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A Source Transformation via Operator Overloading Method for the Automatic Differentiation of Mathematical Functions in MATLAB

A source transformation via operator overloading method is presented for computing derivatives of... (more)

What are the Correct Results for the Special Values of the Operands of the Power Operation?

Language standards such as C99 and C11, as well as the IEEE Standard for Floating-Point Arithmetic 754 (IEEE Std 754-2008) specify the expected... (more)

Algorithm 958

We introduce a new software tool and library named Lattice Builder, written in C++, that implements a variety of construction algorithms for good rank-1 lattice rules. It supports exhaustive and random searches, as well as component-by-component (CBC) and random CBC constructions, for any number of points, and for various measures of... (more)

Algorithm 959

VBF is a collection of C++ classes designed for analyzing vector Boolean functions (functions that map a Boolean vector to another Boolean vector) from a cryptographic perspective. This implementation uses the NTL library from Victor Shoup, adding new modules that call NTL functions and complement the existing ones, making it better suited to... (more)

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The ACM TOMS Replicated Computational Results (RCR) Initiative.

ACM TOMS has introduced a new initiative to optionally review the computationals results of a TOMS submission. This new effort is intended to assist in improving the quality of scientific publication for TOMS and for the computational science community as a whole. Manuscripts that successfully complete the RCR Review process receive the RCR designation when published. If you are interested in participating in this initiative, either as an author or reviewer, please contact the TOMS Editor-in-Chief. Details of the TOMS RCR Initiative are available here.


New options for ACM authors to manage rights and permissions for their work

ACM introduces a new publishing license agreement, an updated copyright transfer agreement, and a new author-pays option which allows for perpetual open access through the ACM Digital Library. For more information, visit the ACM Author Rights webpage.

 

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.

An experimental exploration of Marsaglia's xorshift generators, scrambled

The BLIS Framework: Experiments in Portability

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.

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 $k<m$

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 considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable representations (HSS). Such matrices appear in many applications, e.g., finite element methods, boundary element methods, etc. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The \emph{compression} algorithm that we use, that computes the HSS form of an input dense matrix, relies on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. This work is part of a more global effort, the STRUMPACK (STRUctured Matrices PACKage) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.

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).

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 Toolbox (LTFAT). It provides implementations of various generalizations of the well-known Mallat's algorithm (iterated filterbank) such that completely general filterbank trees, dual-tree complex wavelet transforms and wavelet packets can be created. The resulting transforms can be equivalently represented as filterbanks and analyzed as filterbank frames using fast algorithms.

Parallel Memory Efficient Adaptive Mesh Refinement for Porous Media Flow and Tsunami Simulation

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.

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.

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''.

Competitive Divide-and-Conquer Algorithm for Unconstrained Large Scale Black-Box Optimization

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.

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 equations with varying coefficients. Since the number of systems is large enough to offer considerable parallelism on many-core systems, the choice between different tridiag- onal solution algorithms, such as Thomas, CR (Cyclic Reduction) or PCR (Parallel Cyclic Reduction) needs to be re-examined. This work investigates the optimal choice of tridiagonal algorithm for CPUs and GPUs with a focus on minimizing the amount of data transfer to and from the main memory, and maximizing the achieved bandwidth. It also considers block tridiagonal solutions which are sometimes required in CFD (Computational Fluid Dynamic) applications.

Algorithm xxx: An efficient algorithm to compute the genus of discrete surfaces and applications to turbulent flows

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 defined in a structured-collocated grid, as those obtained by thresholding a scalar field, and does not require any triangulation of the data. This makes the algorithm fast, memory-efficient and suitable for large datasets. Applications to the characterization of complex surfaces in turbulent flows are presented to illustrate the method.

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

Matslise 2.0: a Matlab toolbox for Sturm-Liouville computations

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: Estimation of Stochastic Covariance Models using a Continuum of Moment Conditions.

