int a[100]; #pragma xmp nodes p[*] #pragma xmp template t[100] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i]
|
|
- Alexandra Lucy Banks
- 6 years ago
- Views:
Transcription
1
2 2
3 3
4 4
5 int a[100]; #pragma xmp nodes p[*] #pragma xmp template t[100] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i] integer :: a(100)!$xmp nodes p(*)!$xmp template t(100)!$xmp distribute t(block) onto p!$xmp align a(i) with t(i) #pragma xmp loop on t[i] reduction(+:res) for(int i=0;i<100;i++){ a[i] = i; res += a[i]; }!$xmp loop on t(i) reduction(+:res) do i=0, 100 a(i) = i res = res + a(i) end do 5
6 6
7 node1 node2 node3 Directive or Coarray 7
8 8
9 int a[100], b[100]; #pragma xmp nodes p[*] #pragma xmp template t[100] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i] integer :: a(100), b(100)!$xmp nodes p(*)!$xmp template t(100)!$xmp distribute t(block) onto p!$xmp align a(i) with t(i) 9
10 10
11 11
12 12
13 p(1) p(2) p(3) p(4) Directive or Coarray 13
14 14
15 alignment distribution 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 alignment distribution node, template, align. distribute directives are used 25
26 alignment distribution node, template, align. distribute directives are used 26
27 27
28 28
29 #pragma xmp loop on t(i) for(int i=0;i<16;i++){ a[16] #pragma xmp nodes p[4] #pragma xmp template t[16] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i] p[0] p[1] Execute do-loop in parallel with affinity to template p[2] p[3] 29
30 !$xmp loop on t(i) do i=1, 16 a(16) p(1) p(2) p(3)!$xmp nodes p(4)!$xmp template t(16)!$xmp distribute t(block) onto p!$xmp align a(i) with t(i) Execute do-loop in parallel with affinity to template p(4) 30
31 #pragma xmp loop on t(i) for(int i=2;i<11;i++){ a[16] p[0] p[1] p[2] #pragma xmp nodes p[4] #pragma xmp template t[16] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i] Execute do-loop in parallel with affinity to template p[3] 31
32 !$xmp loop on t(i) do i=3, 11 a(16) p(1) p(2) p(3)!$xmp nodes p(4)!$xmp template t(16)!$xmp distribute t(block) onto p!$xmp align a(i) with t(i) Execute do-loop in parallel with affinity to template p(4) 32
33 33
34 The variables s on all nodes are summed up and updated to the value when ending the loop-statement. 34
35 35
36 36
37 37 node-name base length node-name( base end )
38 38
39 39
40 40
41 41 #pragma xmp gmove a[2:4] = b[3:4]; array-name base length a[8] b[8] p[0] p[1] p[2] p[3]
42 42!$xmp gmove a(3:6) = b(4:7) array-name( base ) a(8) b(8) p(1) p(2) p(3) p(4)
43 43 #pragma xmp nodes p[3] #pragma xmp template t[9] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i] #pragma xmp shadow a[1:1]... #pragma xmp reflect (a)!$xmp nodes p(3)!$xmp template t(9)!$xmp distribute t(block) onto p!$xmp align a(i) with t(i)!$xmp shadow a(1:1)...!$xmp reflect (a) The shadow directive creates a shadow area (gray cell) at the upper and lower bounds of array a[].
44 44 #pragma xmp nodes p[3] #pragma xmp template t[9] #pragma xmp distribute t[block] onto p #pragma xmp align a[i] with t[i] #pragma xmp shadow a[1:1]... #pragma xmp reflect (a)!$xmp nodes p(3)!$xmp template t(9)!$xmp distribute t(block) onto p!$xmp align a(i) with t(i)!$xmp shadow a(1:1)...!$xmp reflect (a) The reflect directive synchronizes the shadow area. The directive generates communication between adjacent nodes.
