Life As a Developer of Numerical Software
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Fortran Resources 1
Fortran Resources 1 Ian D Chivers Jane Sleightholme May 7, 2021 1The original basis for this document was Mike Metcalf’s Fortran Information File. The next input came from people on comp-fortran-90. Details of how to subscribe or browse this list can be found in this document. If you have any corrections, additions, suggestions etc to make please contact us and we will endeavor to include your comments in later versions. Thanks to all the people who have contributed. Revision history The most recent version can be found at https://www.fortranplus.co.uk/fortran-information/ and the files section of the comp-fortran-90 list. https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=comp-fortran-90 • May 2021. Major update to the Intel entry. Also changes to the editors and IDE section, the graphics section, and the parallel programming section. • October 2020. Added an entry for Nvidia to the compiler section. Nvidia has integrated the PGI compiler suite into their NVIDIA HPC SDK product. Nvidia are also contributing to the LLVM Flang project. Updated the ’Additional Compiler Information’ entry in the compiler section. The Polyhedron benchmarks discuss automatic parallelisation. The fortranplus entry covers the diagnostic capability of the Cray, gfortran, Intel, Nag, Oracle and Nvidia compilers. Updated one entry and removed three others from the software tools section. Added ’Fortran Discourse’ to the e-lists section. We have also made changes to the Latex style sheet. • September 2020. Added a computer arithmetic and IEEE formats section. • June 2020. Updated the compiler entry with details of standard conformance. -
Numerical and Parallel Libraries
Numerical and Parallel Libraries Uwe Küster University of Stuttgart High-Performance Computing-Center Stuttgart (HLRS) www.hlrs.de Uwe Küster Slide 1 Höchstleistungsrechenzentrum Stuttgart Numerical Libraries Public Domain commercial vendor specific Uwe Küster Slide 2 Höchstleistungsrechenzentrum Stuttgart 33. — Numerical and Parallel Libraries — 33. 33-1 Overview • numerical libraries for linear systems – dense – sparse •FFT • support for parallelization Uwe Küster Slide 3 Höchstleistungsrechenzentrum Stuttgart Public Domain Lapack-3 linear equations, eigenproblems BLAS fast linear kernels Linpack linear equations Eispack eigenproblems Slatec old library, large functionality Quadpack numerical quadrature Itpack sparse problems pim linear systems PETSc linear systems Netlib Server best server http://www.netlib.org/utk/papers/iterative-survey/packages.html Uwe Küster Slide 4 Höchstleistungsrechenzentrum Stuttgart 33. — Numerical and Parallel Libraries — 33. 33-2 netlib server for all public domain numerical programs and libraries http://www.netlib.org Uwe Küster Slide 5 Höchstleistungsrechenzentrum Stuttgart Contents of netlib access aicm alliant amos ampl anl-reports apollo atlas benchmark bib bibnet bihar blacs blas blast bmp c c++ cephes chammp cheney-kincaid clapack commercial confdb conformal contin control crc cumulvs ddsv dierckx diffpack domino eispack elefunt env f2c fdlibm fftpack fishpack fitpack floppy fmm fn fortran fortran-m fp gcv gmat gnu go graphics harwell hence hompack hpf hypercube ieeecss ijsa image intercom itpack -
Jack Dongarra: Supercomputing Expert and Mathematical Software Specialist
Biographies Jack Dongarra: Supercomputing Expert and Mathematical Software Specialist Thomas Haigh University of Wisconsin Editor: Thomas Haigh Jack J. Dongarra was born in Chicago in 1950 to a he applied for an internship at nearby Argonne National family of Sicilian immigrants. He remembers himself as Laboratory as part of a program that rewarded a small an undistinguished student during his time at a local group of undergraduates with college credit. Dongarra Catholic elementary school, burdened by undiagnosed credits his success here, against strong competition from dyslexia.Onlylater,inhighschool,didhebeginto students attending far more prestigious schools, to Leff’s connect material from science classes with his love of friendship with Jim Pool who was then associate director taking machines apart and tinkering with them. Inspired of the Applied Mathematics Division at Argonne.2 by his science teacher, he