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A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

We present a distributed-memory library for computations with dense structured matrices. A matrix is... (more)

Matslise 2.0

The Matslise 2.0 software package is a thorough revision of the successful Matlab package Matslise of 2005. The package can be used to compute the eigenvalues and eigenfunctions of regular and some important classes of singular self-adjoint Sturm-Liouville boundary value problems. The code uses new or improved algorithms, offers some new features,... (more)

An Experimental Exploration of Marsaglia's xorshift Generators, Scrambled

Marsaglia proposed xorshift generators are a class of very fast, good-quality pseudorandom number generators. Subsequent analysis by Panneton and... (more)

Manycore Algorithms for Batch Scalar and Block Tridiagonal Solvers

Engineering, scientific, and financial applications often require the simultaneous solution of a large number of independent tridiagonal systems of... (more)

Discrete Wavelet Transforms in the Large Time-Frequency Analysis Toolbox for MATLAB/GNU Octave

The discrete wavelet transform module is a recent addition to the Large Time-Frequency Analysis... (more)

Algorithm 963

We describe the implementation of a parameter estimation method suitable for models commonly used in quantitative finance. The Continuum-Generalized Method of Moments (CGMM) is a Generalized Method of Moments (GMM) type of methodology that applies a continuum of moment conditions to achieve the efficiency of a Maximum Likelihood method. Instead of... (more)

Algorithm 964

A simple and efficient algorithm to numerically compute the genus of surfaces of three-dimensional objects using the Euler characteristic formula is presented. The algorithm applies to objects obtained by thresholding a scalar field in a structured-collocated grid and does not require any triangulation of the data. This makes the algorithm fast,... (more)

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Forthcoming Articles

General Template Units for the Finite Volume Method in Box-shaped Domains

Algorithm XXX: A Practical Iterative Algorithm for the Art Gallery Problem using Integer Linear Programming

In the last decades, the search for exact algorithms for NP-hard geometric problems has intensified. Many of these solutions use Integer Linear Programming (ILP) modeling and rely on state of the art solvers to find optimal solutions for large instances, in a matter of minutes. Here, we discuss an ILP based algorithm that solves to optimality the Art Gallery Problem (AGP), one of the most studied problems in Computational Geometry. The basic idea of our method is to iteratively generate upper and lower bounds through the resolution of discretized versions of the AGP, which are reduced to instances of the Set Cover Problem. Our algorithm was implemented and tested on almost 3000 instances and attained optimal solutions for the vast majority of them, greatly increasing the set of instances for which exact solutions are known. To our knowledge, in spite of the extensive study of the AGP, no other algorithm has shown the ability to solve the AGP as effectively and efficiently as the one described here. Evidence of its robustness is presented through tests done on a number of classes of polygons of various sizes with and without holes. A sofware implementing the algorithm is made available.

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

To orthonormalize a set of dense vectors, Singular Value QR (SVQR) requires only one global reduction between the parallel processing units, and uses BLAS-3 kernels to perform most of its local computation. As a result, compared to other orthogonalization schemes, SVQR obtains superior performance on many of the current computers. In this paper, we study the stability and performance of various SVQR implementations on multicore CPUs with a GPU, focusing on the dense triangular solve, which performs half of the total floating-point operations in SVQR. As a part of this study, we examine its adaptive mixed-precision variant that decides if a lower-precision arithmetic can be used for the triangular solution at runtime without increasing the order of its orthogonality error. Since the backward error of this adaptive mixed-precision variant is significantly greater than that of the standard SVQR, we study its effects on the solution convergence of several subspace projection methods for solving a linear system of equations and for computing singular values or eigenvalues of a sparse matrix. Our experimental results indicate that in some cases, the convergence rate of the solver may not be affected by the larger backward errors, while reducing the time to solution.

