D-BAUG Informatik I. Exercise session: week 5 HS 2018
|
|
- Katherine Stevenson
- 5 years ago
- Views:
Transcription
1 1 D-BAUG Informatik I Exercise session: week 5 HS 2018
2 Homework 2 Questions?
3 Matrix and Vector in Java 3 Vector v of length n:
4 Matrix and Vector in Java 3 Vector v of length n: double[] v = new double[n]; Matrix M with n rows and m columns:
5 Matrix and Vector in Java 3 Vector v of length n: double[] v = new double[n]; Matrix M with n rows and m columns: double M[][] = new double[n][m]; (a vector of length n of vectors of length m)
6 Task 4 Write a program that reads an n m matrix of integers from the input. The input is top to bottom, left to right: elements of the first column, followed by elements of the second column, etc. Print the matrix in the typical orientation (a row on the matrix on each row of the output). 1 Allocate an n m matrix.
7 4 Task Write a program that reads an n m matrix of integers from the input. The input is top to bottom, left to right: elements of the first column, followed by elements of the second column, etc. Print the matrix in the typical orientation (a row on the matrix on each row of the output). 1 Allocate an n m matrix. 2 For each column j (m in total), row i (n in total), read an integer from input and store it in the matrix.
8 4 Task Write a program that reads an n m matrix of integers from the input. The input is top to bottom, left to right: elements of the first column, followed by elements of the second column, etc. Print the matrix in the typical orientation (a row on the matrix on each row of the output). 1 Allocate an n m matrix. 2 For each column j (m in total), row i (n in total), read an integer from input and store it in the matrix. 3 For each row i (n in total), column j (m in total), print M[i][j]. Print a new line at the end of each row.
9 Matrix-Matrix-Multiplication 5 Let an n m matrix A and an m p matrix B be given A 11 A 12 A 1m B 11 B 12 B 1p A = A 21 A 22 A 2m B = B 21 B 22 B 2p A n1 A n2 A nm B m1 B m2 B mp
10 Matrix-Matrix-Multiplication 6 The matrix product of n m matrix A and m p matrix B is a new n p matrix C C 11 C 12 C 1p A B = B =: C 21 C 22 C 2p C n1 C n2 C np
11 Matrix-Matrix-Multiplication 7 m m B n A p C = A B
12 Matrix-Matrix-Multiplication 8 m C ij = A ik B kj 1 i p, 1 j n k=1
13 Task 9 Write a program that you can use to multiply two matrices. 1 First think about the input: what kind of format do you use for input?
14 9 Task Write a program that you can use to multiply two matrices. 1 First think about the input: what kind of format do you use for input? 2 Now think about the output: what kind of format do you use for output?
15 9 Task Write a program that you can use to multiply two matrices. 1 First think about the input: what kind of format do you use for input? 2 Now think about the output: what kind of format do you use for output? 3 Write funtions for In- and Output and test them. Always test small parts. What do you write first: input or output function?
16 9 Task Write a program that you can use to multiply two matrices. 1 First think about the input: what kind of format do you use for input? 2 Now think about the output: what kind of format do you use for output? 3 Write funtions for In- and Output and test them. Always test small parts. What do you write first: input or output function? 4 Write the core function for matrix multiplication.
17 9 Task Write a program that you can use to multiply two matrices. 1 First think about the input: what kind of format do you use for input? 2 Now think about the output: what kind of format do you use for output? 3 Write funtions for In- and Output and test them. Always test small parts. What do you write first: input or output function? 4 Write the core function for matrix multiplication. 5 Test your function with some test cases. How do you choose the cases?
18 Challenge 10 Now use your working function in our setup for image transformation: consider the Main method and add your matrix-matrix- multiplication function accordingly.
Lab #10 Multi-dimensional Arrays
Multi-dimensional Arrays Sheet s Owner Student ID Name Signature Group partner 1. Two-Dimensional Arrays Arrays that we have seen and used so far are one dimensional arrays, where each element is indexed
More informationPartha Sarathi Manal
MA 515: Introduction to Algorithms & MA353 : Design and Analysis of Algorithms [3-0-0-6] Lecture 29 http://www.iitg.ernet.in/psm/indexing_ma353/y09/index.html Partha Sarathi Manal psm@iitg.ernet.in Dept.
