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247
An Extended Set of Fortran Basic Linear Algebra Subprograms
 ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
, 1986
"... This paper describes an extension to the set of Basic Linear Algebra Subprograms. The extensions are targeted at matrixvector operations which should provide for efficient and portable implementations of algorithms for high performance computers. ..."
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Cited by 450 (71 self)
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This paper describes an extension to the set of Basic Linear Algebra Subprograms. The extensions are targeted at matrixvector operations which should provide for efficient and portable implementations of algorithms for high performance computers.
Performance of various computers using standard linear equations software
, 2003
"... This report compares the performance of different computer systems in solving dense systems of linear equations. The comparison involves approximately a hundred computers, ranging from the Earth Simulator to personal computers. ..."
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Cited by 328 (20 self)
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This report compares the performance of different computer systems in solving dense systems of linear equations. The comparison involves approximately a hundred computers, ranging from the Earth Simulator to personal computers.
Interprocedural Compilation of Fortran D for MIMD DistributedMemory Machines
 COMMUNICATIONS OF THE ACM
, 1992
"... Algorithms exist for compiling Fortran D for MIMD distributedmemory machines, but are significantly restricted in the presence of procedure calls. This paper presents interprocedural analysis, optimization, and code generation algorithms for Fortran D that limit compilation to only one pass over ea ..."
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Cited by 309 (46 self)
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Algorithms exist for compiling Fortran D for MIMD distributedmemory machines, but are significantly restricted in the presence of procedure calls. This paper presents interprocedural analysis, optimization, and code generation algorithms for Fortran D that limit compilation to only one pass over each procedure. This is accomplished by collecting summary information after edits, then compiling procedures in reverse topological order to propagate necessary information. Delaying instantiation of the computation partition, communication, and dynamic data decomposition is key to enabling interprocedural optimization. Recompilation analysis preserves the benefits of separate compilation. Empirical results show that interprocedural optimization is crucial in achieving acceptable performance for a common application.
An Implementation of Interprocedural Bounded Regular Section Analysis
 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
, 1991
"... Optimizing compilers should produce efficient code even in the presence of highlevel language constructs. However, current programming support systems are significantly lacking in their ability to analyze procedure calls. This deficiency complicates parallel programming, because loops with calls ca ..."
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Cited by 210 (27 self)
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Optimizing compilers should produce efficient code even in the presence of highlevel language constructs. However, current programming support systems are significantly lacking in their ability to analyze procedure calls. This deficiency complicates parallel programming, because loops with calls can be a significant source of parallelism. We describe an implementation of regular section analysis, which summarizes interprocedural side effects on subarrays in a form useful to dependence analysis while avoiding the complexity of prior solutions. The paper gives the results of experiments on the Linpack library and a small set of scientific codes.
Improving Register Allocation for Subscripted Variables
, 1990
"... INTRODUCTION By the late 1980s, memory system performance and CPU performance had already begun to diverge. This trend made effective use of the register file imperative for excellent performance. Although most compilers at that time allocated scalar variables to registers using graph coloring with ..."
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Cited by 200 (34 self)
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INTRODUCTION By the late 1980s, memory system performance and CPU performance had already begun to diverge. This trend made effective use of the register file imperative for excellent performance. Although most compilers at that time allocated scalar variables to registers using graph coloring with marked success [12, 13, 14, 6], allocation of array values to registers only occurred in rare circumstances because standard dataflow analysis techniques could not uncover the available reuse of array memory locations. This deficiency was especially problematic for scientific codes since a majority of the computation involves array references. Our original paper addressed this problem by presenting an algorithm and experiment for a loop transformation, called scalar replacement, that exposed the reuse available in array references in an innermost loop. It also demonstrated experimentally how another loop transformation, called unrollandjam [2], could expose more opportunities for scalar…
Nonparametric regression using Bayesian variable selection
 Journal of Econometrics
, 1996
"... This paper estimates an additive model semiparametrically, while automatically selecting the significant independent variables and the app~opriatc power transformation of the dependent variable. The nonlinear variables arc modeled as regression splincs, with significant knots selected fiom a large ..."
