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131
Parameterized Complexity
, 1998
"... the rapidly developing systematic connections between FPT and useful heuristic algorithms  a new and exciting bridge between the theory of computing and computing in practice. The organizers of the seminar strongly believe that knowledge of parameterized complexity techniques and results belongs ..."
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Cited by 1213 (77 self)
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the rapidly developing systematic connections between FPT and useful heuristic algorithms  a new and exciting bridge between the theory of computing and computing in practice. The organizers of the seminar strongly believe that knowledge of parameterized complexity techniques and results belongs into the toolkit of every algorithm designer. The purpose of the seminar was to bring together leading experts from all over the world, and from the diverse areas of computer science that have been attracted to this new framework. The seminar was intended as the rst larger international meeting with a specic focus on parameterized complexity, and it hopefully serves as a driving force in the development of the eld. 1 We had 49 participants from Australia, Canada, India, Israel, New Zealand, USA, and various European countries. During the workshop 25 lectures were given. Moreover, one night session was devoted to open problems and Thursday was basically used for problem discussion
Structured Programs have Small TreeWidth and Good Register Allocation
 Information and Computation
, 1995
"... The register allocation problem for an imperative program is often modelled as the coloring problem of the interference graph of the controlflow graph of the program. The interference graph of a flow graph G is the intersection graph of some connected subgraphs of G. These connected subgraphs repre ..."
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Cited by 67 (1 self)
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The register allocation problem for an imperative program is often modelled as the coloring problem of the interference graph of the controlflow graph of the program. The interference graph of a flow graph G is the intersection graph of some connected subgraphs of G. These connected subgraphs represent the lives, or life times, of variables, so the coloring problem models that two variables with overlapping life times should be in different registers. For general programs with unrestricted gotos, the interference graph can be any graph, and hence we cannot in general color within a factor O(n " ) from optimality unless NP=P. It is shown that if a graph has treewidth k, we can efficiently color any intersection graph of connected subgraphs within a factor (bk=2c + 1) from optimality. Moreover, it is shown that structured (j gotofree) programs, including, for example, short circuit evaluations and multiple exits from loops, have treewidth at most 6. Thus, for every structured progr...
Scheduling Split Intervals
, 2002
"... We consider the problem of scheduling jobs that are given as groups of nonintersecting segments on the real line. Each job Jj is associated with an interval, Ij, which consists of up to t segments, for some t _) 1, a of their segments intersect. Such jobs show up in a I.I Problem Statement and Mo ..."
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Cited by 63 (5 self)
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We consider the problem of scheduling jobs that are given as groups of nonintersecting segments on the real line. Each job Jj is associated with an interval, Ij, which consists of up to t segments, for some t _) 1, a of their segments intersect. Such jobs show up in a I.I Problem Statement and Motivation. We wide range of applications, including the transmission consider the problem of scheduling jobs that are given of continuousmedia data, allocation of linear resources as groups of nonintersecting segments on the real line. (e.g. bandwidth in linear processor arrays), and in Each job Jj is associated with a tinterval, Ij, which
Monadic secondorder definable graph transductions: A survey
 TCS
, 1994
"... Courcelle, B., Monadic secondorder definable graph transductions: a survey, Theoretical Computer Science 126 (1994) 5375. Formulas of monadic secondorder logic can be used to specify graph transductions, i.e., multivalued functions from graphs to graphs. We obtain in this way classes of graph tr ..."
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Cited by 59 (8 self)
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Courcelle, B., Monadic secondorder definable graph transductions: a survey, Theoretical Computer Science 126 (1994) 5375. Formulas of monadic secondorder logic can be used to specify graph transductions, i.e., multivalued functions from graphs to graphs. We obtain in this way classes of graph transductions, called monadic secondorder definable graph transductions (or, more simply, d&able transductions) that are closed under composition and preserve the two known classes of contextfree sets of graphs, namely the class of hyperedge replacement (HR) and the class of vertex replacement (VR) sets. These two classes can be characterized in terms of definable transductions and recognizable sets of finite trees, independently of the rewriting mechanisms used to define the HR and VR grammars. When restricted to words, the definable transductions are strictly more powerful than the rational transductions such that the image of every finite word is finite; they do not preserve contextfree languages. We also describe the sets of discrete (edgeless) labelled graphs that are the images of HR and VR sets under definable transductions: this gives a version of Parikh’s theorem (i.e., the characterization of the commutative images of contextfree languages) which extends the classical
Highly Parallel Sparse Cholesky Factorization
 SIAM Journal on Scientific and Statistical Computing
, 1992
"... We develop and compare several finegrained parallel algorithms to compute the Cholesky factorization of a sparse matrix. Our experimental implementations are on the Connection Machine, a distributedmemory SIMD machine whose programming model conceptually supplies one processor per data element. In ..."
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Cited by 48 (1 self)
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We develop and compare several finegrained parallel algorithms to compute the Cholesky factorization of a sparse matrix. Our experimental implementations are on the Connection Machine, a distributedmemory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to specialpurpose algorithms in which the matrix structure conforms to the connection structure of the machine, our focus is on matrices with arbitrary sparsity structure.
Register allocation for programs in ssaform
 In Compiler Construction 2006, volume 3923 of LNCS
, 2006
"... In this technical report, we present an architecture for register allocation on the SSAform. We show, how the properties of SSAform programs and their interference graphs can be exploited to develop new methods for spilling, coloring and coalescing. We present heuristic and optimal solution method ..."
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Cited by 44 (5 self)
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In this technical report, we present an architecture for register allocation on the SSAform. We show, how the properties of SSAform programs and their interference graphs can be exploited to develop new methods for spilling, coloring and coalescing. We present heuristic and optimal solution methods for these three subtasks. 1
Fully dynamic algorithms for chordal graphs
, 2001
"... We present the first dynamic algorithm that maintains a clique tree representation of a chordal graph and supports the following operations: (1) query whether deleting or inserting an arbitrary edge preserves chordality, (2) delete or insert an arbitrary edge, provided it preserves chordality. We gi ..."
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Cited by 33 (2 self)
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We present the first dynamic algorithm that maintains a clique tree representation of a chordal graph and supports the following operations: (1) query whether deleting or inserting an arbitrary edge preserves chordality, (2) delete or insert an arbitrary edge, provided it preserves chordality. We give two implementations. In the first, each operation runs in O(n) time, where n is the number of vertices. In the second, an insertion query runs in O(log² n) time, an insertion in O(n) time, a deletion query in O(n) time, and a deletion in O(n log n) time. We also present a data structure that allows a deletion query to run in O ( p m) time in either implementation, where m is the current number of edges. Updating this data structure after a deletion or insertion requires O(m) time. We also present a very simple dynamic algorithm that supports each of the following operations in O(1) time on a general graph: (1) query whether the graph is split, (2) delete or insert an arbitrary edge.
Scalar Aggregation in FDInconsistent Databases
 In Database Theory  ICDT 2001, Springer, LNCS
, 2001
"... We consider here scalar aggregation queries in databases that may violate a given set of functional dependencies. We show how to compute consistent answers (answers true in every minimal repair of the database) to such queries. We provide a complete characterization of the computational complexity o ..."
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Cited by 30 (15 self)
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We consider here scalar aggregation queries in databases that may violate a given set of functional dependencies. We show how to compute consistent answers (answers true in every minimal repair of the database) to such queries. We provide a complete characterization of the computational complexity of this problem. We also show how tractability can be obtained in several special cases (one involves a novel application of the perfect graph theory) and present a practical hybrid query evaluation method.