More informationMatlab- Command Window Operations, Scalars and Arrays
1 ME313 Homework #1 Matlab- Command Window Operations, Scalars and Arrays Last Updated August 17 2012. Assignment: Read and complete the suggested commands. After completing the exercise, copy the contents
More informationIntroduction to Parallel Programming Message Passing Interface Practical Session Part I
Introduction to Parallel Programming Message Passing Interface Practical Session Part I T. Streit, H.-J. Pflug streit@rz.rwth-aachen.de October 28, 2008 1 1. Examples We provide codes of the theoretical
More information1. Represent each of these relations on {1, 2, 3} with a matrix (with the elements of this set listed in increasing order).
Exercises Exercises 1. Represent each of these relations on {1, 2, 3} with a matrix (with the elements of this set listed in increasing order). a) {(1, 1), (1, 2), (1, 3)} b) {(1, 2), (2, 1), (2, 2), (3,
More informationChapter 18 out of 37 from Discrete Mathematics for Neophytes: Number Theory, Probability, Algorithms, and Other Stuff by J. M. Cargal.
Chapter 8 out of 7 from Discrete Mathematics for Neophytes: Number Theory, Probability, Algorithms, and Other Stuff by J. M. Cargal 8 Matrices Definitions and Basic Operations Matrix algebra is also known
More informationExercise Set Decide whether each matrix below is an elementary matrix. (a) (b) (c) (d) Answer:
Understand the relationships between statements that are equivalent to the invertibility of a square matrix (Theorem 1.5.3). Use the inversion algorithm to find the inverse of an invertible matrix. Express
More informationParallelizing The Matrix Multiplication. 6/10/2013 LONI Parallel Programming Workshop
Parallelizing The Matrix Multiplication 6/10/2013 LONI Parallel Programming Workshop 2013 1 Serial version 6/10/2013 LONI Parallel Programming Workshop 2013 2 X = A md x B dn = C mn d c i,j = a i,k b k,j
More informationAttendance (2) Performance (3) Oral (5) Total (10) Dated Sign of Subject Teacher
Attendance (2) Performance (3) Oral (5) Total (10) Dated Sign of Subject Teacher Date of Performance:... Actual Date of Completion:... Expected Date of Completion:... ----------------------------------------------------------------------------------------------------------------
More informationLecture 5: Matrices. Dheeraj Kumar Singh 07CS1004 Teacher: Prof. Niloy Ganguly Department of Computer Science and Engineering IIT Kharagpur
Lecture 5: Matrices Dheeraj Kumar Singh 07CS1004 Teacher: Prof. Niloy Ganguly Department of Computer Science and Engineering IIT Kharagpur 29 th July, 2008 Types of Matrices Matrix Addition and Multiplication
More informationParallel Algorithms CSE /22/2015. Outline of this lecture: 1 Implementation of cilk for. 2. Parallel Matrix Multiplication
CSE 539 01/22/2015 Parallel Algorithms Lecture 3 Scribe: Angelina Lee Outline of this lecture: 1. Implementation of cilk for 2. Parallel Matrix Multiplication 1 Implementation of cilk for We mentioned
More informationAgenda. Arrays 01/12/2009 INTRODUCTION TO VBA PROGRAMMING. Arrays Matrices.