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Cited by 136 (10 self)
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This paper estimates an additive model semiparametrically, while automatically selecting the significant independent variables and the app~opriatc power transformation of the dependent variable. The nonlinear variables arc modeled as regression splincs, with significant knots selected fiom a large number of candidate knots. The estimation is made robust by modeling the errors as a mixture of normals. A Bayesian approach is used to select the significant knots, the power transformation, and to identify oatliers using the Gibbs sampler to curry out the computation. Empirical evidence is given that the sampler works well on both simulated and real examples and that in the univariate case it compares faw)rably with a kernelweighted local linear smoother, The variable selection algorithm in the paper is substantially fasler than previous Bayesian variable sclcclion algorithms. K('I ' word~': Additive nlodel, Pov¢¢r Iransformalio:l: Robust cslinlalion
Sparse matrices in Matlab: Design and implementation
, 1991
"... We have extended the matrix computation language and environment Matlab to include sparse matrix storage and operations. The only change to the outward appearance of the Matlab language is a pair of commands to create full or sparse matrices. Nearly all the operations of Matlab now apply equally to ..."
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Cited by 131 (20 self)
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We have extended the matrix computation language and environment Matlab to include sparse matrix storage and operations. The only change to the outward appearance of the Matlab language is a pair of commands to create full or sparse matrices. Nearly all the operations of Matlab now apply equally to full or sparse matrices, without any explicit action by the user. The sparse data structure represents a matrix in space proportional to the number of nonzero entries, and most of the operations compute sparse results in time proportionaltothenumber of arithmetic operations on nonzeros.
Approaches for Bayesian variable selection
 Statistica Sinica
, 1997
"... Abstract: This paper describes and compares various hierarchical mixture prior formulations of variable selection uncertainty in normal linear regression models. These include the nonconjugate SSVS formulation of George and McCulloch (1993), as well as conjugate formulations which allow for analytic ..."
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Cited by 124 (5 self)
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Abstract: This paper describes and compares various hierarchical mixture prior formulations of variable selection uncertainty in normal linear regression models. These include the nonconjugate SSVS formulation of George and McCulloch (1993), as well as conjugate formulations which allow for analytical simplification. Hyperparameter settings which base selection on practical significance, and the implications of using mixtures with point priors are discussed. Computational methods for posterior evaluation and exploration are considered. Rapid updating methods are seen to provide feasible methods for exhaustive evaluation using Gray Code sequencing in moderately sized problems, and fast Markov Chain Monte Carlo exploration in large problems. Estimation of normalization constants is seen to provide improved posterior estimates of individual model probabilities and the total visited probability. Various procedures are illustrated on simulated sample problems and on a real problem concerning the construction of financial index tracking portfolios.
Optimizing for Parallelism and Data Locality
 In Proceedings of the 1992 ACM International Conference on Supercomputing
, 1992
"... Previous research has used program transformation to introduce parallelism and to exploit data locality. Unfortunately, these two objectives have usually been considered independently. This work explores the tradeoffs between effectively utilizing parallelism and memory hierarchy on sharedmemory mu ..."
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Cited by 94 (14 self)
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Previous research has used program transformation to introduce parallelism and to exploit data locality. Unfortunately, these two objectives have usually been considered independently. This work explores the tradeoffs between effectively utilizing parallelism and memory hierarchy on sharedmemory multiprocessors. We present a simple, but surprisingly accurate, memory model to determine cache line reuse from both multiple accesses to the same memory location and from consecutive memory access. The model is used in memory optimizing and loop parallelization algorithms that effectively exploit data locality and parallelism in concert. We demonstrate the efficacy of this approach with very encouraging experimental results. 1 Introduction Transformations to exploit parallelism and to improve data locality are two of the most valuable compiler techniques in use today. Independently, each of these optimizations has been shown to result in dramatic improvements. This paper seeks to combine t...
Numerical solution of the stable, nonnegative definite Lyapunov equation
 IMA J. Numer. Anal
, 1982
"... We discuss the numerical solution of the Lyapunov equation ..."
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Cited by 87 (2 self)
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We discuss the numerical solution of the Lyapunov equation