Julia: a Fresh Approach to Numerical Computing∗
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IADS Group Uses Multiple MATLAB Licenses
Can We Migrate Our Analysis Routines to Python? Introduction • Can we migrate our analysis routines to Python? - MATLAB is powerful, but it’s expensive. - Capable open-source alternatives exist and are thriving. • Recent developments in scientific Python libraries have made migration from MATLAB to Python possible and attractive. • The IADS Group uses multiple MATLAB licenses. Dominance of MATLAB • MATLAB is the standard language for engineering analysis. • No need to be a programmer to solve engineering problems. • Used for collaboration and development of analysis routines. • MATLAB is required for study in an engineering curriculum - ECE 309 (CSUN), “Numerical Methods in Electrical Engineering”, is now taught using MATLAB. It was taught using Pascal in the 1980s… Dependence on MATLAB • IADS uses MATLAB to prototype new analysis routines. • IADS uses MATLAB to test and maintain data export and import. We need MATLAB! • IADS uses a set of MATLAB scripts to test Autospectrum and PSD results for Every Release. • IADS is dependent upon MATLAB. Problems with Dependency • Budget constraints mean fewer licenses and toolboxes are available. • MATLAB version changes force retest What happens of data interfaces. if they take MATLAB away • Retest requires an active license. from us? • Test Scripts are unusable without a license. • Having no backup plan in place is risky. Requirements for a Replacement • Should have broad industry acceptance. • Should have scientific libraries that mimic functionality that is commonly used in MATLAB by the flight test community. • Should have similar syntax. • Total MATLAB functionality is not necessary for our purposes, but it would be nice for going forward. • Should be relatively free of periodic licensing hassles. -
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. -
Python – an Introduction
Python { AN IntroduCtion . default parameters . self documenting code Example for extensions: Gnuplot Hans Fangohr, [email protected], CED Seminar 05/02/2004 ² The wc program in Python ² Summary Overview ² Outlook ² Why Python ² Literature: How to get started ² Interactive Python (IPython) ² M. Lutz & D. Ascher: Learning Python Installing extra modules ² ² ISBN: 1565924649 (1999) (new edition 2004, ISBN: Lists ² 0596002815). We point to this book (1999) where For-loops appropriate: Chapter 1 in LP ² ! if-then ² modules and name spaces Alex Martelli: Python in a Nutshell ² ² while ISBN: 0596001886 ² string handling ² ¯le-input, output Deitel & Deitel et al: Python { How to Program ² ² functions ISBN: 0130923613 ² Numerical computation ² some other features Other resources: ² . long numbers www.python.org provides extensive . exceptions ² documentation, tools and download. dictionaries Python { an introduction 1 Python { an introduction 2 Why Python? How to get started: The interpreter and how to run code Chapter 1, p3 in LP Chapter 1, p12 in LP ! Two options: ! All sorts of reasons ;-) interactive session ² ² . Object-oriented scripting language . start Python interpreter (python.exe, python, . power of high-level language double click on icon, . ) . portable, powerful, free . prompt appears (>>>) . mixable (glue together with C/C++, Fortran, . can enter commands (as on MATLAB prompt) . ) . easy to use (save time developing code) execute program ² . easy to learn . Either start interpreter and pass program name . (in-built complex numbers) as argument: python.exe myfirstprogram.py Today: . ² Or make python-program executable . easy to learn (Unix/Linux): . some interesting features of the language ./myfirstprogram.py . use as tool for small sysadmin/data . Note: python-programs tend to end with .py, processing/collecting tasks but this is not necessary. -
Julia, My New Friend for Computing and Optimization? Pierre Haessig, Lilian Besson
Julia, my new friend for computing and optimization? Pierre Haessig, Lilian Besson To cite this version: Pierre Haessig, Lilian Besson. Julia, my new friend for computing and optimization?. Master. France. 2018. cel-01830248 HAL Id: cel-01830248 https://hal.archives-ouvertes.fr/cel-01830248 Submitted on 4 Jul 2018 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. « Julia, my new computing friend? » | 14 June 2018, IETR@Vannes | By: L. Besson & P. Haessig 1 « Julia, my New frieNd for computiNg aNd optimizatioN? » Intro to the Julia programming language, for MATLAB users Date: 14th of June 2018 Who: Lilian Besson & Pierre Haessig (SCEE & AUT team @ IETR / CentraleSupélec campus Rennes) « Julia, my new computing friend? » | 14 June 2018, IETR@Vannes | By: L. Besson & P. Haessig 2 AgeNda for today [30 miN] 1. What is Julia? [5 miN] 2. ComparisoN with MATLAB [5 miN] 3. Two examples of problems solved Julia [5 miN] 4. LoNger ex. oN optimizatioN with JuMP [13miN] 5. LiNks for more iNformatioN ? [2 miN] « Julia, my new computing friend? » | 14 June 2018, IETR@Vannes | By: L. Besson & P. Haessig 3 1. What is Julia ? Open-source and free programming language (MIT license) Developed since 2012 (creators: MIT researchers) Growing popularity worldwide, in research, data science, finance etc… Multi-platform: Windows, Mac OS X, GNU/Linux.. -