Systematic Alias Sampling: an efficient and low-variance way to sample from a discrete distribution

In this paper we combine the Alias method with the concept of systematic sampling, a method commonly used in particle filters for efficient low-variance resampling. The proposed method allows very fast sampling from a discrete distribution: drawing $k$ samples is up to an order of magnitude faster than binary search from the cumulative distribution function (cdf) or inversion methods used in many libraries. The produced empirical distribution function is evaluated using a modified Cramér-Von Mises goodness-of-fit statistic, showing that the method compares very favourably to multinomial sampling. As continuous distributions can often be approximated with discrete ones, the proposed method can be used as a very general way to efficiently produce random samples for particle filter proposal distributions, e.g. for motion models in robotics.

ADiJaC  Automatic Differentiation of Java Classfiles

This work presents the current design and implementation of ADiJaC, an automatic differentiation tool for Java classfiles. ADiJaC uses source code transformation to generate derivative codes in both the forward and the reverse modes of automatic differentiation. We describe the overall architecture of the tool and present various details and examples for each of the two modes of differentiation. We emphasize the enhancements that have been made over previous versions of ADiJaC and illustrate their influence on the generality of the tool and on the performance of the generated derivative codes. The ADiJaC tool has been used to generate derivatives for a variety of problems, including real-world applications. We evaluate the performance of such codes and compare it to derivatives generated by Tapenade, a well-established automatic differentiation tool for Fortran and C/C++. Additionally, we present more detailed performance analyses of two real-world applications. Apart from being the only general-purpose automatic differentiation tool for Java bytecode, we argue that ADiJaC's features and performance are comparable to those of similar mature tools for other programming languages such as C/C++ or Fortran.

A note on performance profiles for benchmarking software

In recent years, performance profiles have become a popular and widely used tool for benchmarking and evaluating the performance of several solvers when run on a large test set. Here we use data from a real application as well as a simple artificial example to illustrate that caution should be exercised when trying to interpret performance profiles to assess the relative performance of the solvers.

Pipelined Iterative Solvers with Kernel Fusion for Graphics Processing Units

We revisit the implementation of iterative solvers on discrete graphics processing units and demonstrate the benefit of implementations using extensive kernel fusion for pipelined formulations over conventional implementations of classical formulations. The proposed implementations with both CUDA and OpenCL are freely available in ViennaCL and are shown to be competitive with or even superior to other solver packages for graphics processing units. Highest performance gains are obtained for small to medium-sized systems, while our implementations are on par with vendor-tuned implementations for very large systems. Our results are especially beneficial for transient problems, where many small to medium-sized systems instead of a single big system need to be solved.

IEEE754 precision-$k$ base-$\beta$ arithmetic inherited by precision-$m$ base-$\beta$ arithmetic for $k<m$

RIDC Methods: Software for Parallel-Time Integration

Revisionist integral deferred correction (RIDC) methods are a family of parallel--in--time methods to solve systems of initial values problems. The approach is able to bootstrap lower order time integrators to provide high order approximations in approximately the same wall clock time, hence providing a multiplicative increase in the number of compute cores utilized. Here we provide a C++ framework which automatically produces a parallel--in--time solution of a system of initial value problems given user supplied code for the right hand side of the system and a sequential code for a first-order time step. The user supplied time step routine may be explicit or implicit and may make use of any auxiliary libraries which take care of the solution of any nonlinear algebraic systems which may arise or the numerical linear algebra required. The code contains six examples of increasing complexity which also serve as templates to solve user defined problems.

Analytical Modeling is Enough for High Performance BLIS

We show how the BLAS-like Library Instantiation Software (BLIS) framework, which provides a more detailed layering of the GotoBLAS (now maintained as OpenBLAS) implementation, allows one to analytically determine optimal tuning parameters for high-end instantiations of the matrix-matrix multiplication. This is of both practical and scientific importance, as it greatly reduces the development effort required for the implementation of the level-3 BLAS while also advancing our understanding of how hierarchically layered memories interact with high performance software. This allows the community to move on from valuable engineering solutions (empirically autotuning) to scientific understanding (analytical insight).