45 45 #pragma xmp loop on t[i] for(int i=1;i<9;i++){ b[i] = a[i-1] + a[i] + a[i+1]; }!$xmp loop on t(i) do i = 2, 8 b(i) = a(i-1) + a(i) + a(i+1) end do
46 46 #pragma xmp shadow a[1:1]... #pragma xmp reflect (a) #pragma xmp loop on t[i] for(int i=1;i<9;i++){ b[i] = a[i-1] + a[i] + a[i+1]; }!$xmp shadow a(1:1)...!$xmp reflect (a)!$xmp loop on t(i) do i = 2, 8 b(i) = a(i-1) + a(i) + a(i+1) end do
47 47 #pragma xmp shadow a[1:1][1:1]!$xmp shadow a(1:1,1:1) #pragma xmp reflect (a)!$xmp reflect (a)
48 48
49 49 real a(8) real b(8)[*] if(this_image() == 1) then b(6)[3] = b(2) a(4) = b(3)[2] end if sync all
50 50 double b[8]:[*]; b[6]:[3] = b[2]; a[4] = b[3]:[2]; void xmp_sync_all(int *status); double a[8]; double b[8]:[*]; real a(8) real b(8)[*] if(xmpc_this_image() == 1){ b[6]:[3] = b[2]; a[4] = b[3]:[2]; } if(this_image() == 1) then b(6)[3] = b(2) a(4) = b(3)[2] end if xmpc_sync_all(null); sync all
51 array-name baselengthstep if(xmpc_this_image() == 1){ a[10:5]:[3] = b[0:5]; a[10:5:2]:[3] = b[0:5:2]; a[:]:[3] = b[:]; } 51
52 array-name baselaststep if(this_image() == 1) then a(10:14)[3] = b(1:5) a(10:18:2)[3] = b(1:9:2) a(:)[3] = b(:) end if 52
53 53
54 Omni Compiler Support XMP, OpenACC, XcalableACC Developed by RIKEN AICS and University of Tsukuba, Japan Open Source Software on GitHub Source-to-source Compiler The latest version is available at 54
55 55
56 56
57 57
58 $ emacs hello.c $ emacs hello.f90 #include <stdio.h> #include <xmp.h> #pragma xmp nodes p[*] int main(){ printf("hello World on node %d\n", xmpc_node_num()); return 0; } program hello!$xmp nodes p(*) write(*,*) "Hello World on node ", xmp_node_num() end program $ xmpcc hello.c -o hello $ mpirun -np 2./hello $ xmpf90 hello.f90 -o hello $ mpirun -np 2./hello 58
Masahiro Nakao, Hitoshi Murai, Takenori Shimosaka, Mitsuhisa Sato
Masahiro Nakao, Hitoshi Murai, Takenori Shimosaka, Mitsuhisa Sato Center for Computational Sciences, University of Tsukuba, Japan RIKEN Advanced Institute for Computational Science, Japan 2 XMP/C int array[16];
More informationOmni Compiler and XcodeML: An Infrastructure for Source-to- Source Transformation
http://omni compiler.org/ Omni Compiler and XcodeML: An Infrastructure for Source-to- Source Transformation MS03 Code Generation Techniques for HPC Earth Science Applications Mitsuhisa Sato (RIKEN / Advanced
More informationWhat is Stencil Computation?
Model Checking Stencil Computations Written in a Partitioned Global Address Space Language Tatsuya Abe, Toshiyuki Maeda, and Mitsuhisa Sato RIKEN AICS HIPS 13 May 20, 2013 What is Stencil Computation?
More informationC PGAS XcalableMP(XMP) Unified Parallel
PGAS XcalableMP Unified Parallel C 1 2 1, 2 1, 2, 3 C PGAS XcalableMP(XMP) Unified Parallel C(UPC) XMP UPC XMP UPC 1 Berkeley UPC GASNet 1. MPI MPI 1 Center for Computational Sciences, University of Tsukuba
More informationMPI_Send(a,..., MPI_COMM_WORLD); MPI_Recv(a,..., MPI_COMM_WORLD, &status);
$ $ 2 global void kernel(int a[max], int llimit, int ulimit) {... } : int main(int argc, char *argv[]){ MPI_Int(&argc, &argc); MPI_Comm_rank(MPI_COMM_WORLD, &rank); MPI_Comm_size(MPI_COMM_WORLD, &size);
More informationMPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &rank); double a[100]; #pragma acc data copy(a) { #pragma acc parallel loop for(i=0;i<100;i++)
2 MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &rank); double a[100]; #pragma acc data copy(a) { #pragma acc parallel loop for(i=0;i
More informationHPC Challenge Awards 2010 Class2 XcalableMP Submission
HPC Challenge Awards 2010 Class2 XcalableMP Submission Jinpil Lee, Masahiro Nakao, Mitsuhisa Sato University of Tsukuba Submission Overview XcalableMP Language and model, proposed by XMP spec WG Fortran
More informationPerformance Comparison between Two Programming Models of XcalableMP
Performance Comparison between Two Programming Models of XcalableMP H. Sakagami Fund. Phys. Sim. Div., National Institute for Fusion Science XcalableMP specification Working Group (XMP-WG) Dilemma in Parallel
More informationLinkage of XcalableMP and Python languages for high productivity on HPC cluster system
Linkage of XcalableMP and Python languages for high productivity on HPC cluster system Masahiro Nakao (RIKEN Center for Computational Science) 6th XMP Workshop@University of Tsukuba, Japan Background XcalableMP
