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Runtime Compilation for Parallel Sparse Matrix Computations
 In Proceedings of ACM International Conference on Supercomputing
, 1996
"... Runtime compilation techniques have been shown effective for automating the parallelization of loops with unstructured indirect data accessing patterns. However, it is still an open problem to efficiently parallelize sparse matrix factorizations commonly used in iterative numerical problems. The di ..."
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Cited by 18 (10 self)
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Runtime compilation techniques have been shown effective for automating the parallelization of loops with unstructured indirect data accessing patterns. However, it is still an open problem to efficiently parallelize sparse matrix factorizations commonly used in iterative numerical problems
Tools and Libraries for Parallel Sparse Matrix Computations
, 1995
"... This paper describes two portable packages for generalpurpose sparse matrix computations: SPARSKIT and P SPARSLIB. Their emphasis is on iterative techniques, with the latter also emphasizing parallel computation. The packages are a collection of tools which may be used either as a library, or as ..."
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This paper describes two portable packages for generalpurpose sparse matrix computations: SPARSKIT and P SPARSLIB. Their emphasis is on iterative techniques, with the latter also emphasizing parallel computation. The packages are a collection of tools which may be used either as a library
Exploiting Locality on Parallel Sparse Matrix Computations
, 1995
"... By now, irregular problems are difficult to parallelize in an automatic way because of their lack of regularity in data access patterns. Most times, programmers must handwrite a particular solution for each problem separately. In this paper we present two pseudoregular distributions which can be a ..."
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Cited by 4 (3 self)
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problems constitute the most annoying field of parallel computing research. There are no adequate methods nor heuristics for automatic partitioning of either computations and data while parallelizing a large family of problems. In some cases, the problem must be analized to find some regularity (that is
Parallel Sparse Matrix Computations Using the PINEAPL Library: A Performance Study
"... . The Numerical Algorithms Group Ltd is currently participating in the European HPCN Fourth Framework project on Parallel I ndustrial NumE rical Applications and Portable Libraries (PINEAPL). One of the main goals of the project is to increase the suitability of the existing NAG Parallel Library for ..."
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parallel library routines. A substantial part of the library material being developed is concerned with the solution of PDE problems using parallel sparse linear algebra modules. These modules provide support for crucial computational tasks such as graph partitioning, preconditioning and iterative solution
Experience with FineGrain Communication in EMX Multiprocessor for Parallel Sparse Matrix Computation
"... Sparse matrix problems require a communication paradigm different from those used in conventional distributedmemory multiprocessors. We present in this paper how finegrain communication can help obtain high performance in the experimental distributedmemory multiprocessor, EMX, developed at ETL, ..."
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Sparse matrix problems require a communication paradigm different from those used in conventional distributedmemory multiprocessors. We present in this paper how finegrain communication can help obtain high performance in the experimental distributedmemory multiprocessor, EMX, developed at ETL
The University of Florida sparse matrix collection
 NA DIGEST
, 1997
"... The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural enginee ..."
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Cited by 536 (17 self)
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The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural
Parallel Numerical Linear Algebra
, 1993
"... We survey general techniques and open problems in numerical linear algebra on parallel architectures. We first discuss basic principles of parallel processing, describing the costs of basic operations on parallel machines, including general principles for constructing efficient algorithms. We illust ..."
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Cited by 773 (23 self)
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We survey general techniques and open problems in numerical linear algebra on parallel architectures. We first discuss basic principles of parallel processing, describing the costs of basic operations on parallel machines, including general principles for constructing efficient algorithms. We
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 555 (22 self)
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remarkable features making this attractive for lowrank matrix completion problems. The first is that the softthresholding operation is applied to a sparse matrix; the second is that the rank of the iterates {X k} is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
, 2007
"... A fullrank matrix A ∈ IR n×m with n < m generates an underdetermined system of linear equations Ax = b having infinitely many solutions. Suppose we seek the sparsest solution, i.e., the one with the fewest nonzero entries: can it ever be unique? If so, when? As optimization of sparsity is combin ..."
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Cited by 427 (36 self)
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A fullrank matrix A ∈ IR n×m with n < m generates an underdetermined system of linear equations Ax = b having infinitely many solutions. Suppose we seek the sparsest solution, i.e., the one with the fewest nonzero entries: can it ever be unique? If so, when? As optimization of sparsity
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
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Cited by 736 (22 self)
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is a computationally attractive alternative to standard covariance selection for sparse highdimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We
Results 1  10
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