Delivering a Professional ®
Delivering a professional ® We stand on the shoulders of giants (2015) Custodians of OpenFOAM® (2016) … www.esi-group.com 1 Copyright © ESICopyright Group, 2017. © ESI All Group, rights reserved.2017. All rights reserved. OpenFOAM – foreword Greetings from, and thanks to the Team • OpenCFD Core Development‣ Karen Kettle and Supporting Teams ‣ Takashi Minabe (Japan) ‣ Andrew Heather ‣ Mohsen Battoei (North America) ‣ Mattijs Janssens ‣ Ravi Ajjampudi (India) ‣ Sergio Ferraris ‣ Bjorn Landmann, Sebastien Vilfayeau (Germany) ‣ Mark Olesen ‣ Matej Forman (Training Coordinator) ‣ Prashant Sonakar ‣ Roger Almenar ‣ Pawan Ghildiyal ‣ Fred Mendonca OpenFOAM Operation www.esi-group.com 2 Copyright © ESI Group, 2017. All rights reserved. OpenCFD – Commitment to OpenFOAM Users Development and Release Schedule • OpenCFD owns the trademark • Releasing OpenFOAM since 2004 • Professional Six-monthly Development and Release cycle, including ‣ New developments ‣ Consolidated bug-fixes ‣ Overhaulled testing procedure for Quality Assurance ‣ Release and Development repositories in GitLab https://develop.openfoam.com ‣ Master branch ‣ Develop branch (includes > Master > Release ‣ Community Repositories > Develop www.esi-group.com 3 Copyright © ESI Group, 2017. All rights reserved. OpenCFD – Commitment to OpenFOAM Users Development and Release Schedule • OpenFOAM.com releases so far • OpenFOAM-v3.0+ on Jan 13th 2016 • OpenFOAM-v1606+ on June 30th 2016 • OpenFOAM-v1612+ on 23rd December 2016 • OpenFOAM-v1706 on 30th June 2017 www.esi-group.com 4 Copyright -
Ordinary Differential Equations (ODE) Using Euler's Technique And
Mathematical Models and Methods in Modern Science Ordinary Differential Equations (ODE) using Euler’s Technique and SCILAB Programming ZULZAMRI SALLEH Applied Sciences and Advance Technology Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Bandar Teknologi Maritim Jalan Pantai Remis 32200 Lumut, Perak MALAYSIA [email protected] http://www.mimet.edu.my Abstract: - Mathematics is very important for the engineering and scientist but to make understand the mathematics is very difficult if without proper tools and suitable measurement. A numerical method is one of the algorithms which involved with computer programming. In this paper, Scilab is used to carter the problems related the mathematical models such as Matrices, operation with ODE’s and solving the Integration. It remains true that solutions of the vast majority of first order initial value problems cannot be found by analytical means. Therefore, it is important to be able to approach the problem in other ways. Today there are numerous methods that produce numerical approximations to solutions of differential equations. Here, we introduce the oldest and simplest such method, originated by Euler about 1768. It is called the tangent line method or the Euler method. It uses a fixed step size h and generates the approximate solution. The purpose of this paper is to show the details of implementing of Euler’s method and made comparison between modify Euler’s and exact value by integration solution, as well as solve the ODE’s use built-in functions available in Scilab programming. Key-Words: - Numerical Methods, Scilab programming, Euler methods, Ordinary Differential Equation. 1 Introduction availability of digital computers has led to a Numerical methods are techniques by which veritable explosion in the use and development of mathematical problems are formulated so that they numerical methods [1]. -
Foundations of Computational Science August 29-30, 2019
