Netlib and NA-Net: Building a Scientific Computing Community Dongarra
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Gene H. Golub 1932–2007
Gene H. Golub 1932–2007 A Biographical Memoir by Dianne P. O’Leary ©2018 National Academy of Sciences. Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. GENE HOWARD GOLUB February 29, 1932–November 16, 2007 Elected to the NAS, 1993 Gene Howard Golub was the most influential person of his generation in the field of numerical analysis, in partic- ular, in the area of matrix computation. This was a conse- quence not just of his extraordinary technical contribu- tions but was also due to his clear writing, his influential treatise on matrix computation, his mentorship of a host of students and collaborators, his service as founder and editor of two journals, his leadership in the Society for Industrial and Applied Mathematics (SIAM), his efforts to bring colleagues together in meetings and electronic forums, and his role as a research catalyst. By Dianne P. O’Leary Biography Gene was born in Chicago, Illinois, on Leap Day, February 29, 1932, to Bernice Gelman Golub (an immigrant to the U.S. from Latvia) and Nathan Golub (from Ukraine). Gene’s father worked as a “bread man” and his mother was a seamstress, and Gene was proud of his Jewish heritage. From the age of 12 Gene worked, first at his cousin’s pharmacy and later at other jobs. After junior college and a year at the University of Chicago, he transferred to the University of Illinois in Urbana-Champaign, where he received his BS in Mathematics (1953), MA in Mathematical Statistics (1954), and PhD in Mathematics (1959). -
Computational Economics and Econometrics
Computational Economics and Econometrics A. Ronald Gallant Penn State University c 2015 A. Ronald Gallant Course Website http://www.aronaldg.org/courses/compecon Go to website and discuss • Preassignment • Course plan (briefly now, in more detail a few slides later) • Source code • libscl • Lectures • Homework • Projects Course Objective – Intro Introduce modern methods of computation and numerical anal- ysis to enable students to solve computationally intensive prob- lems in economics, econometrics, and finance. The key concepts to be mastered are the following: • The object oriented programming style. • The use of standard data structures. • Implementation and use of a matrix class. • Implementation and use of numerical algorithms. • Practical applications. • Parallel processing. Details follow Object Oriented Programming Object oriented programming is a style of programming devel- oped to support modern computing projects. Much of the devel- opment was in the commercial sector. The features of interest to us are the following: • The computer code closely resembles the way we think about a problem. • The computer code is compartmentalized into objects that perform clearly specified tasks. Importantly, this allows one to work on one part of the code without having to remember how the other parts work: se- lective ignorance • One can use inheritance and virtual functions both to describe the project design as interfaces to its objects and to permit polymorphism. Inter- faces cause the compiler to enforce our design, relieving us of the chore. Polymorphism allows us to easily swap in and out objects so we can try different models, different algorithms, etc. • The structures used to implement objects are much more flexible than the minimalist types of non-object oriented language structures such as subroutines, functions, and static common storage. -
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. -
Over-Scalapack.Pdf
1 2 The Situation: Parallel scienti c applications are typically Overview of ScaLAPACK written from scratch, or manually adapted from sequen- tial programs Jack Dongarra using simple SPMD mo del in Fortran or C Computer Science Department UniversityofTennessee with explicit message passing primitives and Mathematical Sciences Section Which means Oak Ridge National Lab oratory similar comp onents are co ded over and over again, co des are dicult to develop and maintain http://w ww .ne tl ib. org /ut k/p e opl e/J ackDo ngarra.html debugging particularly unpleasant... dicult to reuse comp onents not really designed for long-term software solution 3 4 What should math software lo ok like? The NetSolve Client Many more p ossibilities than shared memory Virtual