More informationAn Extension of XcalableMP PGAS Lanaguage for Multi-node GPU Clusters
An Extension of XcalableMP PGAS Lanaguage for Multi-node Clusters Jinpil Lee, Minh Tuan Tran, Tetsuya Odajima, Taisuke Boku and Mitsuhisa Sato University of Tsukuba 1 Presentation Overview l Introduction
More informationXcalableMP Implementation and
XcalableMP Implementation and Performance of NAS Parallel Benchmarks Mitsuhisa Sato Masahiro Nakao, Jinpil Lee and Taisuke Boku University of Tsukuba, Japan What s XcalableMP? XcalableMP (XMP for short)
More informationGPU GPU CPU. Raymond Namyst 3 Samuel Thibault 3 Olivier Aumage 3
/CPU,a),2,2 2,2 Raymond Namyst 3 Samuel Thibault 3 Olivier Aumage 3 XMP XMP-dev CPU XMP-dev/StarPU XMP-dev XMP CPU StarPU CPU /CPU XMP-dev/StarPU N /CPU CPU. Graphics Processing Unit GP General-Purpose
More informationLinkage of XcalableMP and Python languages for high productivity on HPC cluster system
Linkage of XcalableMP and Python languages for high productivity on HPC cluster system - Application to Graph Order/degree Problem - 1 1 2 1 Masahiro Nakao, Hitoshi Murai, Taisuke Boku, Mitsuhisa Sato
More informationIPSJ SIG Technical Report Vol.2014-HPC-145 No /7/29 XcalableMP FFT 1 1 1,2 HPC PGAS XcalableMP XcalableMP G-FFT 90.6% 186.6TFLOPS XMP MPI
XcalableMP FFT, HPC PGAS XcalableMP XcalableMP 89 G-FFT 9.6% 86.6TFLOPS XMP MPI. Fourier (FFT) MPI [] Partitioned Global Address Space (PGAS) FFT PGAS PGAS XcalableMP(XMP)[] C Fortran XMP HPC [] Global-FFT
More informationImplementation and Evaluation of Coarray Fortran Translator Based on OMNI XcalableMP. October 29, 2015 Hidetoshi Iwashita, RIKEN AICS
Implementation and Evaluation of Coarray Fortran Translator Based on OMNI XcalableMP October 29, 2015 Hidetoshi Iwashita, RIKEN AICS Background XMP Contains Coarray Features XcalableMP (XMP) A PGAS language,
More informationCS 470 Spring Mike Lam, Professor. OpenMP
CS 470 Spring 2017 Mike Lam, Professor OpenMP OpenMP Programming language extension Compiler support required "Open Multi-Processing" (open standard; latest version is 4.5) Automatic thread-level parallelism
More informationThe CLAW project. Valentin Clément, Xavier Lapillonne. CLAW provides high-level Abstractions for Weather and climate models
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The CLAW project CLAW provides high-level Abstractions for Weather and climate models Valentin Clément,
More informationRuntime Correctness Checking for Emerging Programming Paradigms
(protze@itc.rwth-aachen.de), Christian Terboven, Matthias S. Müller, Serge Petiton, Nahid Emad, Hitoshi Murai and Taisuke Boku RWTH Aachen University, Germany University of Tsukuba / RIKEN, Japan Maison
More informationAMCAT Automata Coding Sample Questions And Answers
1) Find the syntax error in the below code without modifying the logic. #include int main() float x = 1.1; switch (x) case 1: printf( Choice is 1 ); default: printf( Invalid choice ); return
More informationFirst day. Basics of parallel programming. RIKEN CCS HPC Summer School Hiroya Matsuba, RIKEN CCS
First day Basics of parallel programming RIKEN CCS HPC Summer School Hiroya Matsuba, RIKEN CCS Today s schedule: Basics of parallel programming 7/22 AM: Lecture Goals Understand the design of typical parallel
More informationPerformance Evaluation for Omni XcalableMP Compiler on Many-core Cluster System based on Knights Landing
ABSTRACT Masahiro Nakao RIKEN Advanced Institute for Computational Science Hyogo, Japan masahiro.nakao@riken.jp Taisuke Boku Center for Computational Sciences University of Tsukuba Ibaraki, Japan To reduce
More informationCS 470 Spring Mike Lam, Professor. OpenMP
CS 470 Spring 2018 Mike Lam, Professor OpenMP OpenMP Programming language extension Compiler support required "Open Multi-Processing" (open standard; latest version is 4.5) Automatic thread-level parallelism
More informationAssignment 5 Using Paraguin to Create Parallel Programs
Overview Assignment 5 Using Paraguin to Create Parallel Programs C. Ferner andb. Wilkinson October 15, 2014 The goal of this assignment is to use the Paraguin compiler to create parallel solutions using
More informationParallel Programming Libraries and implementations
Parallel Programming Libraries and implementations Partners Funding Reusing this material This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License.
