CSE 307: Principles of Programming Languages Classes and Inheritance
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Application of TRIE Data Structure and Corresponding Associative Algorithms for Process Optimization in GRID Environment
Application of TRIE data structure and corresponding associative algorithms for process optimization in GRID environment V. V. Kashanskya, I. L. Kaftannikovb South Ural State University (National Research University), 76, Lenin prospekt, Chelyabinsk, 454080, Russia E-mail: a [email protected], b [email protected] Growing interest around different BOINC powered projects made volunteer GRID model widely used last years, arranging lots of computational resources in different environments. There are many revealed problems of Big Data and horizontally scalable multiuser systems. This paper provides an analysis of TRIE data structure and possibilities of its application in contemporary volunteer GRID networks, including routing (L3 OSI) and spe- cialized key-value storage engine (L7 OSI). The main goal is to show how TRIE mechanisms can influence de- livery process of the corresponding GRID environment resources and services at different layers of networking abstraction. The relevance of the optimization topic is ensured by the fact that with increasing data flow intensi- ty, the latency of the various linear algorithm based subsystems as well increases. This leads to the general ef- fects, such as unacceptably high transmission time and processing instability. Logically paper can be divided into three parts with summary. The first part provides definition of TRIE and asymptotic estimates of corresponding algorithms (searching, deletion, insertion). The second part is devoted to the problem of routing time reduction by applying TRIE data structure. In particular, we analyze Cisco IOS switching services based on Bitwise TRIE and 256 way TRIE data structures. The third part contains general BOINC architecture review and recommenda- tions for highly-loaded projects. -
C Programming: Data Structures and Algorithms
C Programming: Data Structures and Algorithms An introduction to elementary programming concepts in C Jack Straub, Instructor Version 2.07 DRAFT C Programming: Data Structures and Algorithms, Version 2.07 DRAFT C Programming: Data Structures and Algorithms Version 2.07 DRAFT Copyright © 1996 through 2006 by Jack Straub ii 08/12/08 C Programming: Data Structures and Algorithms, Version 2.07 DRAFT Table of Contents COURSE OVERVIEW ........................................................................................ IX 1. BASICS.................................................................................................... 13 1.1 Objectives ...................................................................................................................................... 13 1.2 Typedef .......................................................................................................................................... 13 1.2.1 Typedef and Portability ............................................................................................................. 13 1.2.2 Typedef and Structures .............................................................................................................. 14 1.2.3 Typedef and Functions .............................................................................................................. 14 1.3 Pointers and Arrays ..................................................................................................................... 16 1.4 Dynamic Memory Allocation ..................................................................................................... -
Improving Virtual Function Call Target Prediction Via Dependence-Based Pre-Computation
Improving Virtual Function Call Target Prediction via Dependence-Based Pre-Computation Amir Roth, Andreas Moshovos and Gurindar S. Sohi Computer Sciences Department University of Wisconsin-Madison 1210 W Dayton Street, Madison, WI 53706 {amir, moshovos, sohi}@cs.wisc.edu Abstract In this initial work, we concentrate on pre-computing virtual function call (v-call) targets. The v-call mecha- We introduce dependence-based pre-computation as a nism enables code reuse by allowing a single static func- complement to history-based target prediction schemes. tion call to transparently invoke one of multiple function We present pre-computation in the context of virtual implementations based on run-time type information. function calls (v-calls), a class of control transfers that As shown in Table 1, v-calls are currently used in is becoming increasingly important and has resisted object-oriented programs with frequencies ranging from conventional prediction. Our proposed technique 1 in 200 to 1000 instructions (4 to 20 times fewer than dynamically identifies the sequence of operations that direct calls and 30 to 150 times fewer than conditional computes a v-call’s target. When the first instruction in branches). V-call importance will increase as object-ori- such a sequence is encountered, a small execution ented languages and methodologies gain popularity. For engine speculatively and aggressively pre-executes the now, v-calls make an ideal illustration vehicle for pre- rest. The pre-computed target is stored and subse- computation techniques since their targets are both diffi- quently used when a prediction needs to be made. We cult to predict and easy to pre-compute. -
