A Coarray Fortran Implementation to Support Data-Intensive Application Development
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Introduction to Programming in Fortran 77 for Students of Science and Engineering
Introduction to programming in Fortran 77 for students of Science and Engineering Roman GrÄoger University of Pennsylvania, Department of Materials Science and Engineering 3231 Walnut Street, O±ce #215, Philadelphia, PA 19104 Revision 1.2 (September 27, 2004) 1 Introduction Fortran (FORmula TRANslation) is a programming language designed speci¯cally for scientists and engineers. For the past 30 years Fortran has been used for such projects as the design of bridges and aeroplane structures, it is used for factory automation control, for storm drainage design, analysis of scienti¯c data and so on. Throughout the life of this language, groups of users have written libraries of useful standard Fortran programs. These programs can be borrowed and used by other people who wish to take advantage of the expertise and experience of the authors, in a similar way in which a book is borrowed from a library. Fortran belongs to a class of higher-level programming languages in which the programs are not written directly in the machine code but instead in an arti¯cal, human-readable language. This source code consists of algorithms built using a set of standard constructions, each consisting of a series of commands which de¯ne the elementary operations with your data. In other words, any algorithm is a cookbook which speci¯es input ingredients, operations with them and with other data and ¯nally returns one or more results, depending on the function of this algorithm. Any source code has to be compiled in order to obtain an executable code which can be run on your computer. -
Writing Fast Fortran Routines for Python
Writing fast Fortran routines for Python Table of contents Table of contents ............................................................................................................................ 1 Overview ......................................................................................................................................... 2 Installation ...................................................................................................................................... 2 Basic Fortran programming ............................................................................................................ 3 A Fortran case study ....................................................................................................................... 8 Maximizing computational efficiency in Fortran code ................................................................. 12 Multiple functions in each Fortran file ......................................................................................... 14 Compiling and debugging ............................................................................................................ 15 Preparing code for f2py ................................................................................................................ 16 Running f2py ................................................................................................................................. 17 Help with f2py .............................................................................................................................. -
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. -
Evaluation of the Coarray Fortran Programming Model on the Example of a Lattice Boltzmann Code
Evaluation of the Coarray Fortran Programming Model on the Example of a Lattice Boltzmann Code Klaus Sembritzki Georg Hager Bettina Krammer Friedrich-Alexander University Friedrich-Alexander University Université de Versailles Erlangen-Nuremberg Erlangen-Nuremberg St-Quentin-en-Yvelines Erlangen Regional Computing Erlangen Regional Computing Exascale Computing Center (RRZE) Center (RRZE) Research (ECR) Martensstrasse 1 Martensstrasse 1 45 Avenue des Etats-Unis 91058 Erlangen, Germany 91058 Erlangen, Germany 78000 Versailles, France [email protected] [email protected] [email protected] Jan Treibig Gerhard Wellein Friedrich-Alexander University Friedrich-Alexander University Erlangen-Nuremberg Erlangen-Nuremberg Erlangen Regional Computing Erlangen Regional Computing Center (RRZE) Center (RRZE) Martensstrasse 1 Martensstrasse 1 91058 Erlangen, Germany 91058 Erlangen, Germany [email protected] [email protected] ABSTRACT easier to learn. The Lattice Boltzmann method is an explicit time-stepping sche- With the Fortran 2008 standard, coarrays became a native Fort- me for the numerical simulation of fluids. In recent years it has ran feature [2]. The “hybrid” nature of modern massively parallel gained popularity since it is straightforward to parallelize and well systems, which comprise shared-memory nodes built from multico- suited for modeling complex boundary conditions and multiphase re processor chips, can be addressed by the compiler and runtime flows. Starting from an MPI/OpenMP-parallel 3D prototype im- system by using shared memory for intra-node access to codimen- plementation of the algorithm in Fortran90, we construct several sions. Inter-node access may either by conducted using an existing coarray-based versions and compare their performance and requi- messaging layer such as MPI or SHMEM, or carried out natively red programming effort to the original code, demonstrating the per- using the primitives provided by the network infrastructure. -
A Comparison of Coarray Fortran, Unified Parallel C and T
Technical Report RAL-TR-2005-015 8 December 2005 Novel Parallel Languages for Scientific Computing ± a comparison of Co-Array Fortran, Unified Parallel C and Titanium J.V.Ashby Computational Science and Engineering Department CCLRC Rutherford Appleton Laboratory [email protected] Abstract With the increased use of parallel computers programming languages need to evolve to support parallelism. Three languages built on existing popular languages, are considered: Co-array Fortran, Universal Parallel C and Titanium. The features of each language which support paralleism are explained, a simple example is programmed and the current status and availability of the languages is reviewed. Finally we also review work on the performance of algorithms programmed in these languages. In most cases they perform favourably with the same algorithms programmed using message passing due to the much lower latency in their communication model. 1 1. Introduction As the use of parallel computers becomes increasingly prevalent there is an increased need for languages and programming methodologies which support parallelism. Ideally one would like an automatic parallelising compiler which could analyse a program written sequentially, identify opportunities for parallelisation and implement them without user knowledge or intervention. Attempts in the past to produce such compilers have largely failed (though automatic vectorising compilers, which may be regarded as one specific form of parallelism, have been successful, and the techniques are now a standard part of most optimising compilers for pipelined processors). As a result, interest in tools to support parallel programming has largely concentrated on two main areas: directive driven additions to existing languages and data-passing libraries. -
Cafe: Coarray Fortran Extensions for Heterogeneous Computing
2016 IEEE International Parallel and Distributed Processing Symposium Workshops CAFe: Coarray Fortran Extensions for Heterogeneous Computing Craig Rasmussen∗, Matthew Sottile‡, Soren Rasmussen∗ Dan Nagle† and William Dumas∗ ∗ University of Oregon, Eugene, Oregon †NCAR, Boulder, Colorado ‡Galois, Portland, Oregon Abstract—Emerging hybrid accelerator architectures are often simple tuning if the architecture is fundamentally different than proposed for inclusion as components in an exascale machine, previous ones. not only for performance reasons but also to reduce total power A number of options are available to an HPC programmer consumption. Unfortunately, programmers of these architectures face a daunting and steep learning curve that frequently requires beyond just using a serial language with MPI or OpenMP. learning a new language (e.g., OpenCL) or adopting a new New parallel languages have been under development for programming model. Furthermore, the distributed (and fre- over a decade, for example, Chapel and the PGAS languages quently multi-level) nature of the memory organization of clusters including Coarray Fortran (CAF). Options for heterogeneous of these machines provides an additional level of complexity. computing include usage of OpenACC, CUDA, CUDA For- This paper presents preliminary work examining how Fortran coarray syntax can be extended to provide simpler access to tran, and OpenCL for programming attached accelerator de- accelerator architectures. This programming model integrates the vices. Each of these choices provides the programmer with Partitioned Global Address Space (PGAS) features of Fortran abstractions over parallel systems that expose different levels with some of the more task-oriented constructs in OpenMP of detail about the specific systems to compile to, and as 4.0 and OpenACC. -
NUG Single Node Optimization Presentation