More informationOpenMP on the FDSM software distributed shared memory. Hiroya Matsuba Yutaka Ishikawa
OpenMP on the FDSM software distributed shared memory Hiroya Matsuba Yutaka Ishikawa 1 2 Software DSM OpenMP programs usually run on the shared memory computers OpenMP programs work on the distributed
More informationPRINCIPLES OF OPERATING SYSTEMS
PRINCIPLES OF OPERATING SYSTEMS Tutorial-1&2: C Review CPSC 457, Spring 2015 May 20-21, 2015 Department of Computer Science, University of Calgary Connecting to your VM Open a terminal (in your linux machine)
More informationArrays and Pointers in C. Alan L. Cox
Arrays and Pointers in C Alan L. Cox alc@rice.edu Objectives Be able to use arrays, pointers, and strings in C programs Be able to explain the representation of these data types at the machine level, including
More informationTutorial: parallel coding MPI
Tutorial: parallel coding MPI Pascal Viot September 12, 2018 Pascal Viot Tutorial: parallel coding MPI September 12, 2018 1 / 24 Generalities The individual power of a processor is still growing, but at
More informationXcalableMP and XcalableACC for Productivity and Performance in HPC Challenge Award Competition Class 2 at SC14
XcalableMP and XcalableACC for Productivity and Performance in HPC Challenge Award Competition Class 2 at SC14 Masahiro Nakao 1,2,a) Hitoshi Murai 1 Hidetoshi Iwashita 1 Takenori Shimosaka 1 Akihiro Tabuchi
More informationModule 10: Open Multi-Processing Lecture 19: What is Parallelization? The Lecture Contains: What is Parallelization? Perfectly Load-Balanced Program
The Lecture Contains: What is Parallelization? Perfectly Load-Balanced Program Amdahl's Law About Data What is Data Race? Overview to OpenMP Components of OpenMP OpenMP Programming Model OpenMP Directives
More informationParallel Programming. Libraries and Implementations
Parallel Programming Libraries and Implementations Reusing this material This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_us
More informationCourse Information and Introduction
August 22, 2017 Course Information 1 Instructors : Email : arash.rafiey@indstate.edu Office : Root Hall A-127 Office Hours : Tuesdays 11:30 pm 12:30 pm. Root Hall, A127. 2 Course Home Page : http://cs.indstate.edu/~arash/cs256.html
More informationNew Programming Paradigms: Partitioned Global Address Space Languages
Raul E. Silvera -- IBM Canada Lab rauls@ca.ibm.com ECMWF Briefing - April 2010 New Programming Paradigms: Partitioned Global Address Space Languages 2009 IBM Corporation Outline Overview of the PGAS programming
More informationParallele Numerik. Blatt 1
Universität Konstanz FB Mathematik & Statistik Prof. Dr. M. Junk Dr. Z. Yang Ausgabe: 02. Mai; SS08 Parallele Numerik Blatt 1 As a first step, we consider two basic problems. Hints for the realization
More information27-Sep CSCI 2132 Software Development Lecture 10: Formatted Input and Output. Faculty of Computer Science, Dalhousie University. Lecture 10 p.