Software Library Multiplicityofinterfaces ! Ease of use Possible approaches { Minimum change to status quo Programming interfaces Message passing and ob ject oriented { Reusable templates { requires sophisticated user Cinterface FORTRAN interface { Problem solving environments Integrated systems a la Matlab, Mathematica use with heterogeneous platform Interactiveinterfaces { Computational server, like netlib/xnetlib f2c Non-graphic interfaces Some users want high p erf., access to details {MATLAB interface Others sacri ce sp eed to hide details { UNIX-Shell interface Not enough p enetration of libraries into hardest appli- Graphic interfaces cations on vector machines { TK/TCL interface Need to lo ok at applications and rethink mathematical software { HotJava Browser -
Prospectus for the Next LAPACK and Scalapack Libraries
Prospectus for the Next LAPACK and ScaLAPACK Libraries James Demmel1, Jack Dongarra23, Beresford Parlett1, William Kahan1, Ming Gu1, David Bindel1, Yozo Hida1, Xiaoye Li1, Osni Marques1, E. Jason Riedy1, Christof Voemel1, Julien Langou2, Piotr Luszczek2, Jakub Kurzak2, Alfredo Buttari2, Julie Langou2, Stanimire Tomov2 1 University of California, Berkeley CA 94720, USA, 2 University of Tennessee, Knoxville TN 37996, USA, 3 Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA, 1 Introduction Dense linear algebra (DLA) forms the core of many scienti¯c computing appli- cations. Consequently, there is continuous interest and demand for the devel- opment of increasingly better algorithms in the ¯eld. Here 'better' has a broad meaning, and includes improved reliability, accuracy, robustness, ease of use, and most importantly new or improved algorithms that would more e±ciently use the available computational resources to speed up the computation. The rapidly evolving high end computing systems and the close dependence of DLA algo- rithms on the computational environment is what makes the ¯eld particularly dynamic. A typical example of the importance and impact of this dependence is the development of LAPACK [1] (and later ScaLAPACK [2]) as a successor to the well known and formerly widely used LINPACK [3] and EISPACK [3] libraries. Both LINPACK and EISPACK were based, and their e±ciency depended, on optimized Level 1 BLAS [4]. Hardware development trends though, and in par- ticular an increasing Processor-to-Memory speed gap of approximately 50% per year, started to increasingly show the ine±ciency of Level 1 BLAS vs Level 2 and 3 BLAS, which prompted e®orts to reorganize DLA algorithms to use block matrix operations in their innermost loops. -
History of MATLAB and the Company Mathworks
Engineering Problem Solving and Programming (CS 1133) History of MATLAB and the Company MathWorks K. Ming Leung [email protected] http://cis.poly.edu/˜mleung Department of Computer Science and Engineering Polytechnic Institute of NYU CS 1133: History, MATLAB, MathWorks – p. 1/1 Material presented here was taken with slight modifications from The Origin of MATLAB and The Growth of MATLAB and The MathW at the MathWorks website. In the late 1970s, Cleve Moler, then a professor at the University of New Mexico, used a computer language called Fortran to develop the first version of MATLAB. His goal was to provide to students the ability to utilize powerful numerical packages for scientific and engineering computations without having to write FORTRAN code. He visited Stanford University to spend his sabbatical in 1979 and taught the graduate numerical analysis course where the students used MATLAB for some of their homeworks. Half of the students in the class were from mathematics and com- puter science, and the other half were from engineering. Al- though the students in mathematics and computer science didn’t particularly like MATLAB, the engineering students were im- pressed. The emphasis on matrices (arrays) in MATLAB proved to be very useful to them. A few of the Stanford engineering students from his class later joined two consulting companies in Palo Alto, California. These companies extended MATLAB to have more capability in control analysis and signal processing. Jack Little, a Stanford- and MIT-trained control engineer, was the principal developer of one of the first commercial prod- ucts based on Fortran MATLAB. -
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. -
Totalview: Debugging from Desktop to Supercomputer