attended Chicago State University and majored in mathematics, thinking that EISPACK this would combine well with education courses to equip Dongarra was supervised by Brian Smith, a young him for a high school teaching career. The first person in researcher whose primary concern at the time was the his family to go to college, he lived at home and worked lab’s EISPACK project. Although budget cuts forced Pool to in a pizza restaurant to cover the cost of his education.1 make substantial layoffs during his time as acting In 1972, during his senior year, a series of chance director of the Applied Mathematics Division in 1970– events reshaped Dongarra’s planned career. On the 1971, he had made a special effort to find funds to suggestion of Harvey Leff, one of his physics professors, protect the project and hire Smith. -
Randnla: Randomized Numerical Linear Algebra
review articles DOI:10.1145/2842602 generation mechanisms—as an algo- Randomization offers new benefits rithmic or computational resource for the develop ment of improved algo- for large-scale linear algebra computations. rithms for fundamental matrix prob- lems such as matrix multiplication, BY PETROS DRINEAS AND MICHAEL W. MAHONEY least-squares (LS) approximation, low- rank matrix approxi mation, and Lapla- cian-based linear equ ation solvers. Randomized Numerical Linear Algebra (RandNLA) is an interdisci- RandNLA: plinary research area that exploits randomization as a computational resource to develop improved algo- rithms for large-scale linear algebra Randomized problems.32 From a foundational per- spective, RandNLA has its roots in theoretical computer science (TCS), with deep connections to mathemat- Numerical ics (convex analysis, probability theory, metric embedding theory) and applied mathematics (scientific computing, signal processing, numerical linear Linear algebra). From an applied perspec- tive, RandNLA is a vital new tool for machine learning, statistics, and data analysis. Well-engineered implemen- Algebra tations have already outperformed highly optimized software libraries for ubiquitous problems such as least- squares,4,35 with good scalability in par- allel and distributed environments. 52 Moreover, RandNLA promises a sound algorithmic and statistical foundation for modern large-scale data analysis. MATRICES ARE UBIQUITOUS in computer science, statistics, and applied mathematics. An m × n key insights matrix can encode information about m objects ˽ Randomization isn’t just used to model noise in data; it can be a powerful (each described by n features), or the behavior of a computational resource to develop discretized differential operator on a finite element algorithms with improved running times and stability properties as well as mesh; an n × n positive-definite matrix can encode algorithms that are more interpretable in the correlations between all pairs of n objects, or the downstream data science applications. -
HPC Libraries
High Performance Computing: Concepts, Methods & Means HPC Libraries Hartmut Kaiser PhD Center for Computation & Technology Louisiana State University April 19 th , 2007 Outline • Introduction to High Performance Libraries • Linear Algebra Libraries (BLAS, LAPACK) • PDE Solvers (PETSc) • Mesh manipulation and load balancing (METIS/ParMETIS, JOSTLE) • Special purpose libraries (FFTW) • General purpose libraries (C++: Boost) • Summary – Materials for test 2 Outline • Introduction to High Performance Libraries • Linear Algebra Libraries (BLAS, LAPACK) • PDE Solvers (PETSc) • Mesh manipulation and load balancing (METIS/ParMETIS, JOSTLE) • Special purpose libraries (FFTW) • General purpose libraries (C++: Boost) • Summary – Materials for test 3 Puzzle of the Day #include <stdio.h> int main() { int a = 10; switch (a) { case '1': printf("ONE\n"); break ; case '2': printf("TWO\n"); break ; defa1ut : printf("NONE\n"); } If you expect the output of the above return 0; } program to be NONE , I would request you to check it out! 4 Application domains • Linear algebra – BLAS, ATLAS, LAPACK, ScaLAPACK, Slatec, pim • Ordinary and partial Differential Equations – PETSc • Mesh manipulation and Load Balancing – METIS, ParMETIS, CHACO, JOSTLE, PARTY • Graph manipulation – Boost.Graph library • Vector/Signal/Image processing – VSIPL, PSSL. • General parallelization – MPI, pthreads • Other domain specific libraries – NAMD, NWChem, Fluent, Gaussian, LS-DYNA 5 Application Domain Overview • Linear Algebra Libraries – Provide optimized methods for constructing sets of linear equations, performing operations on them (matrix-matrix products, matrix-vector products) and solving them (factoring, forward & backward substitution. – Commonly used libraries include BLAS, ATLAS, LAPACK, ScaLAPACK, PaLAPACK • PDE Solvers: – Developing general-porpose, parallel numerical PDE libraries – Usual toolsets include manipulation of sparse data structures, iterative linear system solvers, preconditioners, nonlinear solvers and time-stepping methods. -