Lecture 10 p.1 Faculty of Computer Science, Dalhousie University CSCI 2132 Software Development Lecture 10: Formatted Input and Output 27-Sep-2017 Location: Goldberg CS 127 Time: 14:35 15:25 Instructor:
More informationLecture 4: OpenMP Open Multi-Processing
CS 4230: Parallel Programming Lecture 4: OpenMP Open Multi-Processing January 23, 2017 01/23/2017 CS4230 1 Outline OpenMP another approach for thread parallel programming Fork-Join execution model OpenMP
More informationHigh Performance Computing: Tools and Applications
High Performance Computing: Tools and Applications Edmond Chow School of Computational Science and Engineering Georgia Institute of Technology Lecture 2 OpenMP Shared address space programming High-level
More informationEL2310 Scientific Programming
Lecture 7: Introduction to C (pronobis@kth.se) Overview Overview Lecture 7: Introduction to C Wrap Up Basic Datatypes and printf Branching and Loops in C Constant values Wrap Up Lecture 7: Introduction
More informationLaboratory 2: Programming Basics and Variables. Lecture notes: 1. A quick review of hello_comment.c 2. Some useful information
Laboratory 2: Programming Basics and Variables Lecture notes: 1. A quick review of hello_comment.c 2. Some useful information 3. Comment: a. name your program with extension.c b. use o option to specify
More informationHPCSE - I. «OpenMP Programming Model - Part I» Panos Hadjidoukas
HPCSE - I «OpenMP Programming Model - Part I» Panos Hadjidoukas 1 Schedule and Goals 13.10.2017: OpenMP - part 1 study the basic features of OpenMP able to understand and write OpenMP programs 20.10.2017:
More informationOpenMP. António Abreu. Instituto Politécnico de Setúbal. 1 de Março de 2013
OpenMP António Abreu Instituto Politécnico de Setúbal 1 de Março de 2013 António Abreu (Instituto Politécnico de Setúbal) OpenMP 1 de Março de 2013 1 / 37 openmp what? It s an Application Program Interface
More informationPost-Petascale Computing. Mitsuhisa Sato
Challenges on Programming Models and Languages for Post-Petascale Computing -- from Japanese NGS project "The K computer" to Exascale computing -- Mitsuhisa Sato Center for Computational Sciences (CCS),
More informationQUIZ on Ch.8. What is kit?
QUIZ on Ch.8 What is kit? QUIZ on Ch.8 What is kit? A: A vector of nested structures! QUIZ on Ch.8 C crash-course Compilation and Execution of a C Program Run-time Interpreted languages (like MATLAB, Python,
More informationOpenMP and MPI. Parallel and Distributed Computing. Department of Computer Science and Engineering (DEI) Instituto Superior Técnico.
OpenMP and MPI Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico November 15, 2010 José Monteiro (DEI / IST) Parallel and Distributed Computing
More informationMiwako TSUJI XcalableMP
Miwako TSUJI AICS 2014.10.24 2 XcalableMP 2010.09 2014.03 2013.10.25 AKIHABARA FP2C (Framework for Post-Petascale Computing) YML + XMP(-dev) + StarPU integrated developed in Japan and in France Experimental
More informationCOMP Parallel Computing. SMM (2) OpenMP Programming Model
COMP 633 - Parallel Computing Lecture 7 September 12, 2017 SMM (2) OpenMP Programming Model Reading for next time look through sections 7-9 of the Open MP tutorial Topics OpenMP shared-memory parallel
More informationParallel Programming Languages 1 - OpenMP
some slides are from High-Performance Parallel Scientific Computing, 2008, Purdue University & CSCI-UA.0480-003: Parallel Computing, Spring 2015, New York University Parallel Programming Languages 1 -
More informationOpenMP and MPI. Parallel and Distributed Computing. Department of Computer Science and Engineering (DEI) Instituto Superior Técnico.
OpenMP and MPI Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico November 16, 2011 CPD (DEI / IST) Parallel and Distributed Computing 18
More informationObjective. GPU Teaching Kit. OpenACC. To understand the OpenACC programming model. Introduction to OpenACC
GPU Teaching Kit Accelerated Computing OpenACC Introduction to OpenACC Objective To understand the OpenACC programming model basic concepts and pragma types simple examples 2 2 OpenACC The OpenACC Application
More informationAssignment 2 Using Paraguin to Create Parallel Programs
Overview Assignment 2 Using Paraguin to Create Parallel Programs C. Ferner and B. Wilkinson Minor clarification Oct 11, 2013 The goal of this assignment is to use the Paraguin compiler to create parallel
More informationLab 1: Introduction to C Programming. (Creating a program using the Microsoft developer Studio, Compiling and Linking)
Lab 1: Introduction to C Programming (Creating a program using the Microsoft developer Studio, Compiling and Linking) Learning Objectives 0. To become familiar with Microsoft Visual C++ 6.0 environment
More informationAPT Session 4: C. Software Development Team Laurence Tratt. 1 / 14
APT Session 4: C Laurence Tratt Software Development Team 2017-11-10 1 / 14 http://soft-dev.org/ What to expect from this session 1 C. 2 / 14 http://soft-dev.org/ Prerequisites 1 Install either GCC or
More informationCS 426. Building and Running a Parallel Application
CS 426 Building and Running a Parallel Application 1 Task/Channel Model Design Efficient Parallel Programs (or Algorithms) Mainly for distributed memory systems (e.g. Clusters) Break Parallel Computations
More informationPresented By : Gaurav Juneja
Presented By : Gaurav Juneja Introduction C is a general purpose language which is very closely associated with UNIX for which it was developed in Bell Laboratories. Most of the programs of UNIX are written
More informationOpenMP, Part 2. EAS 520 High Performance Scientific Computing. University of Massachusetts Dartmouth. Spring 2015
OpenMP, Part 2 EAS 520 High Performance Scientific Computing University of Massachusetts Dartmouth Spring 2015 References This presentation is almost an exact copy of Dartmouth College's openmp tutorial.
More informationAssignment 1 OpenMP Tutorial Assignment
Assignment 1 OpenMP Tutorial Assignment B. Wilkinson and C Ferner: Modification date Aug 5, 2014 Overview In this assignment, you will write and execute run some simple OpenMP programs as a tutorial. First
More informationPractical Introduction to
Outline of the workshop Practical Introduction to Bart Oldeman, Calcul Québec McGill HPC Bart.Oldeman@mcgill.ca Theoretical / practical introduction Parallelizing your serial code What is OpenMP? Why do
More informationmith College Computer Science CSC352 Week #7 Spring 2017 Introduction to C Dominique Thiébaut
mith College CSC352 Week #7 Spring 2017 Introduction to C Dominique Thiébaut dthiebaut@smith.edu Learning C in 2 Hours D. Thiebaut Dennis Ritchie 1969 to 1973 AT&T Bell Labs Close to Assembly Unix Standard
More informationEL2310 Scientific Programming
(yaseminb@kth.se) Overview Overview Roots of C Getting started with C Closer look at Hello World Programming Environment Discussion Basic Datatypes and printf Schedule Introduction to C - main part of
More informationThe C language. Introductory course #1
The C language Introductory course #1 History of C Born at AT&T Bell Laboratory of USA in 1972. Written by Dennis Ritchie C language was created for designing the UNIX operating system Quickly adopted
More informationIntroduction to Programming. Lecture 2: Introduction to C
Introduction to Programming Lecture 2: Introduction to C Mahmoud El-Gayyar elgayyar@ci.suez.edu.eg Review Lecture 1 Introduction to the course General information Syllabus Course organization General rules
More informationBSM540 Basics of C Language
BSM540 Basics of C Language Chapter 4: Character strings & formatted I/O Prof. Manar Mohaisen Department of EEC Engineering Review of the Precedent Lecture To explain the input/output functions printf()
More informationKurt Schmidt. October 30, 2018
to Structs Dept. of Computer Science, Drexel University October 30, 2018 Array Objectives to Structs Intended audience: Student who has working knowledge of Python To gain some experience with a statically-typed
More informationOpen Multi-Processing: Basic Course
HPC2N, UmeåUniversity, 901 87, Sweden. May 26, 2015 Table of contents Overview of Paralellism 1 Overview of Paralellism Parallelism Importance Partitioning Data Distributed Memory Working on Abisko 2 Pragmas/Sentinels
More informationOpenACC. Part 2. Ned Nedialkov. McMaster University Canada. CS/SE 4F03 March 2016
OpenACC. Part 2 Ned Nedialkov McMaster University Canada CS/SE 4F03 March 2016 Outline parallel construct Gang loop Worker loop Vector loop kernels construct kernels vs. parallel Data directives c 2013
More informationExploring XcalableMP. Shun Liang. August 24, 2012
Exploring XcalableMP Shun Liang August 24, 2012 MSc in High Performance Computing The University of Edinburgh Year of Presentation: 2012 Abstract This project has implemented synthetic and application
More informationRicardo Rocha. Department of Computer Science Faculty of Sciences University of Porto
Ricardo Rocha Department of Computer Science Faculty of Sciences University of Porto Adapted from the slides Revisões sobre Programação em C, Sérgio Crisóstomo Compilation #include int main()
More informationShared Memory Programming Paradigm!
Shared Memory Programming Paradigm! Ivan Girotto igirotto@ictp.it Information & Communication Technology Section (ICTS) International Centre for Theoretical Physics (ICTP) 1 Multi-CPUs & Multi-cores NUMA
More informationFrom Hello World to Exascale
From Hello World to Exascale Rob Farber Chief Scien0st, BlackDog Endeavors, LLC Author, CUDA Applica0on Design and Development Research consultant: ICHEC and others Doctor Dobb s Journal CUDA & OpenACC
More informationParallel Processing/Programming
Parallel Processing/Programming with the applications to image processing Lectures: 1. Parallel Processing & Programming from high performance mono cores to multi- and many-cores 2. Programming Interfaces
More informationComputer Science & Engineering 150A Problem Solving Using Computers
Computer Science & Engineering 150A Problem Solving Using Computers Lecture 06 - Stephen Scott Adapted from Christopher M. Bourke 1 / 30 Fall 2009 Chapter 8 8.1 Declaring and 8.2 Array Subscripts 8.3 Using
More informationME759 High Performance Computing for Engineering Applications
ME759 High Performance Computing for Engineering Applications Parallel Computing on Multicore CPUs October 25, 2013 Dan Negrut, 2013 ME964 UW-Madison A programming language is low level when its programs
More informationCS 261 Fall C Introduction. Variables, Memory Model, Pointers, and Debugging. Mike Lam, Professor
CS 261 Fall 2017 Mike Lam, Professor C Introduction Variables, Memory Model, Pointers, and Debugging The C Language Systems language originally developed for Unix Imperative, compiled language with static
More informationSynchronization. Event Synchronization
Synchronization Synchronization: mechanisms by which a parallel program can coordinate the execution of multiple threads Implicit synchronizations Explicit synchronizations Main use of explicit synchronization
More informationECEN 449 Microprocessor System Design. Review of C Programming. Texas A&M University
ECEN 449 Microprocessor System Design Review of C Programming 1 Objectives of this Lecture Unit Review C programming basics Refresh programming skills 2 Basic C program structure # include main()
More informationCOMP s1 Lecture 1
COMP1511 18s1 Lecture 1 1 Numbers In, Numbers Out Andrew Bennett more printf variables scanf 2 Before we begin introduce yourself to the person sitting next to you why did
More informationHPC Practical Course Part 3.1 Open Multi-Processing (OpenMP)
HPC Practical Course Part 3.1 Open Multi-Processing (OpenMP) V. Akishina, I. Kisel, G. Kozlov, I. Kulakov, M. Pugach, M. Zyzak Goethe University of Frankfurt am Main 2015 Task Parallelism Parallelization
More informationCMPT 115. C tutorial for students who took 111 in Java. University of Saskatchewan. Mark G. Eramian, Ian McQuillan CMPT 115 1/32
CMPT 115 C tutorial for students who took 111 in Java Mark G. Eramian Ian McQuillan University of Saskatchewan Mark G. Eramian, Ian McQuillan CMPT 115 1/32 Part I Starting out Mark G. Eramian, Ian McQuillan
More informationHigh Performance Computing: Tools and Applications
High Performance Computing: Tools and Applications Edmond Chow School of Computational Science and Engineering Georgia Institute of Technology Lecture 9 SIMD vectorization using #pragma omp simd force
More informationIntroduction to C Programming
Introduction to C Programming Digital Design and Computer Architecture David Money Harris and Sarah L. Harris 2- C Chapter :: Topics Introduction to C Why C? Example Program Compiling and running a
More informationOpenMP. A parallel language standard that support both data and functional Parallelism on a shared memory system
OpenMP A parallel language standard that support both data and functional Parallelism on a shared memory system Use by system programmers more than application programmers Considered a low level primitives
More informationComparing OpenACC 2.5 and OpenMP 4.1 James C Beyer PhD, Sept 29 th 2015
Comparing OpenACC 2.5 and OpenMP 4.1 James C Beyer PhD, Sept 29 th 2015 Abstract As both an OpenMP and OpenACC insider I will present my opinion of the current status of these two directive sets for programming
More informationIntroduction to OpenMP
Introduction to OpenMP Ricardo Fonseca https://sites.google.com/view/rafonseca2017/ Outline Shared Memory Programming OpenMP Fork-Join Model Compiler Directives / Run time library routines Compiling and
More informationSTRUCTURED DATA TYPE ARRAYS IN C++ ONE-DIMENSIONAL ARRAY TWO-DIMENSIONAL ARRAY
STRUCTURED DATA TYPE ARRAYS IN C++ ONE-DIMENSIONAL ARRAY TWO-DIMENSIONAL ARRAY Objectives Declaration of 1-D and 2-D Arrays Initialization of arrays Inputting array elements Accessing array elements Manipulation