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

Matrix multiplication over word-size modular fields using approximate formulae

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 1443
Citation Count 12912
Available for Download 1443
Downloads (6 weeks) 5082
Downloads (12 Months) 47208
Downloads (cumulative) 970232
Average downloads per article 672
Average citations per article 9
First Name Last Name Award
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
Burton Garbow 12
James Demmel 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
Nicholas Higham 8
Nico Temme 8
Ping Tang 8
Amparo Gil 8
Javier Segura 8
John Pryce 8
Patrick Keast 8
Terje Espelid 7
Michael Heroux 7
Ronald Boisvert 7
Field Van Zee 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
James Lyness 6
Roger Grimes 6
Richard Bartels 6
Robert Kirby 6
David Gay 6
Sven Hammarling 6
Anders Logg 6
Robert Ward 6
Ian Gladwell 6
Nicholas Gould 6
Ronald Cools 6
Panos Pardalos 6
Gilbert Stewart 6
Annie Cuyt 6
Robert Schnabel 5
Geoffrey Hill 5
Tamar Schlick 5
Thomas Coleman 5
Patrick Gaffney 5
Jerzy Waśniewski 5
Philippe Toint 5
Kendall Atkinson 5
Graeme Fairweather 5
Tony Chan 5
Van Van Snyder 5
Richard Sincovec 5
Jarle Berntsen 5
Sivan Toledo 5
Pierre L'Ecuyer 5
Alexander Morgan 5
Krzysztof Sikorski 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
Paul Muir 4
Valerio Parisi 4
Francesco Zirilli 4
William Hager 4
Jean Muller 4
Richard Lehoucq 4
Christof Vömel 4
Paul Bailey 4
Uwe Naumann 4
Naren Ramakrishnan 4
Norman Schryer 4
Werner Rheinboldt 4
Christian Bischof 4
Jan Verwer 4
Calvin Ribbens 4
John Monahan 4
Zhaojun Bai 4
Daniel Lozier 4
Anil Rao 4
Lothar Reichel 4
Jorge Nocedal 4
Charles Fulton 4
David Kahaner 4
Ahmed Sameh 4
Osni Marques 4
Michael Saunders 4
Joke Blom 4
Filippo Aluffi-Pentini 4
Carl De Boor 4
Michael Patterson 4
Beresford Parlett 4
David Smith 4
Tamara Kolda 4
Richard Brent 4
Fayez Alhargan 3
Kenneth Hillstrom 3
Steven Benson 3
Andrew Conn 3
John Gunnels 3
Margaret Wright 3
Silvano Martello 3
Charles Gear 3
Richard Simard 3
Willy Govaerts 3
Wolfgang Hörmann 3
Shaun Forth 3
Norman Gibbs 3
Alan Genz 3
David Kincaid 3
Thomas Aird 3
Jon Bentley 3
Tobin Driscoll 3
Sanjiva Weerawarana 3
Ron Dembo 3
Yifan Hu 3
Francesco Romani 3
Alan Jennings 3
Alfred Morris 3
Armido Didonato 3
William Brown 3
Jonathan Hogg 3
R Brankin 3
Philip Sharp 3
Joachim Ahrens 3
Paola Favati 3
Juan Meza 3
William Kahan 3
Marzio Sala 3
Desmond Higham 3
David Bailey 3
Bennett Fox 3
Maria Sosonkina 3
Aaron Fogelson 3
Webb Miller 3
David Shanno 3
Mariarosaria Rizzardi 3
Martin Berzins 3
Yozo Hida 3
Kenneth Neves 3
Anton Zettl 3
Stan Cabay 3
Richard Fateman 3
Anshul Gupta 3
Ulrich Dieter 3
Ian Barrodale 3
William Symes 3
Alan Laub 3
Giorgio Carpaneto 3
Christoph Lauter 3
Julien Langou 3
Arnold Neumaier 3
Steven Pruess 3
Roscoe Bartlett 3
Salvatore Filippone 3
Grazia Lotti 3
Bruce Schmeiser 3
Lars Karlsson 3
Chao Yang 3
Andrew Sommese 3
Danny Sorensen 3
John Rice 3
Ron Brown 3
Robert Granat 3
Niel Madsen 3
Kristján Jónasson 3
Bruno Lang 3
Toyat Cheung 3
Stavros Zenios 3
Giulio Giunta 3
Masao Kodama 3
Kanghoh Phua 3
J Diaz 3
Garth Wells 3
Michael Berry 3
David Stoutemyer 3
Eric Grosse 3
Wolfgang Bangerth 3
Luisa D'Amore 3
Frank Stenger 3
Granville Sewell 3
Phyllis Fox 3
Paul Bratley 2
Michael Jenkins 2
Masakazu Kojima 2
Peter Hanson 2
Karla Hoffman 2
Douglas Shier 2
Wlodzimierz Proskurowski 2
Pamela Zave 2
Charles Lawson 2
Michael McClellan 2