INTRODUCTION TO VBA PROGRAMMING LESSON6 dario.bonino@polito.it Agenda Matrices 1 Allow to store vectorial data Geometric vectors Sets of data having something in common... Declared as Dim array_name (begin
More informationDynamic Programming II
June 9, 214 DP: Longest common subsequence biologists often need to find out how similar are 2 DNA sequences DNA sequences are strings of bases: A, C, T and G how to define similarity? DP: Longest common
More informationLecture 57 Dynamic Programming. (Refer Slide Time: 00:31)
Programming, Data Structures and Algorithms Prof. N.S. Narayanaswamy Department of Computer Science and Engineering Indian Institution Technology, Madras Lecture 57 Dynamic Programming (Refer Slide Time:
More informationLecture 13: Chain Matrix Multiplication
Lecture 3: Chain Matrix Multiplication CLRS Section 5.2 Revised April 7, 2003 Outline of this Lecture Recalling matrix multiplication. The chain matrix multiplication problem. A dynamic programming algorithm
More informationStudent Outcomes. Classwork. Opening Exercise (3 minutes) Example 1 (8 minutes)
Student Outcomes Students model and write equivalent expressions using the distributive property. They move from a factored form to an expanded form of an expression. Classwork Opening Exercise (3 minutes)
More information12 Dynamic Programming (2) Matrix-chain Multiplication Segmented Least Squares
12 Dynamic Programming (2) Matrix-chain Multiplication Segmented Least Squares Optimal substructure Dynamic programming is typically applied to optimization problems. An optimal solution to the original
More informationCS4620/5620. Professor: Kavita Bala. Cornell CS4620/5620 Fall 2012 Lecture Kavita Bala 1 (with previous instructors James/Marschner)
CS4620/5620 Affine and 3D Transformations Professor: Kavita Bala 1 Announcements Updated schedule on course web page 2 Prelim days finalized and posted Oct 11, Nov 29 No final exam, final project will
More information14 Dynamic. Matrix-chain multiplication. P.D. Dr. Alexander Souza. Winter term 11/12
Algorithms Theory 14 Dynamic Programming (2) Matrix-chain multiplication P.D. Dr. Alexander Souza Optimal substructure Dynamic programming is typically applied to optimization problems. An optimal solution
More informationSelf-study session 1, Discrete mathematics
Self-study session 1, Discrete mathematics First year mathematics for the technology and science programmes Aalborg University In this self-study session we are working with time complexity. Space complexity
More informationEXTENSION. a 1 b 1 c 1 d 1. Rows l a 2 b 2 c 2 d 2. a 3 x b 3 y c 3 z d 3. This system can be written in an abbreviated form as
EXTENSION Using Matrix Row Operations to Solve Systems The elimination method used to solve systems introduced in the previous section can be streamlined into a systematic method by using matrices (singular:
More informationModified Distribution Method
istributors C 8 Step : Make an initial allocation with the North-West corner rule. KPP istributors C 8 V j Step : Make an initial allocation with the North-West corner rule. Step : Introduce the variables,
More informationCS 177. Lists and Matrices. Week 8
CS 177 Lists and Matrices Week 8 1 Announcements Project 2 due on 7 th March, 2015 at 11.59 pm Table of Contents Lists Matrices Traversing a Matrix Construction of Matrices 3 Just a list of numbers 1D
More informationSolve the matrix equation AX B for X by using A.(1-3) Use the Inverse Matrix Calculator Link to check your work
Name: Math 1324 Activity 9(4.6)(Due by Oct. 20) Dear Instructor or Tutor, These problems are designed to let my students show me what they have learned and what they are capable of doing on their own.
More informationCE 601: Numerical Methods Lecture 5. Course Coordinator: Dr. Suresh A. Kartha, Associate Professor, Department of Civil Engineering, IIT Guwahati.