Evolving Software Repositories
1 Evolving Software Rep ositories http://www.netli b.org/utk/pro ject s/esr/ Jack Dongarra UniversityofTennessee and Oak Ridge National Lab oratory Ron Boisvert National Institute of Standards and Technology Eric Grosse AT&T Bell Lab oratories 2 Pro ject Fo cus Areas NHSE Overview Resource Cataloging and Distribution System RCDS Safe execution environments for mobile co de Application-l evel and content-oriented to ols Rep ository interop erabili ty Distributed, semantic-based searching 3 NHSE National HPCC Software Exchange NASA plus other agencies funded CRPC pro ject Center for ResearchonParallel Computation CRPC { Argonne National Lab oratory { California Institute of Technology { Rice University { Syracuse University { UniversityofTennessee Uniform interface to distributed HPCC software rep ositories Facilitation of cross-agency and interdisciplinary software reuse Material from ASTA, HPCS, and I ITA comp onents of the HPCC program http://www.netlib.org/nhse/ 4 Goals: Capture, preserve and makeavailable all software and software- related artifacts pro duced by the federal HPCC program. Soft- ware related artifacts include algorithms, sp eci cations, designs, do cumentation, rep ort, ... Promote formation, growth, and interop eration of discipline-oriented rep ositories that organize, evaluate, and add value to individual contributions. Employ and develop where necessary state-of-the-art technologies for assisting users in nding, understanding, and using HPCC software and technologies. 5 Bene ts: 1. Faster development of high-quality software so that scientists can sp end less time writing and debugging programs and more time on research problems. 2. Less duplication of software development e ort by sharing of soft- ware mo dules. -
2 Accessing Matlab at ER4
Department of Electrical Engineering EE281 Intro duction to MATLAB on the Region IV Computing Facilities 1 What is Matlab? Matlab is a high-p erformance interactive software package for scienti c and enginnering numeric computation. Matlab integrates numerical analysis, matrix computation, signal pro cessing, and graphics in an easy-to-use envi- ronment without traditional programming. The name Matlab stands for matrix laboratory. Matlab was originally written to provide easy access to the matrix software develop ed by the LIN- PACK and EISPACK pro jects. Matlab is an interactive system whose basic data element is a matrix that do es not require dimensioning. Furthermore, problem solutions are expressed in Matlab almost exactly as they are written mathematically. Matlab has evolved over the years with inputs from many users. Matlab Toolboxes are sp ecialized collections of Matlab les designed for solving particular classes of functions. Currently available to olb oxes include: signal pro cessing control system optimization neural networks system identi cation robust control analysis splines symb olic mathematics image pro cessing statistics 2 Accessing Matlab at ER4 Sit down at a workstation. Log on. login: smithj <press return> password: 1234js <press return> Use the mouse button to view the Ro ot Menu. Drag the cursor to \Design Tools" then to \Matlab." Release the mouse button. You are now running the Matlab interactive software package. The prompt is a double \greater than" sign. You may use emacs line editor commands and the arrow keys while typing in Matlab. 1 3 An Intro ductory Demonstration Execute the following command to view aquick intro duction to Matlab. -
CS 267 Dense Linear Algebra: History and Structure, Parallel Matrix