Single Node Optimization on Hopper Michael Stewart, NERSC Introduction ● Why are there so many compilers available on Hopper? ● Strengths and weaknesses of each compiler. ● Advice on choosing the most appropriate compiler for your work. ● Comparative benchmark results. ● How to compile and run with OpenMP for each compiler. ● Recommendations for running hybrid MPI/OpenMP codes on a node. Why So Many Compilers on Hopper? ● Franklin was delivered with the only commercially available compiler for Cray Opteron systems, PGI. ● GNU compilers were on Franklin, but at that time GNU Fortran optimization was poor. ● Next came Pathscale because of superior optimization. ● Cray was finally legally allowed to port their compiler to the Opteron so it was added next. ● Intel was popular on Carver, and it produced highly optimized codes on Hopper. ● PGI is still the default, but this is not a NERSC recommendation. Cray's current default is the Cray compiler, but we kept PGI to avoid disruption. PGI ● Strengths ○ Available on a wide variety of platforms making codes very portable. ○ Because of its wide usage, it is likely to compile almost any valid code cleanly. ● Weaknesses ○ Does not optimize as well as compilers more narrowly targeted to AMD architectures. ● Optimization recommendation: ○ -fast Cray ● Strengths ○ Fortran is well optimized for the Hopper architecture. ○ Uses Cray math libraries for optimization. ○ Well supported. ● Weaknesses ○ Compilations can take much longer than with other compilers. ○ Not very good optimization of C++ codes. ● Optimization recommendations: ○ Compile with no explicit optimization arguments. The default level of optimization is very high. Intel ● Strengths ○ Optimizes C++ and Fortran codes very well. -
Coarray Fortran Runtime Implementation in Openuh
COARRAY FORTRAN RUNTIME IMPLEMENTATION IN OPENUH A Thesis Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements for the Degree Master of Science By Debjyoti Majumder December 2011 COARRAY FORTRAN RUNTIME IMPLEMENTATION IN OPENUH Debjyoti Majumder APPROVED: Dr. Barbara Chapman, Chairman Dept. of Computer Science Dr. Jaspal Subhlok Dept. of Computer Science Dr. Terrence Liao TOTAL E&P Research & Technology USA, LLC Dean, College of Natural Sciences and Mathematics ii Acknowledgements I would like to express my gratitude to Dr. Barbara Chapman for providing financial support and for creating an excellent environment for research. I would like to thank my mentor Deepak Eachempati. Without his guidance, this work would not have been possible. I sincerely thank TOTAL S.A. for funding this project and Dr. Terrence Liao for his valuable inputs and help in performing experiments on TOTAL's hardware. I also thank the interns at TOTAL, Asma Farjallah, and France Boillod-Cerneux for the applications that I used for performance evaluation. I thank Hyoungjoon Jun for his earlier work on the CAF runtime. I thank Tony Curtis for his suggestions and excellent work on maintaining the infrastructure on which I ran my experiments. I thank Dr.Edgar Gabriel for answering all my questions and his help with using the shark cluster. I also thank the Berkeley Lab and PNNL's ARMCI developers for their guidance in using GASNet and ARMCI. Lastly, I would like to thank the Department of Computer Science for giving me the opportunity to be in this great country of USA and all my lab-mates and friends. -
PHYS-4007/5007: Computational Physics Course Lecture Notes Section II
PHYS-4007/5007: Computational Physics Course Lecture Notes Section II Dr. Donald G. Luttermoser East Tennessee State University Version 7.1 Abstract These class notes are designed for use of the instructor and students of the course PHYS-4007/5007: Computational Physics taught by Dr. Donald Luttermoser at East Tennessee State University. II. Choosing a Programming Language A. Which is the Best Programming Language for Your Work? 1. You have come up with a scientific idea which will require nu- merical work using a computer. One needs to ask oneself, which programming language will work best for the project? a) Projects involving data reduction and analysis typically need software with graphics capabilities. Examples of such graphics languages include IDL, Mathlab, Origin, GNU- plot, SuperMongo, and Mathematica. b) Projects involving a lot of ‘number-crunching’ typically require a programming language that is capable of car- rying out math functions in scientific notation with as much precision as possible. In physics and astronomy, the most commonly used number-crunching programming languages include the various flavors of Fortran, C, and more recently, Python. i) As noted in the last section, the decades old For- tran 77 is still widely use in physics and astronomy. ii) Over the past few years, Python has been growing in popularity in scientific programming. c) In this class, we will focus on two programming languages: Fortran and Python. From time to time we will discuss the IDL programming language since some of you may encounter this software during your graduate and profes- sional career. d) Any coding introduced in these notes will be written in either of these three programming languages. -