CE 601: Numerical Methods Lecture 5 Course Coordinator: Dr. Suresh A. Kartha, Associate Professor, Department of Civil Engineering, IIT Guwahati. Elimination Methods For a system [A]{x} = {b} where [A]
More informationDynamic Programming part 2
Dynamic Programming part 2 Week 7 Objectives More dynamic programming examples - Matrix Multiplication Parenthesis - Longest Common Subsequence Subproblem Optimal structure Defining the dynamic recurrence
More informationMATLAB for Experimental Research. Fall 2018 Vectors, Matrices, Matrix Operations
MATLAB for Experimental Research Fall 2018 Vectors, Matrices, Matrix Operations Matlab is more than a calculator! The array is a fundamental form that MATLAB uses to store and manipulate data. An array
More informationPerformance Optimization Tutorial. Labs
Performance Optimization Tutorial Labs Exercise 1 These exercises are intended to provide you with a general feeling about some of the issues involved in writing high performance code, both on a single
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra)
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 1: Course Overview; Matrix Multiplication Xiangmin Jiao Stony Brook University Xiangmin Jiao Numerical Analysis I 1 / 21 Outline 1 Course
More informationREGULAR GRAPHS OF GIVEN GIRTH. Contents
REGULAR GRAPHS OF GIVEN GIRTH BROOKE ULLERY Contents 1. Introduction This paper gives an introduction to the area of graph theory dealing with properties of regular graphs of given girth. A large portion
More informationSection 26: Associativity and Order of Operations
Section 26: Associativity and Order of Operations One of the most important properties of the matrix operations is called associativity To understand what this property is we need to discuss something
More informationFOR LOOP. for <indexmin:indexstep:indexmax> {statements} end
FOR LOOP for {statements} Exercise: Define a vector z R 10 (= R 10 1 ) s.t. z j = 2 j for j = 1,...,10. Solution. Create a new script wiht the following instructions: for
More informationMatrix Transformations The position of the corners of this triangle are described by the vectors: 0 1 ] 0 1 ] Transformation:
Matrix Transformations The position of the corners of this triangle are described by the vectors: [ 2 1 ] & [4 1 ] & [3 3 ] Use each of the matrices below to transform these corners. In each case, draw
More informationLecture 8. Dynamic Programming
Lecture 8. Dynamic Programming T. H. Cormen, C. E. Leiserson and R. L. Rivest Introduction to Algorithms, 3rd Edition, MIT Press, 2009 Sungkyunkwan University Hyunseung Choo choo@skku.edu Copyright 2000-2018
More informationCS 33. Caches. CS33 Intro to Computer Systems XVIII 1 Copyright 2017 Thomas W. Doeppner. All rights reserved.
CS 33 Caches CS33 Intro to Computer Systems XVIII 1 Copyright 2017 Thomas W. Doeppner. All rights reserved. Cache Performance Metrics Miss rate fraction of memory references not found in cache (misses
More informationCSC-140 Assignment 5
CSC-140 Assignment 5 Please do not Google a solution to these problems, cause that won t teach you anything about programming - the only way to get good at it, and understand it, is to do it! 1 Introduction
More informationn = 1 What problems are interesting when n is just 1?
What if n=1??? n = 1 What problems are interesting when n is just 1? Sorting? No Median finding? No Addition? How long does it take to add one pair of numbers? Multiplication? How long does it take to
More informationHomework3: Dynamic Programming - Answers
Most Exercises are from your textbook: Homework3: Dynamic Programming - Answers 1. For the Rod Cutting problem (covered in lecture) modify the given top-down memoized algorithm (includes two procedures)
More informationMatrix Multiplication
Matrix Multiplication Nur Dean PhD Program in Computer Science The Graduate Center, CUNY 05/01/2017 Nur Dean (The Graduate Center) Matrix Multiplication 05/01/2017 1 / 36 Today, I will talk about matrix
More informationSo far... Finished looking at lower bounds and linear sorts.
So far... Finished looking at lower bounds and linear sorts. Next: Memoization -- Optimization problems - Dynamic programming A scheduling problem Matrix multiplication optimization Longest Common Subsequence
More informationLecture 7: Divide & Conquer 2. Integer Multiplication. & Matrix Multiplication. CS 341: Algorithms. Tuesday, Jan 29 th 2019
Lecture 7: Divide & Conquer 2 Integer Multiplication & Matrix Multiplication CS 341: Algorithms Tuesday, Jan 29 th 2019 1 Outline For Today 1. Integer Multiplication 2. Matrix Multiplication 2 Outline
More informationLecture 4: Principles of Parallel Algorithm Design (part 4)
Lecture 4: Principles of Parallel Algorithm Design (part 4) 1 Mapping Technique for Load Balancing Minimize execution time Reduce overheads of execution Sources of overheads: Inter-process interaction
More informationLesson 6.8. Activity Materials square tiles
Name Write Related Facts Essential Question How can you write a set of related multiplication and division facts? Unlock the Problem ALGEBRA Lesson 6.8 Operations and Algebraic Thinking 3.OA.C.7 Also 3.OA.A.2,
More informationChain Matrix Multiplication
Chain Matrix Multiplication Version of November 5, 2014 Version of November 5, 2014 Chain Matrix Multiplication 1 / 27 Outline Outline Review of matrix multiplication. The chain matrix multiplication problem.