Foundations of Computational Science August 29-30, 2019 Thursday, August 29 Time Speaker Title/Abstract 8:30 - 9:30am Breakfast 9:30 - 9:40am Opening Explainable AI and Multimedia Research Abstract: AI as a concept has been around since the 1950’s. With the recent advancements in machine learning algorithms, and the availability of big data and large computing resources, the scene is set for AI to be used in many more systems and applications which will profoundly impact society. The current deep learning based AI systems are mostly in black box form and are often non-explainable. Though it has high performance, it is also known to make occasional fatal mistakes. 9:30 - 10:05am Tat Seng Chua This has limited the applications of AI, especially in mission critical applications. In this talk, I will present the current state-of-the arts in explainable AI, which holds promise to helping humans better understand and interpret the decisions made by the black-box AI models. This is followed by our preliminary research on explainable recommendation, relation inference in videos, as well as leveraging prior domain knowledge, information theoretic principles, and adversarial algorithms to achieving explainable framework. I will also discuss future research towards quality, fairness and robustness of explainable AI. Group Photo and 10:05 - 10:35am Coffee Break Deep Learning-based Chinese Language Computation at Tsinghua University: 10:35 - 10:50am Maosong Sun Progress and Challenges 10:50 - 11:05am Minlie Huang Controllable Text Generation 11:05 - 11:20am Peng Cui Stable Learning: The Convergence of Causal Inference and Machine Learning 11:20 - 11:45am Yike Guo Data Efficiency in Machine Learning Deep Approximation via Deep Learning Abstract: The primary task of many applications is approximating/estimating a function through samples drawn from a probability distribution on the input space. -
Computational Science and Engineering
Computational Science and Engineering, PhD College of Engineering Graduate Coordinator: TBA Email: Phone: Department Chair: Marwan Bikdash Email: [email protected] Phone: 336-334-7437 The PhD in Computational Science and Engineering (CSE) is an interdisciplinary graduate program designed for students who seek to use advanced computational methods to solve large problems in diverse fields ranging from the basic sciences (physics, chemistry, mathematics, etc.) to sociology, biology, engineering, and economics. The mission of Computational Science and Engineering is to graduate professionals who (a) have expertise in developing novel computational methodologies and products, and/or (b) have extended their expertise in specific disciplines (in science, technology, engineering, and socioeconomics) with computational tools. The Ph.D. program is designed for students with graduate and undergraduate degrees in a variety of fields including engineering, chemistry, physics, mathematics, computer science, and economics who will be trained to develop problem-solving methodologies and computational tools for solving challenging problems. Research in Computational Science and Engineering includes: computational system theory, big data and computational statistics, high-performance computing and scientific visualization, multi-scale and multi-physics modeling, computational solid, fluid and nonlinear dynamics, computational geometry, fast and scalable algorithms, computational civil engineering, bioinformatics and computational biology, and computational physics. Additional Admission Requirements Master of Science or Engineering degree in Computational Science and Engineering (CSE) or in science, engineering, business, economics, technology or in a field allied to computational science or computational engineering field. GRE scores Program Outcomes: Students will demonstrate critical thinking and ability in conducting research in engineering, science and mathematics through computational modeling and simulations. -
Artificial Intelligence Research For
Charles Xie ([email protected]) is a senior scientist who works at the inter- section between computational science and learning science. Artificial Intelligence research for engineering design By Charles Xie Imagine a high school classroom in 2030. AI is one of the three most promising areas for which Bill Gates Students are challenged to design an said earlier this year he would drop out of college again if he were a young student today. The victory of Google’s AlphaGo against engineering system that meets the needs of a top human Go player in 2016 was a landmark in the history of their community. They work with advanced AI. The game of Go is considered a difficult challenge because computer-aided design (CAD) software that the number of legal positions on a 19×19 grid is estimated to be leverages the power of scientific simulation, 2×10170, far more than the total number of atoms in the observ- mixed reality, and cloud computing. Every able universe combined (1082). While AlphaGo may be only a cool tool needed to complete an engineering type of weak AI that plays Go at present, scientists believe that its development will engender technologies that eventually find design project is seamlessly integrated into applications in other fields. the CAD platform to streamline the learning process. But the most striking capability of Performance modeling the software is its ability to act like a mentor (e.g., simulation-based analysis) who has all the knowledge about the design project to answer questions, learns from all the successful or unsuccessful solutions ever Evaluator attempted by human designers or computer agents, adapts to the needs of each student based on experience interacting with all sorts of designers, inspires students to explore creative ideas, and, most importantly, remains User Ideator responsive all the time without ever running out of steam. -
