Chapter 8. Arrays and Files
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Functional Languages
Functional Programming Languages (FPL) 1. Definitions................................................................... 2 2. Applications ................................................................ 2 3. Examples..................................................................... 3 4. FPL Characteristics:.................................................... 3 5. Lambda calculus (LC)................................................. 4 6. Functions in FPLs ....................................................... 7 7. Modern functional languages...................................... 9 8. Scheme overview...................................................... 11 8.1. Get your own Scheme from MIT...................... 11 8.2. General overview.............................................. 11 8.3. Data Typing ...................................................... 12 8.4. Comments ......................................................... 12 8.5. Recursion Instead of Iteration........................... 13 8.6. Evaluation ......................................................... 14 8.7. Storing and using Scheme code ........................ 14 8.8. Variables ........................................................... 15 8.9. Data types.......................................................... 16 8.10. Arithmetic functions ......................................... 17 8.11. Selection functions............................................ 18 8.12. Iteration............................................................. 23 8.13. Defining functions ........................................... -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
S-Algol Reference Manual Ron Morrison
S-algol Reference Manual Ron Morrison University of St. Andrews, North Haugh, Fife, Scotland. KY16 9SS CS/79/1 1 Contents Chapter 1. Preface 2. Syntax Specification 3. Types and Type Rules 3.1 Universe of Discourse 3.2 Type Rules 4. Literals 4.1 Integer Literals 4.2 Real Literals 4.3 Boolean Literals 4.4 String Literals 4.5 Pixel Literals 4.6 File Literal 4.7 pntr Literal 5. Primitive Expressions and Operators 5.1 Boolean Expressions 5.2 Comparison Operators 5.3 Arithmetic Expressions 5.4 Arithmetic Precedence Rules 5.5 String Expressions 5.6 Picture Expressions 5.7 Pixel Expressions 5.8 Precedence Table 5.9 Other Expressions 6. Declarations 6.1 Identifiers 6.2 Variables, Constants and Declaration of Data Objects 6.3 Sequences 6.4 Brackets 6.5 Scope Rules 7. Clauses 7.1 Assignment Clause 7.2 if Clause 7.3 case Clause 7.4 repeat ... while ... do ... Clause 7.5 for Clause 7.6 abort Clause 8. Procedures 8.1 Declarations and Calls 8.2 Forward Declarations 2 9. Aggregates 9.1 Vectors 9.1.1 Creation of Vectors 9.1.2 upb and lwb 9.1.3 Indexing 9.1.4 Equality and Equivalence 9.2 Structures 9.2.1 Creation of Structures 9.2.2 Equality and Equivalence 9.2.3 Indexing 9.3 Images 9.3.1 Creation of Images 9.3.2 Indexing 9.3.3 Depth Selection 9.3.4 Equality and Equivalence 10. Input and Output 10.1 Input 10.2 Output 10.3 i.w, s.w and r.w 10.4 End of File 11. -
Data Structure Invariants
Lecture 8 Notes Data Structures 15-122: Principles of Imperative Computation (Spring 2016) Frank Pfenning, André Platzer, Rob Simmons 1 Introduction In this lecture we introduce the idea of imperative data structures. So far, the only interfaces we’ve used carefully are pixels and string bundles. Both of these interfaces had the property that, once we created a pixel or a string bundle, we weren’t interested in changing its contents. In this lecture, we’ll talk about an interface that mimics the arrays that are primitively available in C0. To implement this interface, we’ll need to round out our discussion of types in C0 by discussing pointers and structs, two great tastes that go great together. We will discuss using contracts to ensure that pointer accesses are safe. Relating this to our learning goals, we have Computational Thinking: We illustrate the power of abstraction by con- sidering both the client-side and library-side of the interface to a data structure. Algorithms and Data Structures: The abstract arrays will be one of our first examples of abstract datatypes. Programming: Introduction of structs and pointers, use and design of in- terfaces. 2 Structs So far in this course, we’ve worked with five different C0 types — int, bool, char, string, and arrays t[] (there is a array type t[] for every type t). The character, Boolean and integer values that we manipulate, store LECTURE NOTES Data Structures L8.2 locally, and pass to functions are just the values themselves. For arrays (and strings), the things we store in assignable variables or pass to functions are addresses, references to the place where the data stored in the array can be accessed. -
Using Machine Learning to Improve Dense and Sparse Matrix Multiplication Kernels
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2019 Using machine learning to improve dense and sparse matrix multiplication kernels Brandon Groth Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Applied Mathematics Commons, and the Computer Sciences Commons Recommended Citation Groth, Brandon, "Using machine learning to improve dense and sparse matrix multiplication kernels" (2019). Graduate Theses and Dissertations. 17688. https://lib.dr.iastate.edu/etd/17688 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Using machine learning to improve dense and sparse matrix multiplication kernels by Brandon Micheal Groth A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Applied Mathematics Program of Study Committee: Glenn R. Luecke, Major Professor James Rossmanith Zhijun Wu Jin Tian Kris De Brabanter The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2019 Copyright c Brandon Micheal Groth, 2019. All rights reserved. ii DEDICATION I would like to dedicate this thesis to my wife Maria and our puppy Tiger. -
Verification-Aware Opencl Based Read Mapper for Heterogeneous
JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 CORAL: Verification-aware OpenCL based Read Mapper for Heterogeneous Systems Sidharth Maheshwari, Venkateshwarlu Y. Gudur, Rishad Shafik, Senion Member, IEEE, Ian Wilson, Alex Yakovlev, Fellow, IEEE, and Amit Acharyya, Member, IEEE Abstract—Genomics has the potential to transform medicine from reactive to a personalized, predictive, preventive and participatory (P4) form. Being a Big Data application with continuously increasing rate of data production, the computational costs of genomics have become a daunting challenge. Most modern computing systems are heterogeneous consisting of various combinations of computing resources, such as CPUs, GPUs and FPGAs. They require platform-specific software and languages to program making their simultaneous operation challenging. Existing read mappers and analysis tools in the whole genome sequencing (WGS) pipeline do not scale for such heterogeneity. Additionally, the computational cost of mapping reads is high due to expensive dynamic programming based verification, where optimized implementations are already available. Thus, improvement in filtration techniques is needed to reduce verification overhead. To address the aforementioned limitations with regards to the mapping element of the WGS pipeline, we propose a Cross-platfOrm Read mApper using opencL (CORAL). CORAL is capable of executing on heterogeneous devices/platforms simultaneously. It can reduce computational time by suitably distributing the workload without any additional programming effort. We showcase this on a quadcore Intel CPU along with two Nvidia GTX 590 GPUs, distributing the workload judiciously to achieve up to 2× speedup compared to when only CPUs are used. To reduce the verification overhead, CORAL dynamically adapts k-mer length during filtration. -
Applying Front End Compiler Process to Parse Polynomials in Parallel
Western University Scholarship@Western Electronic Thesis and Dissertation Repository 12-16-2020 10:30 AM Applying Front End Compiler Process to Parse Polynomials in Parallel Amha W. Tsegaye, The University of Western Ontario Supervisor: Dr. Marc Moreno Maza, The University of Western Ontario A thesis submitted in partial fulfillment of the equirr ements for the Master of Science degree in Computer Science © Amha W. Tsegaye 2020 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Computer Sciences Commons, and the Mathematics Commons Recommended Citation Tsegaye, Amha W., "Applying Front End Compiler Process to Parse Polynomials in Parallel" (2020). Electronic Thesis and Dissertation Repository. 7592. https://ir.lib.uwo.ca/etd/7592 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact [email protected]. Abstract Parsing large expressions, in particular large polynomial expressions, is an important task for computer algebra systems. Despite of the apparent simplicity of the problem, its efficient software implementation brings various challenges. Among them is the fact that this is a memory bound application for which a multi-threaded implementation is necessarily limited by the characteristics of the memory organization of supporting hardware. In this thesis, we design, implement and experiment with a multi-threaded parser for large polynomial expressions. We extract parallelism by splitting the input character string, into meaningful sub-strings that can be parsed concurrently before being merged into a single polynomial. -
Exploratory Large Scale Graph Analytics in Arkouda 59 2 60 3 61 4 Zhihui Du,Oliver Alvarado Rodriguez and Michael Merrill and William Reus 62 5 David A
1 Exploratory Large Scale Graph Analytics in Arkouda 59 2 60 3 61 4 Zhihui Du,Oliver Alvarado Rodriguez and Michael Merrill and William Reus 62 5 David A. Bader [email protected] 63 6 {zhihui.du,oaa9,bader}@njit.edu [email protected] 64 7 New Jersey Institute of Technology Department of Defense 65 8 Newark, New Jersey, USA USA 66 9 67 10 ABSTRACT 1 INTRODUCTION 68 11 Exploratory graph analytics helps maximize the informational value A graph is a well defined mathematical model to formulate the rela- 69 12 for a graph. However, the increasing graph size makes it impossi- tionship between different objects and is widely used in numerous 70 13 ble for existing popular exploratory data analysis tools to handle domains such as social sciences, biological systems, and informa- 71 14 dozens-of-terabytes or even larger data sets in the memory of a tion systems. The edge distributions of many large scale real world 72 15 common laptop/personal computer. Arkouda is a framework un- problems tend to follow a power-law distribution [1, 11, 26]. Dense 73 16 der early-development that brings together the productivity of graph data structures and algorithms will consume much more 74 17 Python at the user side with the high-performance of Chapel at memory and cannot analyze very large sparse graphs efficiently. 75 18 the server side. In this paper, the preliminary work on overcoming Therefore, parallel algorithms for sparse graphs [23] have become 76 19 the memory limit and high performance computing coding road- an important research topic to efficiently analyze the large and 77 20 block for high level Python users to perform large graph analysis sparse graphs from different real-world problems. -
Functional Programming Functional Vs. Imperative Referential
Functional vs. Imperative Referential transparency Imperative programming concerned with “how.” The main (good) property of functional programming is Functional Programming referential transparency. Functional programming concerned with “what.” COMS W4115 Every expression denotes a single value. Based on the mathematics of the lambda calculus Prof. Stephen A. Edwards (Church as opposed to Turing). The value cannot be changed by evaluating an expression Spring 2003 or by sharing it between different parts of the program. “Programming without variables” Columbia University No references to global data; there is no global data. Department of Computer Science It is inherently concise, elegant, and difficult to create subtle bugs in. There are no side-effects, unlike in referentially opaque Original version by Prof. Simon Parsons languages. It’s a cult: once you catch the functional bug, you never escape. The Joy of Pascal Strange behavior Variables program example(output) This prints 5 then 4. At the heart of the “problem” is fact that the global data var flag: boolean; flag affects the value of f. Odd since you expect function f(n:int): int In particular begin f (1) + f (2) = f (2) + f (1) if flag then f := n flag := not flag else f := 2*n; gives the offending behavior flag := not flag Mathematical functions only depend on their inputs end They have no memory Eliminating assignments eliminates such problems. begin In functional languages, variables not names for storage. flag := true; What does this print? Instead, they’re names that refer to particular values. writeln(f(1) + f(2)); writeln(f(2) + f(1)); Think of them as not very variables. -
The Building Blocks: Data Types, Literals, and Variables
Quigley.book Page 31 Thursday, May 22, 2003 3:19 PM chapter 3 The Building Blocks: Data Types, Literals, and Variables 3.1 Data Types A program can do many things, including calculations, sorting names, preparing phone lists, displaying images, validating forms, ad infinitum. But in order to do anything, the program works with the data that is given to it. Data types specify what kind of data, such as numbers and characters, can be stored and manipulated within a program. Java- Script supports a number of fundamental data types. These types can be broken down into two categories, primitive data types and composite data types. 3.1.1 Primitive Data Types Primitive data types are the simplest building blocks of a program. They are types that can be assigned a single literal value such as the number 5.7, or a string of characters such as "hello". JavaScript supports three core or basic data types: • numeric • string • Boolean In addition to the three core data types, there are two other special types that consist of a single value: • null • undefined Numeric Literals. JavaScript supports both integers and floating-point numbers. Integers are whole numbers and do not contain a decimal point; e.g., 123 and –6. Inte- gers can be expressed in decimal (base 10), octal (base 8), and hexadecimal (base 16), and are either positive or negative values. See Example 3.1. 31 Quigley.book Page 32 Thursday, May 22, 2003 3:19 PM 32 Chapter 3 • The Building Blocks: Data Types, Literals, and Variables Floating-point numbers are fractional numbers such as 123.56 or –2.5. -
Array Data Structure
© 2014 IJIRT | Volume 1 Issue 6 | ISSN : 2349-6002 ARRAY DATA STRUCTURE Isha Batra, Divya Raheja Information Technology Dronacharya College Of Engineering, Farukhnagar,Gurgaon Abstract- In computer science, an array data structure valid index tuples and the addresses of the elements or simply an array is a data structure consisting of a (and hence the element addressing formula) are collection of elements (values or variables), each usually but not always fixed while the array is in use. identified by at least one array index or key. An array is The term array is often used to mean array data type, stored so that the position of each element can be a kind of data type provided by most high-level computed from its index tuple by a mathematical formula. The simplest type of data structure is a linear programming languages that consists of a collection array, also called one-dimensional array. For example, of values or variables that can be selected by one or an array of 10 32-bit integer variables, with indices 0 more indices computed at run-time. Array types are through 9, may be stored as 10 words at memory often implemented by array structures; however, in addresses 2000, 2004, 2008, … 2036, so that the element some languages they may be implemented by hash with index i has the address 2000 + 4 × i. The term array tables, linked lists, search trees, or other data is often used to mean array data type, a kind of data structures. The term is also used, especially in the type provided by most high-level programming description of algorithms, to mean associative array languages that consists of a collection of values or or "abstract array", a theoretical computer science variables that can be selected by one or more indices computed at run-time. -
View of This Work
INFORMATION TO USERS The most advanced technology has been used to photo graph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are re produced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. These are also available as one exposure on a standard 35mm slide or as a 17" x 23" black and white photographic print for an additional charge. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. University Microfilms International A Bell & Howell Information Company 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 313/761-4700 800/521-0600 Order Number 8824450 Performance evaluation of RISC-based architectures for image processing Al-Ghitany, Nashat El-Khameesy, Ph.D.