Algorithm xxx: DISODE45: A Matlab Runge-Kutta solver for Piecewise Smooth IVPs of Filippov type

In this paper an adaptive Runge-Kutta code, based on the DOPRI5(4) pair, for solving Initial Value Problems for differential systems with Piecewise Smooth solutions (PWS) is presented and the algorithms used in the code are described. The code automatically detects and locate accurately the switching points of the PWS, restarting the integration after each discontinuity. Further, in the case of Filippov systems, algorithms to handle properly sliding mode regimes in an automatic way are included. The code requires from the user a description of the IVP and the functions defining the hypersurfaces where the switching points are located, and it gives the discrete approximated solution together with the switching points. Several numerical experiments are presented to illustrate the reliability and efficiency of the code.

Parallel Memory Efficient Adaptive Mesh Refinement on Structured Triangular Meshes with Billions of Grid Cells

We present sam(oa)², a software package for dynamically adaptive, parallel solution of 2D partial differential equations on triangular grids created via newest vertex bisection. Using an element-oriented data view suitable for local operators, an element order imposed by the Sierpinski space-filling curve provides an algorithm for grid generation, refinement and traversal that is inherently memory efficient. Based purely on stack and stream data structures, it completely avoids random memory access. Scenarios are implemented based on control loops and event hooks, which hide the complexity of the underlying traversal scheme. Two case studies are presented: Two-phase flow in heterogeneous porous media and tsunami wave propagation, demonstrated on the Tohoku tsunami 2011 in Japan. sam(oa)² features hybrid MPI+OpenMP parallelization based on the Sierpinski order induced on the elements. Sections defined by contiguous grid cells define atomic tasks for OpenMP work sharing and stealing, as well as for migration of grid cells between MPI processes. Using optimized communication and load balancing algorithms, sam(oa)² achieves 88% strong scaling efficiency on 512 cores and 92% efficiency in a weak scaling test on 8192 cores with 10 billion elements - all tests including adaptive mesh refinement and load balancing in each time step.

Replicated Computational Results (RCR) Report for ``A distributed-memory package for dense Hierarchically Semi-Separable matrix computations using randomization'

In this report we replicate a subset of the performance results in the paper ``A distributed-memory package for dense Hierarchically Semi-Separable matrix computations using randomization''.

An efficient representation format for fuzzy intervals based on symmetric membership functions

This paper proposes a novel implementation of fuzzy arithmetics that exploits both fuzzy intervals and hardware specificities. First, we propose and evaluate the benefit of an alternative representation format to the traditional lower-upper and midpoint-radius representation formats for intervals. Thanks to the proposed formats, we show that it is possible to halve the number of operations and memory requirements compared to conventional methods. Then, we show that operations on fuzzy intervals are sensitive to hardware specificities of accelerators such as GPU. These include static rounding, memory usage, instruction level parallelism (ILP) and thread-level parallelism (TLP). We develop a library of fuzzy arithmetic operations in CUDA and C++ over several formats. The proposed library is evaluated using compute-bound and memory-bound benchmarks on Nvidia GPUs, and shows a performance gain of 2 to 20 over traditional approaches.

Algorithm xxx: A Distributed Memory Fast Multipole Method for Volume Potentials

The solution of a constant-coefficient elliptic partial differential equation (PDE) can be computed using an integral transform: a convolution with the fundamental solution of the PDE, also known as a volume potential. We present a Fast Multipole Method (FMM) for computing volume potentials and use them to construct spatially-adaptive solvers for the Poisson, Stokes and Helmholtz problems. Conventional N-body methods apply to discrete particle interactions. With volume potentials, one replaces the sums with volume integrals. In this paper, we discuss the efficient implementation of such an FMM. We use high-order piecewise Chebyshev polynomials and an octree data structure to represent the input and output fields, enable spectrally accurate approximation of the near field, and the kernel independent FMM (KIFMM) for the far field approximation. For distributed memory parallelism, we use space filling curves, locally essential trees, and a hypercube-like communication scheme developed previously in our group. We present new near and far interaction traversals which optimize cache usage and use vectorization, including the AVX instruction set to get over 50% of peak floating point performance. We use task parallelism to employ the Xeon Phi co-processors.