Introduction to Ggplot2
Introduction to ggplot2 Dawn Koffman Office of Population Research Princeton University January 2014 1 Part 1: Concepts and Terminology 2 R Package: ggplot2 Used to produce statistical graphics, author = Hadley Wickham "attempt to take the good things about base and lattice graphics and improve on them with a strong, underlying model " based on The Grammar of Graphics by Leland Wilkinson, 2005 "... describes the meaning of what we do when we construct statistical graphics ... More than a taxonomy ... Computational system based on the underlying mathematics of representing statistical functions of data." - does not limit developer to a set of pre-specified graphics adds some concepts to grammar which allow it to work well with R 3 qplot() ggplot2 provides two ways to produce plot objects: qplot() # quick plot – not covered in this workshop uses some concepts of The Grammar of Graphics, but doesn’t provide full capability and designed to be very similar to plot() and simple to use may make it easy to produce basic graphs but may delay understanding philosophy of ggplot2 ggplot() # grammar of graphics plot – focus of this workshop provides fuller implementation of The Grammar of Graphics may have steeper learning curve but allows much more flexibility when building graphs 4 Grammar Defines Components of Graphics data: in ggplot2, data must be stored as an R data frame coordinate system: describes 2-D space that data is projected onto - for example, Cartesian coordinates, polar coordinates, map projections, ... geoms: describe type of geometric objects that represent data - for example, points, lines, polygons, ... aesthetics: describe visual characteristics that represent data - for example, position, size, color, shape, transparency, fill scales: for each aesthetic, describe how visual characteristic is converted to display values - for example, log scales, color scales, size scales, shape scales, .. -
Introduction to GNU Octave
Introduction to GNU Octave Hubert Selhofer, revised by Marcel Oliver updated to current Octave version by Thomas L. Scofield 2008/08/16 line 1 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 8 6 4 2 -8 -6 0 -4 -2 -2 0 -4 2 4 -6 6 8 -8 Contents 1 Basics 2 1.1 What is Octave? ........................... 2 1.2 Help! . 2 1.3 Input conventions . 3 1.4 Variables and standard operations . 3 2 Vector and matrix operations 4 2.1 Vectors . 4 2.2 Matrices . 4 1 2.3 Basic matrix arithmetic . 5 2.4 Element-wise operations . 5 2.5 Indexing and slicing . 6 2.6 Solving linear systems of equations . 7 2.7 Inverses, decompositions, eigenvalues . 7 2.8 Testing for zero elements . 8 3 Control structures 8 3.1 Functions . 8 3.2 Global variables . 9 3.3 Loops . 9 3.4 Branching . 9 3.5 Functions of functions . 10 3.6 Efficiency considerations . 10 3.7 Input and output . 11 4 Graphics 11 4.1 2D graphics . 11 4.2 3D graphics: . 12 4.3 Commands for 2D and 3D graphics . 13 5 Exercises 13 5.1 Linear algebra . 13 5.2 Timing . 14 5.3 Stability functions of BDF-integrators . 14 5.4 3D plot . 15 5.5 Hilbert matrix . 15 5.6 Least square fit of a straight line . 16 5.7 Trapezoidal rule . 16 1 Basics 1.1 What is Octave? Octave is an interactive programming language specifically suited for vectoriz- able numerical calculations. -
An Evaluation of Tensorflow As a Programming Framework for HPC Applications
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE An Evaluation of TensorFlow as a Programming Framework for HPC Applications WEI DER CHIEN Master in Computer Science Date: August 28, 2018 Supervisor: Stefano Markidis Examiner: Erwin Laure Swedish title: En undersökning av TensorFlow som ett utvecklingsramverk för högpresterande datorsystem School of Electrical Engineering and Computer Science iii Abstract In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and perfor- mance. At the core of these application is dense matrix-matrix multipli- cation. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabili- ties. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed.