Introduction to Matlab
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Bring Down the Two-Language Barrier with Julia Language for Efficient
AbstractSDRs: Bring down the two-language barrier with Julia Language for efficient SDR prototyping Corentin Lavaud, Robin Gerzaguet, Matthieu Gautier, Olivier Berder To cite this version: Corentin Lavaud, Robin Gerzaguet, Matthieu Gautier, Olivier Berder. AbstractSDRs: Bring down the two-language barrier with Julia Language for efficient SDR prototyping. IEEE Embedded Systems Letters, Institute of Electrical and Electronics Engineers, 2021, pp.1-1. 10.1109/LES.2021.3054174. hal-03122623 HAL Id: hal-03122623 https://hal.archives-ouvertes.fr/hal-03122623 Submitted on 27 Jan 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. AbstractSDRs: Bring down the two-language barrier with Julia Language for efficient SDR prototyping Corentin LAVAUD∗, Robin GERZAGUET∗, Matthieu GAUTIER∗, Olivier BERDER∗. ∗ Univ Rennes, CNRS, IRISA, [email protected] Abstract—This paper proposes a new methodology based glue interface [8]. Regarding programming semantic, low- on the recently proposed Julia language for efficient Software level languages (e.g. C++/C, Rust) offers very good runtime Defined Radio (SDR) prototyping. SDRs are immensely popular performance but does not offer a good prototyping experience. as they allow to have a flexible approach for sounding, monitoring or processing radio signals through the use of generic analog High-level languages (e.g. -
Xcode Package from App Store
KH Computational Physics- 2016 Introduction Setting up your computing environment Installation • MAC or Linux are the preferred operating system in this course on scientific computing. • Windows can be used, but the most important programs must be installed – python : There is a nice package ”Enthought Python Distribution” http://www.enthought.com/products/edudownload.php – C++ and Fortran compiler – BLAS&LAPACK for linear algebra – plotting program such as gnuplot Kristjan Haule, 2016 –1– KH Computational Physics- 2016 Introduction Software for this course: Essentials: • Python, and its packages in particular numpy, scipy, matplotlib • C++ compiler such as gcc • Text editor for coding (for example Emacs, Aquamacs, Enthought’s IDLE) • make to execute makefiles Highly Recommended: • Fortran compiler, such as gfortran or intel fortran • BLAS& LAPACK library for linear algebra (most likely provided by vendor) • open mp enabled fortran and C++ compiler Useful: • gnuplot for fast plotting. • gsl (Gnu scientific library) for implementation of various scientific algorithms. Kristjan Haule, 2016 –2– KH Computational Physics- 2016 Introduction Installation on MAC • Install Xcode package from App Store. • Install ‘‘Command Line Tools’’ from Apple’s software site. For Mavericks and lafter, open Xcode program, and choose from the menu Xcode -> Open Developer Tool -> More Developer Tools... You will be linked to the Apple page that allows you to access downloads for Xcode. You wil have to register as a developer (free). Search for the Xcode Command Line Tools in the search box in the upper left. Download and install the correct version of the Command Line Tools, for example for OS ”El Capitan” and Xcode 7.2, Kristjan Haule, 2016 –3– KH Computational Physics- 2016 Introduction you need Command Line Tools OS X 10.11 for Xcode 7.2 Apple’s Xcode contains many libraries and compilers for Mac systems. -
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. -
SEISGAMA: a Free C# Based Seismic Data Processing Software Platform
Hindawi International Journal of Geophysics Volume 2018, Article ID 2913591, 8 pages https://doi.org/10.1155/2018/2913591 Research Article SEISGAMA: A Free C# Based Seismic Data Processing Software Platform Theodosius Marwan Irnaka, Wahyudi Wahyudi, Eddy Hartantyo, Adien Akhmad Mufaqih, Ade Anggraini, and Wiwit Suryanto Seismology Research Group, Physics Department, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara Bulaksumur, Yogyakarta 55281, Indonesia Correspondence should be addressed to Wiwit Suryanto; [email protected] Received 4 July 2017; Accepted 24 December 2017; Published 30 January 2018 Academic Editor: Filippos Vallianatos Copyright © 2018 Teodosius Marwan Irnaka et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Seismic refection is one of the most popular methods in geophysical prospecting. Nevertheless, obtaining high resolution and accurate results requires a sophisticated processing stage. Tere are many open-source seismic refection data processing sofware programs available; however, they ofen use a high-level programming language that decreases its overall performance, lacks intuitive user-interfaces, and is limited to a small set of tasks. Tese shortcomings reveal the need to develop new sofware using a programming language that is natively supported by Windows5 operating systems, which uses a relatively medium-level programming language (such as C#) and can be enhanced by an intuitive user interface. SEISGAMA was designed to address this need and employs a modular concept, where each processing group is combined into one module to ensure continuous and easy development and documentation. -
GNU Scientific Library – Reference Manual
GNU Scientific Library – Reference Manual: Top http://www.gnu.org/software/gsl/manual/html_node/ GNU Scientific Library – Reference Manual Next: Introduction, Previous: (dir), Up: (dir) [Index] GSL This file documents the GNU Scientific Library (GSL), a collection of numerical routines for scientific computing. It corresponds to release 1.16+ of the library. Please report any errors in this manual to [email protected]. More information about GSL can be found at the project homepage, http://www.gnu.org/software/gsl/. Printed copies of this manual can be purchased from Network Theory Ltd at http://www.network-theory.co.uk /gsl/manual/. The money raised from sales of the manual helps support the development of GSL. A Japanese translation of this manual is available from the GSL project homepage thanks to Daisuke Tominaga. Copyright © 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013 The GSL Team. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections and no cover texts. A copy of the license is included in the section entitled “GNU Free Documentation License”. • Introduction: • Using the library: • Error Handling: • Mathematical Functions: • Complex Numbers: • Polynomials: • Special Functions: • Vectors and Matrices: • Permutations: • Combinations: • Multisets: • Sorting: • BLAS Support: • Linear Algebra: • Eigensystems: -
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. -
Julia: a Modern Language for Modern ML
Julia: A modern language for modern ML Dr. Viral Shah and Dr. Simon Byrne www.juliacomputing.com What we do: Modernize Technical Computing Today’s technical computing landscape: • Develop new learning algorithms • Run them in parallel on large datasets • Leverage accelerators like GPUs, Xeon Phis • Embed into intelligent products “Business as usual” will simply not do! General Micro-benchmarks: Julia performs almost as fast as C • 10X faster than Python • 100X faster than R & MATLAB Performance benchmark relative to C. A value of 1 means as fast as C. Lower values are better. A real application: Gillespie simulations in systems biology 745x faster than R • Gillespie simulations are used in the field of drug discovery. • Also used for simulations of epidemiological models to study disease propagation • Julia package (Gillespie.jl) is the state of the art in Gillespie simulations • https://github.com/openjournals/joss- papers/blob/master/joss.00042/10.21105.joss.00042.pdf Implementation Time per simulation (ms) R (GillespieSSA) 894.25 R (handcoded) 1087.94 Rcpp (handcoded) 1.31 Julia (Gillespie.jl) 3.99 Julia (Gillespie.jl, passing object) 1.78 Julia (handcoded) 1.2 Those who convert ideas to products fastest will win Computer Quants develop Scientists prepare algorithms The last 25 years for production (Python, R, SAS, DEPLOY (C++, C#, Java) Matlab) Quants and Computer Compress the Scientists DEPLOY innovation cycle collaborate on one platform - JULIA with Julia Julia offers competitive advantages to its users Julia is poised to become one of the Thank you for Julia. Yo u ' v e k i n d l ed leading tools deployed by developers serious excitement. -
Quantian: a Scientific Computing Environment