Implementing multifrontal sparse solvers for multicore architectures with Sequential Task Flow runtime systems

To face the advent of multicore processors and the ever increasing complexity of hardware architectures, programming models based on DAG parallelism regained popularity in the high performance, scientific computing community. Modern runtime systems offer a programming interface that complies with this paradigm and powerful engines for scheduling the tasks into which the application is decomposed. These tools have already proved their effectiveness on a number of dense linear algebra applications. This paper evaluates the usability and effectiveness of runtime systems based on the Sequential Task Flow model for complex applications, namely, sparse matrix multifrontal factorizations which feature extremely irregular workloads, with tasks of different granularities and characteristics and with a variable memory consumption. Most importantly, it shows how this parallel programming model eases the development of complex features that benefit the performance of sparse, direct solvers as well as their memory consumption. We illustrate our discussion with the multifrontal QR factorization running on top of the StarPU runtime system.

\texttt{PSelInv}--A Distributed Memory Parallel Algorithm for Selected Inversion : the Symmetric Case

Modular SIMD arithmetic in Mathemagix

Modular integer arithmetic occurs in many algorithms for computer algebra, cryptography, and error correcting codes. Although recent microprocessors typically offer a wide range of highly optimized arithmetic functions, modular integer operations still require dedicated implementations. In this article, we survey existing algorithms for modular integer arithmetic, and present detailed vectorized counterparts. We also present several applications, such as fast modular Fourier transforms and multiplication of integer polynomials and matrices. The vectorized algorithms have been implemented in C++ inside the free computer algebra and analysis system Mathemagix. The performance of our implementation is illustrated by various benchmarks.

A High Performance QDWH-SVD Solver using Hardware Accelerators

This paper describes a new high performance implementation of the QR-based Dynamically Weighted Halley Singular Value Decomposition (QDWH-SVD) solver on multicore architecture enhanced with multiple GPUs. The standard QDWH-SVD algorithm was introduced by Nakatsukasa and Higham (SIAM SISC, 2013) and combines three successive computational stages: (1) the polar decomposition calculation using the QDWH algorithm, (2) the symmetric eigendecomposition of the resulting polar factor to obtain the singular values and the right singular vectors and (3) the matrix-matrix multiplication to get the associated left singular vectors. A comprehensive test suite highlights the numerical robustness of the QDWH-SVD solver. Although it performs up to two times more flops when computing all singular vectors compared to the standard SVD solver algorithm, our new high performance implementation results in up to 4x improvements for asymptotic matrix sizes, compared to the equivalent routines from existing state-of-the-art open-source and commercial libraries. However, when only singular values are needed, QDWH-SVD is penalized by performing up to 14 times more flops but can still run up to 18% faster than the best existing equivalent routines. Integrating mixed precision techniques in the solver can additionally provide up to 40% improvement at the price of losing few digits of accuracy.

On BLAS Level-3 Implementations of Common Solvers for (Quasi-) Triangular Generalized Lyapunov Equations

The solutions of Lyapunov and generalized Lyapunov equations are a key player in many applications in systems and control theory. Their stable numerical computation, when the full solution is sought, is considered solved since the seminal work of Bartels and Stewart. A number of variants of their algorithm have been proposed, but none of them goes beyond BLAS level-2 style implementation. On modern computers, however, the formulation of level-3 BLAS type implementations is crucial to enable optimal usage of cache hierarchies and modern block scheduling methods based on directed acyclic graphs describing the interdependence of single block computations. Our contribution closes this gap by a transformation of the aforementioned level-2 variants to level-3 versions and a comparison on a standard multicore machine.

Topology-oriented Incremental Algorithm for the Robust Construction of the Voronoi Diagrams of Disks

The Voronoi diagram of circles in R^2 is important on its own and for its extension to that of spherical balls in R^3. However, its robust computation has remained as a challenge. In this paper, we propose a new algorithm, a topology-oriented robust algorithm which incrementally computes the Voronoi diagram by adding a new circle generator to an existing one. A benchmark using a large data set including highly degenerate cases shows that the proposed algorithm is significantly superior to CGAL from the perspectives of accuracy of both solution, computational efficiency, and algorithmic robustness. The idea of the proposed algorithm can be easily extended to its three-dimensional counterpart which has critical applications for biotechnology, material science, etc.