More informationCSE 160 Lecture 10. Instruction level parallelism (ILP) Vectorization
CSE 160 Lecture 10 Instruction level parallelism (ILP) Vectorization Announcements Quiz on Friday Signup for Friday labs sessions in APM 2013 Scott B. Baden / CSE 160 / Winter 2013 2 Particle simulation
More informationPointers. Pointer Variables. Chapter 11. Pointer Variables. Pointer Variables. Pointer Variables. Declaring Pointer Variables
Chapter 11 Pointers The first step in understanding pointers is visualizing what they represent at the machine level. In most modern computers, main memory is divided into bytes, with each byte capable
More informationProgramming in C. Pointers and Arrays
Programming in C Pointers and Arrays NEXT SET OF SLIDES FROM DENNIS FREY S FALL 2011 CMSC313 http://www.csee.umbc.edu/courses/undergraduate/313/fall11/" Pointers and Arrays In C, there is a strong relationship
More informationChapter 01 Arrays Prepared By: Dr. Murad Magableh 2013
Chapter 01 Arrays Prepared By: Dr. Murad Magableh 2013 One Dimensional Q1: Write a program that declares two arrays of integers and fills them from the user. Then exchanges their values and display the
More informationMCA Semester 1. MC0061 Computer Programming C Language 4 Credits Assignment: Set 1 (40 Marks)
Summer 2012 MCA Semester 1 4 Credits Assignment: Set 1 (40 Marks) Q1. Explain the following operators with an example for each: a. Conditional Operators b. Bitwise Operators c. gets() and puts() function
More informationC++ ARRAYS POINTERS POINTER ARITHMETIC. Problem Solving with Computers-I
C++ ARRAYS POINTERS POINTER ARITHMETIC Problem Solving with Computers-I General model of memory Sequence of adjacent cells Each cell has 1-byte stored in it Each cell has an address (memory location) Memory
More informationFundamentals of Programming Session 12
Fundamentals of Programming Session 12 Instructor: Reza Entezari-Maleki Email: entezari@ce.sharif.edu 1 Fall 2014 These slides have been created using Deitel s slides Sharif University of Technology Outlines
More informationMPI introduction - exercises -
MPI introduction - exercises - Paolo Ramieri, Maurizio Cremonesi May 2016 Startup notes Access the server and go on scratch partition: ssh a08tra49@login.galileo.cineca.it cd $CINECA_SCRATCH Create a job
More informationPROGRAMOVÁNÍ V C++ CVIČENÍ. Michal Brabec
PROGRAMOVÁNÍ V C++ CVIČENÍ Michal Brabec PARALLELISM CATEGORIES CPU? SSE Multiprocessor SIMT - GPU 2 / 17 PARALLELISM V C++ Weak support in the language itself, powerful libraries Many different parallelization
More informationIntroduction to OpenMP
1 / 7 Introduction to OpenMP: Exercises and Handout Introduction to OpenMP Christian Terboven Center for Computing and Communication, RWTH Aachen University Seffenter Weg 23, 52074 Aachen, Germany Abstract
More informationParallel Programming with OpenMP. CS240A, T. Yang
Parallel Programming with OpenMP CS240A, T. Yang 1 A Programmer s View of OpenMP What is OpenMP? Open specification for Multi-Processing Standard API for defining multi-threaded shared-memory programs
More informationCo-arrays to be included in the Fortran 2008 Standard
Co-arrays to be included in the Fortran 2008 Standard John Reid, ISO Fortran Convener The ISO Fortran Committee has decided to include co-arrays in the next revision of the Standard. Aim of this talk:
More informationRun$me Pointer Disambigua$on
Run$me Pointer Disambigua$on Péricles Alves periclesrafael@dcc.ufmg.br Alexandros Lamprineas labrinea@inria.fr Fabian Gruber fabian.gruber@inria.fr Tobias Grosser tgrosser@inf.ethz.ch Fernando Pereira
More informationOutline Arrays Examples of array usage Passing arrays to functions 2D arrays Strings Searching arrays Next Time. C Arrays.
CS 2060 Week 5 1 Arrays Arrays Initializing arrays 2 Examples of array usage 3 Passing arrays to functions 4 2D arrays 2D arrays 5 Strings Using character arrays to store and manipulate strings 6 Searching
More informationDon t reinvent the wheel. BLAS LAPACK Intel Math Kernel Library
Libraries Don t reinvent the wheel. Specialized math libraries are likely faster. BLAS: Basic Linear Algebra Subprograms LAPACK: Linear Algebra Package (uses BLAS) http://www.netlib.org/lapack/ to download
More informationOpenMP Programming. Prof. Thomas Sterling. High Performance Computing: Concepts, Methods & Means
High Performance Computing: Concepts, Methods & Means OpenMP Programming Prof. Thomas Sterling Department of Computer Science Louisiana State University February 8 th, 2007 Topics Introduction Overview
More information