Ron Sacks-Davis 2
Antoine Petitet 2
Kendall Stanley 2
Alan Karp 2
Virginia Klema 2
Lennart Johnsson 2
Françoise Tisseur 2
George Corliss 2
M Weston 2
Patrick Amestoy 2
David Benson 2
Jörg Peters 2
Barry Brown 2
K Hiebert 2
João Soares 2
Peter Benner 2
Michel Daydé 2
D Barry 2
Isak Jonsson 2
Mohamed el Lozy 2
Wolfgang Janko 2
John Mcnamee 2
Louis Rall 2
Zvonimir Bujanović 2
Frank Olver 2
John Gilbert 2
P Prince 2
H Bücker 2
David Silvester 2
Klaus Schittkowski 2
Stephen Peters 2
U Pape 2
Gopal Gupta 2
John Jezioranski 2
A Abrham 2
Daniel Kressner 2
Nathan Bell 2
Rida Farouki 2
J Yohe 2
Joseph Flaherty 2
Ann Catlin 2
Hans Langtangen 2
Walter Murray 2
Xiaobai Sun 2
David McAllister 2
John Mulvey 2
Ladislav Lukšan 2
Masha Sosonkina 2
Richard Byrd 2
George Hall 2
Dirk Laurie 2
Ali Bouaricha 2
Charles Dunkl 2
Donald Ramirez 2
Cleve Moler 2
Greg Henry 2
Robert Russell 2
J Christiansen 2
Jeffrey Vitter 2
Per Hansen 2
Sunyoung Kim 2
Jan Olszewski 2
Leslie Foster 2
Piotr Luszczek 2
S Barry 2
James Willenbring 2
Nikos Chrisochoides 2
Yves Nievergelt 2
Richard Weiss 2
Mauro Dell'Amico 2
Mohamed Kamel 2
Theodore Papatheodorou 2
Vincent Lefèvre 2
Catherine Houstis 2
J Smith 2
Bengt Fornberg 2
Victor Eijkhout 2
Robert Skeel 2
Tapio Schneider 2
Zvi Drezner 2
Lois McInnes 2
Sebastian Ferrando 2
Kenneth Jackson 2
Malcolm Pike 2
Jian He 2
Jean Utke 2
Michael Mascagni 2
Roy Welsch 2
Arunachalam Ravindran 2
Mark Gockenbach 2
Frederick Fritsch 2
Alexandru Nicolau 2
Wilfried Gansterer 2
Joel Tendler 2
Robert Smith 2
Jose Román 2
Mark Jones 2
Vivek Sarin 2
Dennis Verschaeren 2
Ove Skovgaard 2
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Milan Kubicek 1
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François Rouet 1
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Affiliation Paper Counts
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Numerical Algorithms Group 15
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IBM 17
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Boeing Corporation 18
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IBM Thomas J. Watson Research Center 19
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University of Illinois at Urbana-Champaign 20
Center for Mathematics and Computer Science - Amsterdam 20
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Lawrence Livermore National Laboratory 20
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RWTH Aachen University 21
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Lawrence Berkeley National Laboratory 30
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Rutherford Appleton Laboratory 48
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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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2006
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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
Volume 7 Issue 4, Dec. 1981
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1980
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1979
Volume 5 Issue 4, Dec. 1979
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1978
Volume 4 Issue 4, December 1978
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1977
Volume 3 Issue 4, Dec. 1977
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1976
Volume 2 Issue 4, Dec. 1976
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1975
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