useless variable elimination. The algorithm is shown to remove superfluous variables that are accessed, updated, and passed between functions, as well as collapselet expressions when all the let expression’s variables are superfluous
ControlFlow Analysis of HigherOrder Languages
, 1991
"... representing the official policies, either expressed or implied, of ONR or the U.S. Government. Keywords: dataflow analysis, Scheme, LISP, ML, CPS, type recovery, higherorder functions, functional programming, optimising compilers, denotational semantics, nonstandard Programs written in powerful, ..."
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Cited by 362 (10 self)
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at compile time. I give examples of how this information can be used to perform several dataflow analysis optimisations, including copy propagation, inductionvariable elimination, uselessvariable elimination, and type recovery. The analysis is defined in terms of a nonstandard semantic interpretation
An introduction to variable and feature selection
 Journal of Machine Learning Research
, 2003
"... Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. ..."
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Cited by 1283 (16 self)
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Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.
Efficiently computing static single assignment form and the control dependence graph
 ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS
, 1991
"... In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single ass ..."
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Cited by 997 (8 self)
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In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow propertiee of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimization. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use dominance frontiers, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.
Results 1  10
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