Quick review of earlier lecture •" What do you call •" A program written in PyGAS, a Global Address CS 267 Space language based on Python… Dense Linear Algebra: •" That uses a Monte Carlo simulation algorithm to approximate π … History and Structure, •" That has a race condition, so that it gives you a Parallel Matrix Multiplication" different funny answer every time you run it? James Demmel! Monte - π - thon ! www.cs.berkeley.edu/~demmel/cs267_Spr16! ! 02/25/2016! CS267 Lecture 12! 1! 02/25/2016! CS267 Lecture 12! 2! Outline Outline •" History and motivation •" History and motivation •" What is dense linear algebra? •" What is dense linear algebra? •" Why minimize communication? •" Why minimize communication? •" Lower bound on communication •" Lower bound on communication •" Parallel Matrix-matrix multiplication •" Parallel Matrix-matrix multiplication •" Attaining the lower bound •" Attaining the lower bound •" Other Parallel Algorithms (next lecture) •" Other Parallel Algorithms (next lecture) 02/25/2016! CS267 Lecture 12! 3! 02/25/2016! CS267 Lecture 12! 4! CS267 Lecture 2 1 Motifs What is dense linear algebra? •" Not just matmul! The Motifs (formerly “Dwarfs”) from •" Linear Systems: Ax=b “The Berkeley View” (Asanovic et al.) •" Least Squares: choose x to minimize ||Ax-b||2 Motifs form key computational patterns •" Overdetermined or underdetermined; Unconstrained, constrained, or weighted •" Eigenvalues and vectors of Symmetric Matrices •" Standard (Ax = λx), Generalized (Ax=λBx) •" Eigenvalues and vectors of Unsymmetric matrices -
Comparison of Numerical Methods and Open-Source Libraries for Eigenvalue Analysis of Large-Scale Power Systems
applied sciences Article Comparison of Numerical Methods and Open-Source Libraries for Eigenvalue Analysis of Large-Scale Power Systems Georgios Tzounas , Ioannis Dassios * , Muyang Liu and Federico Milano School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland; [email protected] (G.T.); [email protected] (M.L.); [email protected] (F.M.) * Correspondence: [email protected] Received: 30 September 2020; Accepted: 24 October 2020; Published: 28 October 2020 Abstract: This paper discusses the numerical solution of the generalized non-Hermitian eigenvalue problem. It provides a comprehensive comparison of existing algorithms, as well as of available free and open-source software tools, which are suitable for the solution of the eigenvalue problems that arise in the stability analysis of electric power systems. The paper focuses, in particular, on methods and software libraries that are able to handle the large-scale, non-symmetric matrices that arise in power system eigenvalue problems. These kinds of eigenvalue problems are particularly difficult for most numerical methods to handle. Thus, a review and fair comparison of existing algorithms and software tools is a valuable contribution for researchers and practitioners that are interested in power system dynamic analysis. The scalability and performance of the algorithms and libraries are duly discussed through case studies based on real-world electrical power networks. These are a model of the All-Island Irish Transmission System with 8640 variables; and, a model of the European Network of Transmission System Operators for Electricity, with 146,164 variables. Keywords: eigenvalue analysis; large non-Hermitian matrices; numerical methods; open-source libraries 1. -
Using MATLAB Version 5 How to Contact the Mathworks
MATLAB® The Language of Technical Computing Computation Visualization Programming Using MATLAB Version 5 How to Contact The MathWorks: 508-647-7000 Phone 508-647-7001 Fax The MathWorks, Inc. Mail 24 Prime Park Way Natick, MA 01760-1500 http://www.mathworks.com Web ftp.mathworks.com Anonymous FTP server comp.soft-sys.matlab Newsgroup [email protected] Technical support [email protected] Product enhancement suggestions [email protected] Bug reports [email protected] Documentation error reports [email protected] Subscribing user registration [email protected] Order status, license renewals, passcodes [email protected] Sales, pricing, and general information Using MATLAB COPYRIGHT 1984 - 1999 by The MathWorks, Inc. The software described in this document is furnished under a license agreement. The software may be used or copied only under the terms of the license agreement. No part of this manual may be photocopied or repro- duced in any form without prior written consent from The