Using GNU Fortran 95
Contributed by Steven Bosscher ([email protected]). Using GNU Fortran 95 Steven Bosscher For the 4.0.4 Version* Published by the Free Software Foundation 59 Temple Place - Suite 330 Boston, MA 02111-1307, USA Copyright c 1999-2005 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with the Invariant Sections being “GNU General Public License” and “Funding Free Software”, the Front-Cover texts being (a) (see below), and with the Back-Cover Texts being (b) (see below). A copy of the license is included in the section entitled “GNU Free Documentation License”. (a) The FSF’s Front-Cover Text is: A GNU Manual (b) The FSF’s Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Short Contents Introduction ...................................... 1 GNU GENERAL PUBLIC LICENSE .................... 3 GNU Free Documentation License ....................... 9 Funding Free Software .............................. 17 1 Getting Started................................ 19 2 GFORTRAN and GCC .......................... 21 3 GFORTRAN and G77 ........................... 23 4 GNU Fortran 95 Command Options.................. 25 5 Project Status ................................ 33 6 Extensions ................................... 37 7 Intrinsic Procedures ............................ -
Parallel Computing
Parallel Computing Benson Muite [email protected] http://math.ut.ee/˜benson https://courses.cs.ut.ee/2014/paralleel/fall/Main/HomePage 6 October 2014 Parallel Programming Models and Parallel Programming Implementations Parallel Programming Models • Shared Memory • Distributed Memory • Simple computation and communication cost models Parallel Programming Implementations • OpenMP • MPI • OpenCL – C programming like language that aims to be manufacturer independent and allow for wide range of multithreaded devices • CUDA (Compute Unified Device Architecture) – Used for Nvidia GPUs, based on C/C++, makes numerical computations easier • Pthreads – older threaded programming interface, some legacy codes still around, lower level than OpenMP but not as easy to use • Universal Parallel C (UPC) – New generation parallel programming language, distributed array support, consider trying it • Coarray Fortran (CAF) – New generation parallel programming language, distributed array support, consider trying it • Julia (http://julialang.org/) – New serial and parallel programming language, consider trying it • Parallel Virtual Machine (PVM) – Older, has been replaced by MPI • OpenACC – brief mention • Not covered: Java threads, C++11 threads, Python Threads, High Performance Fortran (HPF) Parallel Programming Models: Flynn’s Taxonomy • (Single Instruction Single Data) – Serial • Single Program Multiple Data • Multiple Program Multiple Data • Multiple Instruction Single Data • Master Worker • Server Client Parallel Programming Models • loop parallelizm; -
Advanced Fortran @
Sami Ilvonen Peter Råback Advanced Fortran Programming March 26-28, 2019 CSC – IT Center for Science Ltd, Finland type revector(rk) integer, kind :: rk real(kind=rk), allocatable :: data(:) contains procedure :: sum => vecsum generic :: operator(+) => sum end type revector type, extends(revector) :: imvector real(kind=rk), allocatable :: imdata(:) contains procedure :: sum => imvecsum end type imvector contains function vecsum(x,y) result(z) implicit none class(revector(dp)), intent(in) :: x,y class(revector(dp)), allocatable :: z integer :: i select type(y) All material (C) 2013-2019 by CSC – IT Center for Science Ltd. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License, http://creativecommons.org/licenses/by-nc-sa/3.0/ Agenda Tuesday Wednesday 9:00-9:15 Course introduction 9:00-9:45 Introduction to Fortran coarrays 9:15-10:00 Useful new features beyond F95 9:45-10:00 Coffee break 10:00-10:15 Coffee break 10:00-11:15 Exercises 11:15-12:00 More coarray features 10:15-11:00 Advanced topics in Fortran I/O 12:00-13:00 Lunch break 11:00-11:15 Working with Fortran 13:00-14:00 Exercises compilers 14:00-14:45 Advanced topics in coarrays 11:15-12:00 Exercises 14:45-15:00 Coffee break 12:00-13:00 Lunch break 15:00-16:00 Exercises 13:00-14:00 Interoperability with C 14:00-14:45 Exercises 14:45-15:00 Coffee break 15:00-16:00 Exercises Thursday 9:00-10:00 Additional capabilities of Fortran types, procedure pointers 10:00-10:15 Coffee break 10:15-11:00 Exercises 11:00-12:00 Type extensions, type-bound