GPU-accelerated generation of correctly-rounded elementary functions

The IEEE 754-2008 standard recommends the correct rounding of some elementary functions. This requires to solve the Table Makers Dilemma which implies a huge amount of CPU computation time. We consider in this paper accelerating such computations, namely Lefèvre algorithm on Graphics Processing Units (GPUs) which are massively parallel architectures with a partial SIMD execution (Single Instruction Multiple Data). We first propose an analysis of the Lefèvre hard-to-round argument search using the concept of continued fractions. We then propose a new parallel search algorithm much more efficient on GPU thanks to its more regular control flow. We also present an efficient hybrid CPU-GPU deployment of the generation of the polynomial approximations required in Lefèvre algorithm. In the end, we manage to obtain overall speedups up to 53.4x on one GPU over a sequential CPU execution, and up to 7.1x over a multi-core CPU, which enable a much faster solving of the Table Makers Dilemma for the double precision format.

A Nonlinear QR Algorithm for Banded Nonlinear Eigenvalue Problems

A robust and scalable implementation of the Parks-McClellan algorithm for designing FIR filters

With a long history dating back to the beginning of the 1970s, the Parks-McClellan algorithm is probably the most well-known alternative for designing finite impulse response filters. Despite being a standard routine in many signal processing packages, it is possible to find practical design specifications where such codes fail to work. In this paper, we introduce a new implementation of this algorithm. It is based on three main ingredients: (1) a new heuristic initialization strategy that generally improves the convergence properties of the Parks-McClellan routine, (2) numerically stable barycentric Lagrange interpolation formulas, and (3) colleague matrix-based rootfinding algorithms. We argue that our approach is very robust in practice, even for hard to design problems. The result, an open source C++ library, is capable of constructing filters where the final degree is more than 50000, outperforming other implementations.

ALGORITHM xxx: MINRES-QLP for Singular Symmetric and Hermitian Linear Equations and Least-Squares Problems

WorkStream -- a design pattern for multicore-enabled finite element computations

About TOMS

The purpose of the ACM Transactions on Mathematical Software (TOMS) is to communicate important research results addressing the development, evaluation and use of mathematical software...

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Bibliometrics

Publication Years 1975-2016
Publication Count 1450
Citation Count 12967
Available for Download 1450
Downloads (6 weeks) 4619
Downloads (12 Months) 46957
Downloads (cumulative) 1796442
Average downloads per article 1239
Average citations per article 9
First Name Last Name Award
Vernon Austel ACM Gordon Bell Prize (2006)
David H Bailey ACM Gordon Bell Prize (2008)
Ronald Boisvert ACM Distinguished Member (2006)
Bruno Buchberger ACM Paris Kanellakis Theory and Practice Award (2007)
Edmond Chow ACM Gordon Bell Prize
Special Category (2009) ACM Gordon Bell Prize
Special Category (2009)
James Demmel ACM Paris Kanellakis Theory and Practice Award (2014)
Jack Dongarra ACM-IEEE CS Ken Kennedy Award (2013)
Stuart Feldman ACM Software System Award (2003)
John Gunnels ACM Gordon Bell Prize (2007)
John A. Gunnels ACM Gordon Bell Prize (2006)
Michael A Heroux ACM Distinguished Member (2009)
William Kahan ACM A. M. Turing Award (1989)
Matthew Knepley SIAM/ACM Prize in Computational Science and Engineering (2014)
David J Kuck ACM-IEEE CS Ken Kennedy Award (2010)
ACM-IEEE CS Eckert-Mauchly Award (1993)
Byounghak Lee ACM Gordon Bell Prize (2008)
Lois C McInnes SIAM/ACM Prize in Computational Science and Engineering (2014)
Cleve Moler SIAM/ACM Prize in Computational Science and Engineering (2009)
Dianne P O'Leary ACM Distinguished Member (2006)
Naren Ramakrishnan ACM Distinguished Member (2009)
Sartaj K Sahni ACM Karl V. Karlstrom Outstanding Educator Award (2003)
Robert Schreiber ACM Distinguished Member (2006)
Horst D Simon ACM Gordon Bell Prize
Special Category (2009) ACM Gordon Bell Prize
Special Category (2009)
Barry Smith SIAM/ACM Prize in Computational Science and Engineering (2014)
James E. Smith ACM-IEEE CS Eckert-Mauchly Award (1999)
Kris Stewart ACM Senior Member (2015)
Nathan Tallent ACM-IEEE CS George Michael Memorial HPC Fellowships (2009)
Robert E Tarjan ACM Paris Kanellakis Theory and Practice Award (1999)
ACM A. M. Turing Award (1986)
Jeffrey S Vetter ACM Distinguished Member (2012)
ACM Gordon Bell Prize
Performance (2010)
Robert A. Walker Outstanding Contribution to ACM Award (2007)
ACM Distinguished Member (2006)
Bruce W Weide ACM Senior Member (2009)
Kesheng Wu ACM Distinguished Member (2010)
ACM Senior Member (2007)
Andrew C Yao ACM A. M. Turing Award (2000)
Hong Zhang SIAM/ACM Prize in Computational Science and Engineering (2014)
Benjamin G Zorn ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Iain Duff 28
Robert Renka 22
Fred Krogh 20
Robert Van De Geijn 19
Jennifer Scott 18
John Reid 17
Jack Dongarra 16
Bo Kågström 15
Timothy Davis 15
Donald Amos 14
Lawrence Shampine 14
Wayne Enright 14
Richard Hanson 13
Jorge Moré 12
John Rice 12
Layne Watson 12
James Demmel 12
Burton Garbow 12
Joseph Liu 11
Fred Gustavson 11
William Cody 10
Timothy Hopkins 9
Walter Gautschi 9
Elias Houstis 9
Jeff Cash 9
Linda Kaufman 9
Xiaoyesherry Li 9
Amparo Gil 8
Javier Segura 8
John Pryce 8
Patrick Keast 8
Nicholas Higham 8
Field Van Zee 8
Nico Temme 8
Ping Tang 8
Terje Espelid 7
Michael Heroux 7
Ronald Boisvert 7
Gregorio Quintana-Ortí 7
Hiroshi Akima 7
Ralph Kearfott 7
Enrique Quintana-Ortí 7
John Lewis 7
A Buckley 7
Almerico Murli 7
Thomas Hull 6
Albrecht Preusser 6
Roger Grimes 6
James Lyness 6
Richard Bartels 6
Robert Kirby 6
David Gay 6
Pierre L'Ecuyer 6
Anders Logg 6
Robert Ward 6
Sven Hammarling 6
Ian Gladwell 6
Nicholas Gould 6
Ronald Cools 6
Panos Pardalos 6
Gilbert Stewart 6
Annie Cuyt 6
Geoffrey Hill 5
Robert Schnabel 5
Tamar Schlick 5
Thomas Coleman 5
Patrick Gaffney 5
Jerzy Waśniewski 5
Philippe Toint 5
Paul Muir 5
Kendall Atkinson 5
Graeme Fairweather 5
Tony Chan 5
Van Van Snyder 5
Jarle Berntsen 5
Sivan Toledo 5
Richard Sincovec 5
Alexander Morgan 5
Krzysztof Sikorski 5
Anil Rao 5
William Mitchell 5
Yu Kuznetsov 5
Paolo Bientinesi 5
Mauricio Resende 5
Jeremy Du Croz 5
Paolo Toth 4
Ian Robinson 4
Wayne Dyksen 4
David Dodson 4
Daniel Kressner 4