MathWorks, Inc. U.S. GOVERNMENT: If Licensee is acquiring the Programs on behalf of any unit or agency of the U.S. Government, the following shall apply: (a) For units of the Department of Defense: the Government shall have only the rights specified in the license under which the commercial computer software or commercial software documentation was obtained, as set forth in subparagraph (a) of the Rights in Commercial Computer Software or Commercial Software Documentation Clause at DFARS 227.7202-3, therefore the rights set forth herein shall apply; and (b) For any other unit or agency: NOTICE: Notwithstanding any other lease or license agreement that may pertain to, or accompany the delivery of, the computer software and accompanying documentation, the rights of the Government regarding its use, reproduction, and disclo- sure are as set forth in Clause 52.227-19 (c)(2) of the FAR. -
Squeezing the Most out of Eigenvalue Solvers on High-Performance Computers
Squeezing the Most out of Eigenvalue Solvers on High-Performance Computers Jack J. Dongarra* Mathematics and Computer Science Division Argonne National Laboratory Argonne, Illinois 60439 Linda Kaufman AT G T Bell Laboratories Murray Hill, New Jersey 07974 and Sven Hammarling Numerical Algorithms Group Ltd. NAG Central Office, Mayfield House 256 Banbuy Road, Oxford OX2 7DE, England Submitted by J. Alan George ABSTRACT This paper describes modifications to many of the standard algorithms used in computing eigenvalues and eigenvectors of matrices. These modifications can dramati- cally increase the performance of the underlying software on high-performance computers without resorting to assembler language, without significantly influencing the floating-point operation count, and without affecting the roundoff-error properties of the algorithms. The techniques are applied to a wide variety of algorithms and are beneficial in various architectural settings. INTRODUCTION On high-performance vector computers like the CRAY-1, CRAY X-MP, Fujitsu VP, Hitachi S-810, and Amdahl 1200, there are three basic performance levels- scalar, vector, and supervector. For example, on the CRAY-1 [5,7, lo], *Work supported in part by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109Eng.38. LINEAR ALGEBRA AND ITS APPLICATIONS 77:113-136 (1986) 113 00243795/86/$0.00 114 JACK J. DONGARRA ET AL. these levels produce the following execution rates: Rate of execution Performance level (MFLOPS)~ Scalp o-4 Vector 4-50 Super-vector 50-160 Scalar performance is obtained when no advantage is taken of the special features of the machine architecture. -
Solving Large Sparse Eigenvalue Problems on Supercomputers
CORE https://ntrs.nasa.gov/search.jsp?R=19890017052Metadata, citation 2020-03-20T01:26:29+00:00Z and similar papers at core.ac.uk Provided by NASA Technical Reports Server a -. Solving Large Sparse Eigenvalue Problems on Supercomputers Bernard Philippe Youcef Saad December, 1988 Research Institute for Advanced Computer Science NASA Ames Research Center RIACS Technical Report 88.38 NASA Cooperative Agreement Number NCC 2-387 {NBSA-CR-185421) SOLVING LARGE SPAESE N89 -26 4 23 EIG E N V ALUE PBOBLE PlS 0 N SO PE IiCOM PUT ERS (Research Inst, for A dvauced Computer Science) 21 p CSCL 09B Unclas G3/61 0217926 Research Institute for Advanced Computer Science Solving Large Sparse Eigenvalue Problems on Supercomputers Bernard Philippe* Youcef Saad Research Institute for Advanced Computer Science NASA Ames Research Center RIACS Technical Report 88.38 December, 1988 An important problem in scientific computing consists in finding a few eigenvalues and corresponding eigenvectors of a very large and sparse matrix. The most popular methods to solve these problems are based on projection techniques on appropriate subspaces. The main attraction of these methods is that they only require to use the mauix in the form of matrix by vector multiplications. We compare the implementations on supercomputers of two such methods for symmetric matrices, namely Lanczos' method and Davidson's method. Since one of the most important operations in these two methods is the multiplication of vectors by the sparse matrix, we fist discuss how to perform this operation efficiently. We then compare the advantages and the disadvantages of each method and discuss implementations aspects.