ATPESC 2017 TotalView: Debugging from Desktop to Supercomputer Peter Thompson Principal Software Support Engineer August 8, 2017 © 2017 Rogue Wave Software, Inc. All Rights Reserved. 1 Some thoughts on debugging • As soon as we started programming, we found out to our surprise that it wasn’t as easy to get programs right as we had thought. Debugging had to be discovered. I can remember the exact instant when I realized that a large part of my life from then on was going to be spent in finding mistakes in my own programs. – Maurice Wilkes • Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it. – Brian W. Kernigan • Sometimes it pays to stay in bed on Monday, rather than spending the rest of the week debugging Monday’s code. – Dan Saloman © 2017 Rogue Wave Software, Inc. All Rights Reserved. 2 Rogue Wave’s Debugging Tool TotalView for HPC • Source code debugger for C/C++/Fortran – Visibility into applications – Control over applications • Scalability • Usability • Support for HPC platforms and languages © 2017 Rogue Wave Software, Inc. All Rights Reserved. 3 TotalView Overview © 2017 Rogue Wave Software, Inc. All Rights Reserved. 4 TotalView Origins Mid-1980’s Bolt, Berenak, and Newman (BBN) Butterfly Machine An early ‘Massively Parallel’ computer © 2017 Rogue Wave Software, Inc. All Rights Reserved. 5 How do you debug a Butterfly? • TotalView project was developed as a solution for this environment – Able to debug multiple processes and threads – Point and click interface – Multiple and Mixed Language Support • Core development group has been there from the beginning and have been/are involved in defining MPI interfaces, DWARF, and lately OMPD (Open MP debugging interface) © 2017 Rogue Wave Software, Inc. -
C++ API for BLAS and LAPACK
2 C++ API for BLAS and LAPACK Mark Gates ICL1 Piotr Luszczek ICL Ahmad Abdelfattah ICL Jakub Kurzak ICL Jack Dongarra ICL Konstantin Arturov Intel2 Cris Cecka NVIDIA3 Chip Freitag AMD4 1Innovative Computing Laboratory 2Intel Corporation 3NVIDIA Corporation 4Advanced Micro Devices, Inc. February 21, 2018 This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two U.S. Department of Energy organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, in support of the nation's exascale computing imperative. Revision Notes 06-2017 first publication 02-2018 • copy editing, • new cover. 03-2018 • adding a section about GPU support, • adding Ahmad Abdelfattah as an author. @techreport{gates2017cpp, author={Gates, Mark and Luszczek, Piotr and Abdelfattah, Ahmad and Kurzak, Jakub and Dongarra, Jack and Arturov, Konstantin and Cecka, Cris and Freitag, Chip}, title={{SLATE} Working Note 2: C++ {API} for {BLAS} and {LAPACK}}, institution={Innovative Computing Laboratory, University of Tennessee}, year={2017}, month={June}, number={ICL-UT-17-03}, note={revision 03-2018} } i Contents 1 Introduction and Motivation1 2 Standards and Trends2 2.1 Programming Language Fortran............................ 2 2.1.1 FORTRAN 77.................................. 2 2.1.2 BLAST XBLAS................................. 4 2.1.3 Fortran 90.................................... 4 2.2 Programming Language C................................ 5 2.2.1 Netlib CBLAS.................................. 5 2.2.2 Intel MKL CBLAS................................ 6 2.2.3 Netlib lapack cwrapper............................. 6 2.2.4 LAPACKE.................................... 7 2.2.5 Next-Generation BLAS: \BLAS G2"..................... -
IMSL C Function Catalog 2020.0
IMSL® C Numerical Library Function Catalog Version 2020.0 1 At the heart of the IMSL C Numerical Library is a comprehensive set of pre-built mathematical and statistical analysis functions that developers can embed directly into their applications. Available for a wide range of computing platforms, the robust, scalable, portable and high performing IMSL analytics allow developers to focus on their domain of expertise and reduce development time. 2 IMSL C Numerical Library v2020.0 Function Catalog COST-EFFECTIVENESS AND VALUE including transforms, convolutions, splines, The IMSL C Numerical Library significantly shortens quadrature, and more. application time to market and promotes standardization. Descriptive function names and EMBEDDABILITY variable argument lists have been implemented to Development is made easier because library code simplify calling sequences. Using the IMSL C Library