New URL: http://www.R-project.org/conferences/DSC-2003/ Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003) March 20–22, Vienna, Austria ISSN 1609-395X Kurt Hornik, Friedrich Leisch & Achim Zeileis (eds.) http://www.ci.tuwien.ac.at/Conferences/DSC-2003/ Quantian: A Scientific Computing Environment Dirk Eddelbuettel [email protected] Abstract This paper introduces Quantian, a scientific computing environment. Quan- tian is directly bootable from cdrom, self-configuring without user interven- tion, and comprises hundreds of applications, including a significant number of programs that are of immediate interest to quantitative researchers. Quantian is available at http://dirk.eddelbuettel.com/quantian.html. 1 Introduction Quantian is a directly bootable and self-configuring Linux system on a single cdrom. Quantian is an extension of Knoppix (Knopper, 2003) from which it takes its base system of about 2.0 gigabytes of software, along with automatic hardware detec- tion and configuration. Quantian adds software with a quantitative, numerical or scientific focus such as R, Octave, Ginac, GSL, Maxima, OpenDX, Pari, PSPP, QuantLib, XLisp-Stat and Yorick. This paper is organized as follows. In the next section, we introduce the Debian distribution upon which Knopppix and, thus, Quantian are built, discuss Debian packages and its packaging system and provide an overview of the support for R and its related programs. We also describe the Knoppix system and provide a basic outline of Quantian. In the ensuing section, we describe the Quantian build process. Possible extensions are discussed in the following section before a short summary concludes the paper. 2 Background Quantian builds on Knoppix, which is itself based on Debian. -
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"..................... -
Capítulo 1 El Lenguaje De Programación Objectivec
www.gnustep.wordpress.com Introducción al entorno de desarrollo GNUstep 1 www.gnustep.wordpress.com Introducción al entorno de desarrollo GNUstep Licencia de este documento Copyright (C) 2008, 2009 German A. Arias. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". 2 www.gnustep.wordpress.com Introducción al entorno de desarrollo GNUstep Tabla de Contenidos INTRODUCCIÓN.....................................................................................................................................6 Capítulo 0...................................................................................................................................................7 Instalación de GNUstep y las especificaciones OpenStep.........................................................................7 0.1 Instalando GNUstep........................................................................................................................8 0.2 Especificaciones OpenStep...........................................................................................................12 0.3 Estableciendo las teclas modificadoras.........................................................................................13 Capítulo 1.................................................................................................................................................16 -
Praktikum Iz Softverskih Alata U Elektronici
PRAKTIKUM IZ SOFTVERSKIH ALATA U ELEKTRONICI 2017/2018 Predrag Pejović 31. decembar 2017 Linkovi na primere: I OS I LATEX 1 I LATEX 2 I LATEX 3 I GNU Octave I gnuplot I Maxima I Python 1 I Python 2 I PyLab I SymPy PRAKTIKUM IZ SOFTVERSKIH ALATA U ELEKTRONICI 2017 Lica (i ostali podaci o predmetu): I Predrag Pejović, [email protected], 102 levo, http://tnt.etf.rs/~peja I Strahinja Janković I sajt: http://tnt.etf.rs/~oe4sae I cilj: savladavanje niza programa koji se koriste za svakodnevne poslove u elektronici (i ne samo elektronici . ) I svi programi koji će biti obrađivani su slobodan softver (free software), legalno možete da ih koristite (i ne samo to) gde hoćete, kako hoćete, za šta hoćete, koliko hoćete, na kom računaru hoćete . I literatura . sve sa www, legalno, besplatno! I zašto svake godine (pomalo) updated slajdovi? Prezentacije predmeta I engleski I srpski, kraća verzija I engleski, prezentacija i animacije I srpski, prezentacija i animacije A šta se tačno radi u predmetu, koji programi? 1. uvod (upravo slušate): organizacija nastave + (FS: tehnička, ekonomska i pravna pitanja, kako to uopšte postoji?) (≈ 1 w) 2. operativni sistem (GNU/Linux, Ubuntu), komandna linija (!), shell scripts, . (≈ 1 w) 3. nastavak OS, snalaženje, neki IDE kao ilustracija i vežba, jedan Python i jedan C program . (≈ 1 w) 4.L ATEX i LATEX 2" (≈ 3 w) 5. XCircuit (≈ 1 w) 6. probni kolokvijum . (= 1 w) 7. prvi kolokvijum . 8. GNU Octave (≈ 1 w) 9. gnuplot (≈ (1 + ) w) 10. wxMaxima (≈ 1 w) 11. drugi kolokvijum . 12. Python, IPython, PyLab, SymPy (≈ 3 w) 13.