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A Data Structure for Manipulating Priority Queues
, 1978
"... A data structure is described which can be used for representing a collection of priority queues. The primitive operations are insertion, deletion, union, update, and search for an item of earliest priority. ..."
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Cited by 88 (1 self)
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A data structure is described which can be used for representing a collection of priority queues. The primitive operations are insertion, deletion, union, update, and search for an item of earliest priority.
Simple generation of static singleassignment form
 In Proceedings of the 9th International Conference on Compiler Construction
, 2000
"... cfl SpringerVerlag Abstract. The static singleassignment (SSA) form of a program provides data flow information in a form which makes some compiler optimizations easy to perform. In this paper we present a new, simple method for converting to SSA form, which produces correct solutions for nonreduc ..."
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Cited by 14 (0 self)
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cfl SpringerVerlag Abstract. The static singleassignment (SSA) form of a program provides data flow information in a form which makes some compiler optimizations easy to perform. In this paper we present a new, simple method for converting to SSA form, which produces correct solutions for nonreducible controlflow graphs, and produces minimal solutions for reducible ones. Our timing results show that, despite its simplicity, our algorithm is competitive with more established techniques. 1 Introduction The static singleassignment (SSA) form is a program representation in which variables are split into &quot;instances. &quot; Every new assignment to a variable or more generally, every new definition of a variable results in a new instance. The variable instances are numbered so that each use of a variable may be easily linked back to a single definition point. Figure 1 gives a example of SSA form for some straightline code. As its name suggests, SSA only reflects static properties; in the example, V1's value is a dynamic property, but the static property that all instances labelled V1 refer to the same value will still hold.
Finding dominators in practice
 In Proceedings of the 12th Annual European Symposium on Algorithms, volume 3221 of Lecture Notes in Computer Science
, 2004
"... Abstract. The computation of dominators in a flowgraph has applications in program optimization, circuit testing, and other areas. Lengauer and Tarjan [17] proposed two versions of a fast algorithm for finding dominators and compared them experimentally with an iterative bit vector algorithm. They c ..."
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Cited by 7 (2 self)
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Abstract. The computation of dominators in a flowgraph has applications in program optimization, circuit testing, and other areas. Lengauer and Tarjan [17] proposed two versions of a fast algorithm for finding dominators and compared them experimentally with an iterative bit vector algorithm. They concluded that both versions of their algorithm were much faster than the bitvector algorithm even on graphs of moderate size. Recently Cooper et al. [9] have proposed a new, simple, treebased iterative algorithm. Their experiments suggested that it was faster than the simple version of the LengauerTarjan algorithm on graphs representing computer program control flow. Motivated by the work of Cooper et al., we present an experimental study comparing their algorithm (and some variants) with careful implementations of both versions of the LengauerTarjan algorithm and with a new hybrid algorithm. Our results suggest that, although the performance of all the algorithms is similar, the most consistently fast are the simple LengauerTarjan algorithm and the hybrid algorithm, and their advantage increases as the graph gets bigger or more complicated. 1
A Tight Lower Bound for TopDown Skew Heaps
, 1997
"... Previously, it was shown in a paper by Kaldewaij and Schoenmakers that for topdown skew heaps the amortized number of comparisons required for meld and delmin is upper bounded by log OE n, where n is the total size of the inputs to these operations and OE = ( p 5+1)=2 denotes the golden ratio. In ..."
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Previously, it was shown in a paper by Kaldewaij and Schoenmakers that for topdown skew heaps the amortized number of comparisons required for meld and delmin is upper bounded by log OE n, where n is the total size of the inputs to these operations and OE = ( p 5+1)=2 denotes the golden ratio. In this paper we present worstcase sequences of operations on topdown skew heaps in which each application of meld and delmin requires approximately log OE n comparisons. As the remaining heap operations require no comparisons, it then follows that the set of bounds is tight. The result relies on a particular class of selfrecreating binary trees, which is related to a sequence known as Hofstadter's Gsequence. 1 Introduction Topdown skew heaps are probably the simplest implementation of mergeable priority queues to date while still achieving good performance. As with other socalled selfadjusting data structures the catch is that the performance is merely good in the amortized sense, but...