Alan George 4
Valerio Parisi 4
Francesco Zirilli 4
William Hager 4
Jean Muller 4
Christof Vömel 4
Richard Lehoucq 4
Paul Bailey 4
Uwe Naumann 4
Norman Schryer 4
Naren Ramakrishnan 4
Christian Bischof 4
Werner Rheinboldt 4
Jan Verwer 4
Calvin Ribbens 4
John Monahan 4
Zhaojun Bai 4
Daniel Lozier 4
Lothar Reichel 4
Charles Fulton 4
Osni Marques 4
Jorge Nocedal 4
David Kahaner 4
Ahmed Sameh 4
Filippo Aluffi-Pentini 4
Michael Saunders 4
Carl De Boor 4
Joke Blom 4
Michael Patterson 4
Beresford Parlett 4
David Smith 4
Tamara Kolda 4
Richard Brent 4
Luisa D'Amore 3
Frank Stenger 3
Granville Sewell 3
Phyllis Fox 3
Fayez Alhargan 3
Kenneth Hillstrom 3
Andrew Conn 3
John Gunnels 3
Margaret Wright 3
Steven Benson 3
Charles Gear 3
Silvano Martello 3
Richard Simard 3
Luís Vicente 3
Shaun Forth 3
Norman Gibbs 3
Alan Genz 3
Willy Govaerts 3
Wolfgang Hörmann 3
Jon Bentley 3
Tobin Driscoll 3
Sanjiva Weerawarana 3
David Kincaid 3
Yifan Hu 3
Thomas Aird 3
Ron Dembo 3
Francesco Romani 3
Alfred Morris 3
Armido Didonato 3
Alan Jennings 3
Jonathan Hogg 3
R Brankin 3
William Brown 3
Philip Sharp 3
Joachim Ahrens 3
Paola Favati 3
Juan Meza 3
William Kahan 3
Marzio Sala 3
David Bailey 3
Maria Sosonkina 3
Desmond Higham 3
Bennett Fox 3
Aaron Fogelson 3
Webb Miller 3
Mariarosaria Rizzardi 3
David Shanno 3
Peter Benner 3
Yozo Hida 3
Martin Berzins 3
Anton Zettl 3
Kenneth Neves 3
Richard Fateman 3
Stan Cabay 3
Anshul Gupta 3
Ian Barrodale 3
Ulrich Dieter 3
William Symes 3
Alan Laub 3
Giorgio Carpaneto 3
Christoph Lauter 3
Steven Pruess 3
Roscoe Bartlett 3
Julien Langou 3
Tzemeng Low 3
Salvatore Filippone 3
Arnold Neumaier 3
Grazia Lotti 3
Bruce Schmeiser 3
Chao Yang 3
Andrew Sommese 3
Lars Karlsson 3
John Rice 3
Ron Brown 3
Danny Sorensen 3
Robert Granat 3
Kristján Jónasson 3
Bruno Lang 3
Niel Madsen 3
Toyat Cheung 3
Stavros Zenios 3
Giulio Giunta 3
Masao Kodama 3
Kanghoh Phua 3
J Diaz 3
Garth Wells 3
Eric Grosse 3
Michael Berry 3
David Stoutemyer 3
Wolfgang Bangerth 3
Donald Ramirez 2
Greg Henry 2
Robert Russell 2
J Christiansen 2
Leslie Foster 2
Jan Olszewski 2
Jeffrey Vitter 2
Per Hansen 2
Piotr Luszczek 2
Sunyoung Kim 2
Nikos Chrisochoides 2
Richard Weiss 2
Yves Nievergelt 2
S Barry 2
James Willenbring 2
Mauro Dell'Amico 2
Mohamed Kamel 2
Theodore Papatheodorou 2
J Smith 2
Bengt Fornberg 2
Vincent Lefèvre 2
Catherine Houstis 2
Victor Eijkhout 2
Zvi Drezner 2
Robert Skeel 2
Lois McInnes 2
Sebastian Ferrando 2
Francisco Igual 2
Tapio Schneider 2
Michael Mascagni 2
Jian He 2
Jean Utke 2
Roy Welsch 2
Arunachalam Ravindran 2
Kenneth Jackson 2
Mofreh Zaghloul 2
Malcolm Pike 2
Alexandru Nicolau 2
Wilfried Gansterer 2
Mark Gockenbach 2
Paul Bratley 2
Michael Jenkins 2
Frederick Fritsch 2
Masakazu Kojima 2
José Morales 2
Michel Cosnard 2
H Knoble 2
James Ball 2
Nelson Beebe 2
Joaquim Martins 2
George Miminis 2
I Hill 2
Thomas Foley 2
George Davis 2
Duncan, Lawrie 2
Christopher Darby 2
Ilyssa Sanders 2
Geoffrey Huntington 2
Michele Goano 2
Voratas Kachitvichyanukul 2
Jean L'excellent 2
Joaquim Júdice 2
Alfredo Buttari 2
David Jeffrey 2
P Culligan-Hensley 2
Christopher Paige 2
Spencer Shellman 2
L Stoltz 2
Charles Duris 2
Dennis Snow 2
Charles Van Loan 2
Per Thomsen 2
David Melgaard 2
Jorge Leis 2
Roger Abernathy 2
David Spooner 2
Guangning Tan 2
Meiyue Shao 2
Kevin Broughan 2
Allan Macleod 2
Brigitte Verdonk 2
Philip Gill 2
Harvey Greenberg 2
Klaus Schittkowski 2
John Roulier 2
Stephen Peters 2
U Pape 2
John Jezioranski 2
Daniel Kressner 2
Nathan Bell 2
Rida Farouki 2
Joseph Flaherty 2
Bryan Marker 2
Ann Catlin 2
Xiaobai Sun 2
Hans Langtangen 2
Richard Byrd 2
J Yohe 2
Walter Murray 2
Ladislav Lukšan 2
Masha Sosonkina 2
David McAllister 2
George Hall 2
John Mulvey 2
Dirk Laurie 2
Ali Bouaricha 2
Cleve Moler 2
Charles Dunkl 2
Markus Neher 2
Walter Anderson 2
Jean Dumas 2
John Butcher 2
Roy Wampler 2
Paul Zimmermann 2
Martin Alnaes 2
Michael Vrahatis 2
David Eyre 2
Timo Betcke 2
T Lam 2
William Eddy 2
Randall Shirts 2
Brian Gunter 2
