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47,760
Highly scalable parallel algorithms for sparse matrix factorization
 IEEE Transactions on Parallel and Distributed Systems
, 1994
"... In this paper, we describe a scalable parallel algorithm for sparse matrix factorization, analyze their performance and scalability, and present experimental results for up to 1024 processors on a Cray T3D parallel computer. Through our analysis and experimental results, we demonstrate that our algo ..."
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Cited by 130 (27 self)
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In this paper, we describe a scalable parallel algorithm for sparse matrix factorization, analyze their performance and scalability, and present experimental results for up to 1024 processors on a Cray T3D parallel computer. Through our analysis and experimental results, we demonstrate that our
Scalable parallel algorithms for fpt problems
 Algorithmica
, 2006
"... Algorithmic methods based on the theory of fixedparameter tractability are combined with powerful computational platforms to launch systematic attacks on combinatorial problems of significance. As a case study, optimal solutions to very large instances of the NPhard vertex cover problem are comput ..."
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Cited by 29 (14 self)
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are computed. To accomplish this, an efficient sequential algorithm and various forms of parallel algorithms are devised, implemented and compared. The importance of maintaining a balanced decomposition of the search space is shown to be critical to achieving scalability. Target problems need only be amenable
Scalable Parallel Algorithms for Predictive Modelling
, 2000
"... Data Mining applications have to deal with increasingly large data sets and complexity. Only algorithms which scale linearly with data size are feasible. We present parallel regression algorithms which after a few initial scans of the data compute predictive models for data mining and do not require ..."
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Data Mining applications have to deal with increasingly large data sets and complexity. Only algorithms which scale linearly with data size are feasible. We present parallel regression algorithms which after a few initial scans of the data compute predictive models for data mining and do
Scalable Parallel Algorithms for Surface Fitting and Data Mining
"... This paper presents scalable parallel algorithms for high dimensional surface fitting and predictive modelling which are used in data mining applications. These algorithms are based on techniques like finite elements, thin plate splines, wavelets and additive models. They all consist of two steps: F ..."
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This paper presents scalable parallel algorithms for high dimensional surface fitting and predictive modelling which are used in data mining applications. These algorithms are based on techniques like finite elements, thin plate splines, wavelets and additive models. They all consist of two steps
Some scalable parallel algorithms for geometric problems
 Journal of Parallel and Distributed Computing
, 1999
"... This paper considers a variety of geometric pattern recognition problems on input sets of size n using a coarse grained multicomputer model consisting of p processors with 0(n p) local memory each (i.e., 0(n p) memory cells of 3(log n) bits apiece), where the processors are connected to an arbitrary ..."
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Cited by 7 (2 self)
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to an arbitrary interconnection network. It introduces efficient scalable parallel algorithms for a number of geometric problems including the rectangle finding problem, the maximal equally spaced collinear points problem, and the point set pattern matching problem. All of the algorithms presented are scalable
A Scalable Parallel Algorithm for Sparse Cholesky Factorization
 In SuperComputing '94
"... In this paper, we describe a scalable parallel algorithm for sparse Cholesky factorization, analyze its performance and scalability, and present experimental results of its implementation on a 1024processor nCUBE2 parallel computer. Through our analysis and experimental results, we demonstrate that ..."
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Cited by 5 (0 self)
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In this paper, we describe a scalable parallel algorithm for sparse Cholesky factorization, analyze its performance and scalability, and present experimental results of its implementation on a 1024processor nCUBE2 parallel computer. Through our analysis and experimental results, we demonstrate
A scalable parallel algorithm for incomplete factor preconditioning
 SIAM Journal on Scientific Computing
"... Abstract. We describe a parallel algorithm for computing incomplete factor (ILU) preconditioners. The algorithm attains a high degree of parallelism through graph partitioning and a twolevel ordering strategy. Both the subdomains and the nodes within each subdomain are ordered to preserve concurren ..."
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Cited by 39 (3 self)
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concurrency. We show through an algorithmic analysis and through computational results that this algorithm is scalable. Experimental results include timings on three parallel platforms for problems with up to 20 million unknowns running on up to 216 processors. The resulting preconditioned Krylov solvers have
Scalable Parallel Algorithms for Surface Fitting and Data Mining
, 2000
"... This paper presents parallel scalable algorithms for high dimensional surface fitting and predictive modelling which can be used in data mining applications. The presented algorithms are based on techniques like finite elements, thin plate splines, additive models and wavelets. They consist of two p ..."
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Cited by 8 (8 self)
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This paper presents parallel scalable algorithms for high dimensional surface fitting and predictive modelling which can be used in data mining applications. The presented algorithms are based on techniques like finite elements, thin plate splines, additive models and wavelets. They consist of two
Fast and Scalable Parallel Algorithms for KnapsackLike Problems
 JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
, 1996
"... We present two new algorithms for searching in sorted X+Y +R+S, one based on heaps and the other on sampling. Each of the algorithms runs in time O(n 2 logn) (n being the size of the sorted arrays X, Y , R and S). Hence in each case, by constructing arrays of size n = O(2 s=4 ), we obtain a new ..."
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Cited by 3 (0 self)
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algorithm for solving certain NPComplete problems such as Knapsack on s data items in time equal (up to a constant factor) to the best algorithm currently known. Each of the algorithms is capable of being efficiently implemented in parallel and so solving large instances of these NPComplete problems fast
Results 1  10
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