readily embeds into application code, with no reduces costs associated with the design, additional infrastructure such as app/management development, documentation, testing and consoles, servers, or programming environments maintenance of applications. Robust, scalable, needed. portable, and high performing analytics with IMSL Wrappers complicate development by requiring the forms the foundation for inherently reliable developer to access external compilers and pass applications. arrays or user-defined data types to ensure compatibility between the different languages. The A RICH SET OF PREDICTIVE ANALYTICS IMSL C Library allows developers to write, build, FUNCTIONS compile and debug code in a single environment, and The library includes a comprehensive set of functions to easily embed analytic functions in applications and for machine learning, data mining, prediction, and databases. classification, including: Time series models such as ARIMA, RELIABILITY GARCH, and VARMA vector auto- 100% pure C code maximizes robustness. -
A Peer-To-Peer Framework for Message Passing Parallel Programs
A Peer-to-Peer Framework for Message Passing Parallel Programs Stéphane GENAUD a;1, and Choopan RATTANAPOKA b;2 a AlGorille Team - LORIA b King Mongkut’s University of Technology Abstract. This chapter describes the P2P-MPI project, a software framework aimed at the development of message-passing programs in large scale distributed net- works of computers. Our goal is to provide a light-weight, self-contained software package that requires minimum effort to use and maintain. P2P-MPI relies on three features to reach this goal: i) its installation and use does not require administra- tor privileges, ii) available resources are discovered and selected for a computation without intervention from the user, iii) program executions can be fault-tolerant on user demand, in a completely transparent fashion (no checkpoint server to config- ure). P2P-MPI is typically suited for organizations having spare individual comput- ers linked by a high speed network, possibly running heterogeneous operating sys- tems, and having Java applications. From a technical point of view, the framework has three layers: an infrastructure management layer at the bottom, a middleware layer containing the services, and the communication layer implementing an MPJ (Message Passing for Java) communication library at the top. We explain the de- sign and the implementation of these layers, and we discuss the allocation strategy based on network locality to the submitter. Allocation experiments of more than five hundreds peers are presented to validate the implementation. We also present how fault-management issues have been tackled. First, the monitoring of the infras- tructure itself is implemented through the use of failure detectors. -
Small-Signal Device and Circuit Simulation
Die approbierte Originalversion dieser Dissertation ist an der Hauptbibliothek der Technischen Universität Wien aufgestellt (http://www.ub.tuwien.ac.at). The approved original version of this thesis is available at the main library of the Vienna University of Technology (http://www.ub.tuwien.ac.at/englweb/). DISSERTATION Small-Signal Device and Circuit Simulation ausgeführt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Wissenschaften eingereicht an der Technischen Universität Wien Fakultät für Elektrotechnik und Informationstechnik von STEPHAN WAGNER Gauermanngasse 3 A-3003 Gablitz, Österreich Matr. Nr. 9525168 I geboren am 15. Oktober 1976 in Wien Wien, im April 2005 Kurzfassung Die Entwicklung der letzten Jahrzehnte hat gezeigt, dass die numerische Bauelementsimulation einen wichtigen Beitrag zur Charakterisierung von Halbleiterstrukturen leistet. Der wirtschaftliche Vorteil des Einsatzes von modernen Simulationsprogrammen ist enorm, da Resultate von aufwendigen und teuren Ex- perimenten durch numerische Berechnungen vorausgesagt werden können. Darüberhinaus können diese Resultate für die Optimierung der Strukturen herangezogen werden, wodurch nur die vielversprechend- sten Varianten hinsichtlich zuvor bestimmter Kenngrößen tatsächlich hergestellt werden brauchen. Neben diesen Vorteilen führte auch die breite Verfügbarkeit und großzügigere Speicherausstattung eines durch- schnittlichen Computers heutzutage zu einer stark vermehrten Anwendung dieser Simulationsprogramme. Um die Voraussetzungen für die