Luc Giraud 2
Bjarne Andersen 2
Matthew Weinstein 2
Geza Schrauf 2
Marco Marletta 2
Josef Leydold 2
Jan Vlček 2
Michael Liepelt 2
Frédéric Cazals 2
John Betts 2
Joris Van Deun 2
James Amato 2
Charles Dunham 2
Uri Ascher 2
Franklin Luk 2
Giuliano Laccetti 2
Linwang Wang 2
Christian Reinsch 2
Keith Geddes 2
Jason Riedy 2
Paul Stockmeyer 2
Angelo Corana 2
Gregory Ammar 2
Hatem Ltaief 2
Kenneth Johnson 2
Dominique Orban 2
D Taylor 2
Mark Kramer 2
Robert Smith 2
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Jaroslav Kautsky 1
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S Daniel 1
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Peter Poromaa 1
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Paul Wang 1
S Polak 1
P Beck 1
Mary Pasternak 1
W Gentleman 1
Yinyu Ye 1
Florent de Dinechin 1
R Whaley 1
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M Ratner 1
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Suh Kang 1
Sonil Mukherjee 1
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P Beck 1
F Lichtenberger 1
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A McKenney 1
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K Helland 1
S Chan 1
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Koen Engelborghs 1
Gary Krenz 1
Martin Kronbichler 1
Serkan Gugercin 1
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A Lenir 1
Peter Kaps 1
C Praagman 1
Francois Durdin 1
H Wang 1
Jason Zwolak 1
HaiJun Su 1
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Zhonggang Zeng 1
Achim Schweikard 1
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Shuo Chen 1
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S Wong 1
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M Fisher 1
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Yi Mei 1

Affiliation Paper Counts
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Rutherford Appleton Laboratory 48
Purdue University 59
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Sandia National Laboratories, New Mexico 72
Argonne National Laboratory 83

ACM Transactions on Mathematical Software (TOMS)
Archive


2016
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2005
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2004
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2003
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2002
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2001
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2000
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1999
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1998
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1997
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1996
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1995
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1994
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1993
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1992
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1991
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1990
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1989
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1988
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1987
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1986
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1985
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1984
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1983
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1982
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1981
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1980
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1979
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1978
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1977
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1976
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1975
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