More informationGRADE 7 MATH LEARNING GUIDE
GRADE 7 MATH Lesson 9: Properties of the Operations on Rational Numbers Time:.5 hours Pre-requisite Concepts: Operations on rational numbers About the Lesson: The purpose of this lesson is to use properties
More informationINTRODUCTION TO FORTRAN PART II
INTRODUCTION TO FORTRAN PART II Prasenjit Ghosh An example: The Fibonacci Sequence The Fibonacci sequence consists of an infinite set of integer nos. which satisfy the following recurrence relation x n
More information2.3 Algorithms Using Map-Reduce
28 CHAPTER 2. MAP-REDUCE AND THE NEW SOFTWARE STACK one becomes available. The Master must also inform each Reduce task that the location of its input from that Map task has changed. Dealing with a failure
More informationExercise 4: Loops, Arrays and Files
Exercise 4: Loops, Arrays and Files worth 24% of the final mark November 4, 2004 Instructions Submit your programs in a floppy disk. Deliver the disk to Michele Zito at the 12noon lecture on Tuesday November
More informationUML CS Algorithms Qualifying Exam Fall, 2003 ALGORITHMS QUALIFYING EXAM
NAME: This exam is open: - books - notes and closed: - neighbors - calculators ALGORITHMS QUALIFYING EXAM The upper bound on exam time is 3 hours. Please put all your work on the exam paper. (Partial credit
More informationFinite Math - J-term Homework. Section Inverse of a Square Matrix
Section.5-77, 78, 79, 80 Finite Math - J-term 017 Lecture Notes - 1/19/017 Homework Section.6-9, 1, 1, 15, 17, 18, 1, 6, 9, 3, 37, 39, 1,, 5, 6, 55 Section 5.1-9, 11, 1, 13, 1, 17, 9, 30 Section.5 - Inverse
More informationCOSC2430: Programming and Data Structures Sparse Matrix Multiplication: Search and Sorting Algorithms
COSC2430: Programming and Data Structures Sparse Matrix Multiplication: Search and Sorting Algorithms 1 Introduction You will create a C++ program to multiply two sparse matrices as efficiently as possible
More informationLesson 13: Exploring Factored Form
Opening Activity Below is a graph of the equation y = 6(x 3)(x + 2). It is also the graph of: y = 3(2x 6)(x + 2) y = 2(3x 9)(x + 2) y = 2(x 3)(3x + 6) y = 3(x 3)(2x + 4) y = (3x 9)(2x + 4) y = (2x 6)(3x
More informationDESIGN AND ANALYSIS OF ALGORITHMS (DAA 2017)
DESIGN AND ANALYSIS OF ALGORITHMS (DAA 2017) Veli Mäkinen Design and Analysis of Algorithms 2017 week 4 11.8.2017 1 Dynamic Programming Week 4 2 Design and Analysis of Algorithms 2017 week 4 11.8.2017
More informationLesson 20: Every Line is a Graph of a Linear Equation
Student Outcomes Students know that any non vertical line is the graph of a linear equation in the form of, where is a constant. Students write the equation that represents the graph of a line. Lesson
More informationProgramming Problem for the 1999 Comprehensive Exam
Programming Problem for the 1999 Comprehensive Exam Out: Monday 1/25/99 at 9:00 am Due: Friday 1/29/99 at 5:00 pm Department of Computer Science Brown University Providence, RI 02912 1.0 The Task This
More informationLAB 2: Linear Equations and Matrix Algebra. Preliminaries
Math 250C, Section C2 Hard copy submission Matlab # 2 1 Revised 07/13/2016 LAB 2: Linear Equations and Matrix Algebra In this lab you will use Matlab to study the following topics: Solving a system of
More informationWorkload Characterization Techniques
Workload Characterization Techniques Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 Jain@cse.wustl.edu These slides are available on-line at: http://www.cse.wustl.edu/~jain/cse567-08/
More informationLinear Equation Systems Iterative Methods
Linear Equation Systems Iterative Methods Content Iterative Methods Jacobi Iterative Method Gauss Seidel Iterative Method Iterative Methods Iterative methods are those that produce a sequence of successive
More information7 Control Structures, Logical Statements
7 Control Structures, Logical Statements 7.1 Logical Statements 1. Logical (true or false) statements comparing scalars or matrices can be evaluated in MATLAB. Two matrices of the same size may be compared,
More informationVector: A series of scalars contained in a column or row. Dimensions: How many rows and columns a vector or matrix has.
ASSIGNMENT 0 Introduction to Linear Algebra (Basics of vectors and matrices) Due 3:30 PM, Tuesday, October 10 th. Assignments should be submitted via e-mail to: matlabfun.ucsd@gmail.com You can also submit
More informationCOSC2430: Programming and Data Structures Sparse Matrix Addition 2 2: Non-recursive and recursive; arrays and linked lists
COSC2430: Programming and Data Structures Sparse Matrix Addition 2 2: Non-recursive and recursive; arrays and linked lists 1 Introduction You will create a C++ program to add two sparse matrices. Sparse
More informationComputer Programming: C++
The Islamic University of Gaza Engineering Faculty Department of Computer Engineering Fall 2017 ECOM 2003 Muath i.alnabris Computer Programming: C++ Experiment #7 Arrays Part II Passing Array to a Function
More informationYEAR 11 GENERAL MATHEMATICS 2015 MATRICES TEST
YEAR 11 GENERAL MATHEMATICS 2015 MATRICES TEST Name: Skills /41 Analysis /19 TOTAL: /60 60 mins SECTION A: Multiple Choice (10 x 1 marks = 10 marks) Questions 1-6 are to be answered using the following
More informationMatrix Multiplication
Matrix Multiplication Material based on Chapter 10, Numerical Algorithms, of B. Wilkinson et al., PARALLEL PROGRAMMING. Techniques and Applications Using Networked Workstations and Parallel Computers c
More informationMatrix Multiplication. (Dynamic Programming)
Matrix Multiplication (Dynamic Programming) Matrix Multiplication: Review Suppose that A 1 is of size S 1 x S 2, and A 2 is of size S 2 x S 3. What is the time complexity of computing A 1 * A 2? What is
More informationTribhuvan University Institute of Science and Technology 2065
1CSc.102-2065 2065 Candidates are required to give their answers in their own words as for as practicable. 1. Draw the flow chart for finding largest of three numbers and write an algorithm and explain
More informationMatrix Multiplication
Matrix Multiplication CPS343 Parallel and High Performance Computing Spring 2018 CPS343 (Parallel and HPC) Matrix Multiplication Spring 2018 1 / 32 Outline 1 Matrix operations Importance Dense and sparse
More informationVectors and Matrices. Chapter 2. Linguaggio Programmazione Matlab-Simulink (2017/2018)
Vectors and Matrices Chapter 2 Linguaggio Programmazione Matlab-Simulink (2017/2018) Matrices A matrix is used to store a set of values of the same type; every value is stored in an element MATLAB stands
More informationComputer Project #2 (Matrix Operations)
Math 0280 Introduction to Matrices and Linear Algebra Fall 2006 Computer Project #2 (Matrix Operations) SCHEDULE: This assignment is due in class on Monday, October 23, 2006. One submission per group is
More informationMaximum Contiguous Subsequence Sum. Check out from SVN: MCSSRaces
Q0 Maximum Contiguous Subsequence Sum Check out from SVN: MCSSRaces Good comments: Javadoc comments for public fields and methods. Explanations of anything else that is not obvious. Good variable and method
More informationParallel Computing: Parallel Algorithm Design Examples Jin, Hai
Parallel Computing: Parallel Algorithm Design Examples Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology ! Given associative operator!! a 0! a 1! a 2!! a
More informationDeclaring and ini,alizing 2D arrays
Declaring and ini,alizing 2D arrays 4 2D Arrays (Savitch, Chapter 7.5) TOPICS Multidimensional Arrays 2D Array Allocation 2D Array Initialization TicTacToe Game // se2ng up a 2D array final int M=3, N=4;
More informationGeneral Instructions. You can use QtSpim simulator to work on these assignments.
General Instructions You can use QtSpim simulator to work on these assignments. Only one member of each group has to submit the assignment. Please Make sure that there is no duplicate submission from your
More information1 Lab 2: C Programming II
1 Lab 2: C Programming II This laboratory requires the following equipment: C compiler Matlab The laboratory duration is approximately 3 hours. Although this laboratory is not graded, we encourage you
More informationCache memories are small, fast SRAM based memories managed automatically in hardware.
Cache Memories Cache memories are small, fast SRAM based memories managed automatically in hardware. Hold frequently accessed blocks of main memory CPU looks first for data in caches (e.g., L1, L2, and
More informationBlocks, Grids, and Shared Memory
Blocks, Grids, and Shared Memory GPU Course, Fall 2012 Last week: ax+b Homework Threads, Blocks, Grids CUDA threads are organized into blocks Threads operate in SIMD(ish) manner -- each executing same
More informationHomework # 2 Due: October 6. Programming Multiprocessors: Parallelism, Communication, and Synchronization
ECE669: Parallel Computer Architecture Fall 2 Handout #2 Homework # 2 Due: October 6 Programming Multiprocessors: Parallelism, Communication, and Synchronization 1 Introduction When developing multiprocessor
More informationSpare Matrix Formats, and The Standard Template Library
Annotated slides CS319: Scientific Computing (with C++) Spare Matrix Formats, and The Standard Template Library Week 10: 9am and 4pm, 20 March 2019 1 Sparse Matrices 2 3 Compressed Column Storage 4 (Not)
More informationLectures 12 and 13 Dynamic programming: weighted interval scheduling
Lectures 12 and 13 Dynamic programming: weighted interval scheduling COMP 523: Advanced Algorithmic Techniques Lecturer: Dariusz Kowalski Lectures 12-13: Dynamic Programming 1 Overview Last week: Graph
More informationMatrix Inverse 2 ( 2) 1 = 2 1 2
Name: Matrix Inverse For Scalars, we have what is called a multiplicative identity. This means that if we have a scalar number, call it r, then r multiplied by the multiplicative identity equals r. Without
More informationGraph Theory: Starting Out
Graph Theory: Starting Out Administrivia To read: Chapter 7, Sections 1-3 (Ensley/Crawley) Problem Set 5 sent out; due Monday 12/8 in class. There will be two review days next week (Wednesday and Friday)
More informationOptimization Problems. Optimization Problems
Optimization Problems Optimization Problems We have been looking at problems such as selection and sorting. These problems don t really involve the notion of making a choice, or more specifically, the
More information1 class Lecture5 { 2 3 "Arrays" 4. Zheng-Liang Lu Java Programming 136 / 174
1 class Lecture5 { 2 3 "Arrays" 4 5 } Zheng-Liang Lu Java Programming 136 / 174 Arrays An array stores a large collection of data which is of the same type. 2 // assume the size variable exists above 3
More informationCOSC 6385 Computer Architecture - Exercises
COSC 6385 Computer Architecture - Exercises Edgar Gabriel Spring 2014 For the 1st Date: February 18 2014, 1.00pm-2.30pm Topics: discussed on the next pages You can have 3 sheets of handwritten notes you
More informationMore Complicated Recursion CMPSC 122
More Complicated Recursion CMPSC 122 Now that we've gotten a taste of recursion, we'll look at several more examples of recursion that are special in their own way. I. Example with More Involved Arithmetic
More information15-451/651: Design & Analysis of Algorithms January 26, 2015 Dynamic Programming I last changed: January 28, 2015
15-451/651: Design & Analysis of Algorithms January 26, 2015 Dynamic Programming I last changed: January 28, 2015 Dynamic Programming is a powerful technique that allows one to solve many different types
More informationMathematics 4330/5344 #1 Matlab and Numerical Approximation
David S. Gilliam Department of Mathematics Texas Tech University Lubbock, TX 79409 806 742-2566 gilliam@texas.math.ttu.edu http://texas.math.ttu.edu/~gilliam Mathematics 4330/5344 #1 Matlab and Numerical
More informationSlightly revised version of Scilab and Matrices by David Arnold, College of the Redwoods, August 17, 1998.
Scilab and Matrices Slightly revised version of Scilab and Matrices by David Arnold, College of the Redwoods, August 7, 998. In this exercise you will learn how to enter and edit matrices in Scilab. You
More informationSubmission instructions (read carefully): SS17 / Assignment 4 Instructor: Markus Püschel. ETH Zurich
263-2300-00: How To Write Fast Numerical Code Assignment 4: 120 points Due Date: Th, April 13th, 17:00 http://www.inf.ethz.ch/personal/markusp/teaching/263-2300-eth-spring17/course.html Questions: fastcode@lists.inf.ethz.ch
More information10/26/ Solving Systems of Linear Equations Using Matrices. Objectives. Matrices
6.1 Solving Systems of Linear Equations Using Matrices Objectives Write the augmented matrix for a linear system. Perform matrix row operations. Use matrices and Gaussian elimination to solve systems.
More informationLecture 6: Parallel Matrix Algorithms (part 3)
Lecture 6: Parallel Matrix Algorithms (part 3) 1 A Simple Parallel Dense Matrix-Matrix Multiplication Let A = [a ij ] n n and B = [b ij ] n n be n n matrices. Compute C = AB Computational complexity of
More informationCommunicators and Topologies: Matrix Multiplication Example
Communicators and Topologies: Matrix Multiplication Example Ned Nedialkov McMaster University Canada CS/SE 4F03 March 2016 Outline Fox s algorithm Example Implementation outline c 2013 16 Ned Nedialkov
More informationNumerical Algorithms
Chapter 10 Slide 464 Numerical Algorithms Slide 465 Numerical Algorithms In textbook do: Matrix multiplication Solving a system of linear equations Slide 466 Matrices A Review An n m matrix Column a 0,0
More informationDynamic Programming Shabsi Walfish NYU - Fundamental Algorithms Summer 2006
Dynamic Programming What is Dynamic Programming? Technique for avoiding redundant work in recursive algorithms Works best with optimization problems that have a nice underlying structure Can often be used
More information1. (a) O(log n) algorithm for finding the logical AND of n bits with n processors
1. (a) O(log n) algorithm for finding the logical AND of n bits with n processors on an EREW PRAM: See solution for the next problem. Omit the step where each processor sequentially computes the AND of
More informationCSE 202 Divide-and-conquer algorithms. Fan Chung Graham UC San Diego
CSE 22 Divide-and-conquer algorithms Fan Chung Graham UC San Diego Announcements Homework due today before the class. About homework, write your own homework, allowing oral discussion with one fixed partner.
More informationLearning objec-ves. Declaring and ini-alizing 2D arrays. Prin-ng 2D arrays. Using 2D arrays Decomposi-on of a solu-on into objects and methods
Learning objec-ves 2D Arrays (Savitch, Chapter 7.5) TOPICS Using 2D arrays Decomposi-on of a solu-on into objects and methods Multidimensional Arrays 2D Array Allocation 2D Array Initialization TicTacToe
More informationAgenda Cache memory organization and operation Chapter 6 Performance impact of caches Cache Memories
Agenda Chapter 6 Cache Memories Cache memory organization and operation Performance impact of caches The memory mountain Rearranging loops to improve spatial locality Using blocking to improve temporal
More informationObject Oriented Programming 2013/14. Final Exam June 20, 2014
Object Oriented Programming 2013/14 Final Exam June 20, 2014 Directions (read carefully): CLEARLY print your name and ID on every page. The exam contains 8 pages divided into 4 parts. Make sure you have
More informationECE 545 Fall 2013 Final Exam
ECE 545 Fall 2013 Final Exam Problem 1 Develop an ASM chart for the circuit EXAM from your Midterm Exam, described below using its A. pseudocode B. table of input/output ports C. block diagram D. interface
More information