Abstract Data Types
Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code. -
Design and Implementation of Generics for the .NET Common Language Runtime
Design and Implementation of Generics for the .NET Common Language Runtime Andrew Kennedy Don Syme Microsoft Research, Cambridge, U.K. fakeÒÒ¸d×ÝÑeg@ÑicÖÓ×ÓfغcÓÑ Abstract cally through an interface definition language, or IDL) that is nec- essary for language interoperation. The Microsoft .NET Common Language Runtime provides a This paper describes the design and implementation of support shared type system, intermediate language and dynamic execution for parametric polymorphism in the CLR. In its initial release, the environment for the implementation and inter-operation of multiple CLR has no support for polymorphism, an omission shared by the source languages. In this paper we extend it with direct support for JVM. Of course, it is always possible to “compile away” polymor- parametric polymorphism (also known as generics), describing the phism by translation, as has been demonstrated in a number of ex- design through examples written in an extended version of the C# tensions to Java [14, 4, 6, 13, 2, 16] that require no change to the programming language, and explaining aspects of implementation JVM, and in compilers for polymorphic languages that target the by reference to a prototype extension to the runtime. JVM or CLR (MLj [3], Haskell, Eiffel, Mercury). However, such Our design is very expressive, supporting parameterized types, systems inevitably suffer drawbacks of some kind, whether through polymorphic static, instance and virtual methods, “F-bounded” source language restrictions (disallowing primitive type instanti- type parameters, instantiation at pointer and value types, polymor- ations to enable a simple erasure-based translation, as in GJ and phic recursion, and exact run-time types. -
Data Structures, Buffers, and Interprocess Communication
Data Structures, Buffers, and Interprocess Communication We’ve looked at several examples of interprocess communication involving the transfer of data from one process to another process. We know of three mechanisms that can be used for this transfer: - Files - Shared Memory - Message Passing The act of transferring data involves one process writing or sending a buffer, and another reading or receiving a buffer. Most of you seem to be getting the basic idea of sending and receiving data for IPC… it’s a lot like reading and writing to a file or stdin and stdout. What seems to be a little confusing though is HOW that data gets copied to a buffer for transmission, and HOW data gets copied out of a buffer after transmission. First… let’s look at a piece of data. typedef struct { char ticker[TICKER_SIZE]; double price; } item; . item next; . The data we want to focus on is “next”. “next” is an object of type “item”. “next” occupies memory in the process. What we’d like to do is send “next” from processA to processB via some kind of IPC. IPC Using File Streams If we were going to use good old C++ filestreams as the IPC mechanism, our code would look something like this to write the file: // processA is the sender… ofstream out; out.open(“myipcfile”); item next; strcpy(next.ticker,”ABC”); next.price = 55; out << next.ticker << “ “ << next.price << endl; out.close(); Notice that we didn’t do this: out << next << endl; Why? Because the “<<” operator doesn’t know what to do with an object of type “item”. -
Subtyping Recursive Types
ACM Transactions on Programming Languages and Systems, 15(4), pp. 575-631, 1993. Subtyping Recursive Types Roberto M. Amadio1 Luca Cardelli CNRS-CRIN, Nancy DEC, Systems Research Center Abstract We investigate the interactions of subtyping and recursive types, in a simply typed λ-calculus. The two fundamental questions here are whether two (recursive) types are in the subtype relation, and whether a term has a type. To address the first question, we relate various definitions of type equivalence and subtyping that are induced by a model, an ordering on infinite trees, an algorithm, and a set of type rules. We show soundness and completeness between the rules, the algorithm, and the tree semantics. We also prove soundness and a restricted form of completeness for the model. To address the second question, we show that to every pair of types in the subtype relation we can associate a term whose denotation is the uniquely determined coercion map between the two types. Moreover, we derive an algorithm that, when given a term with implicit coercions, can infer its least type whenever possible. 1This author's work has been supported in part by Digital Equipment Corporation and in part by the Stanford-CNR Collaboration Project. Page 1 Contents 1. Introduction 1.1 Types 1.2 Subtypes 1.3 Equality of Recursive Types 1.4 Subtyping of Recursive Types 1.5 Algorithm outline 1.6 Formal development 2. A Simply Typed λ-calculus with Recursive Types 2.1 Types 2.2 Terms 2.3 Equations 3. Tree Ordering 3.1 Subtyping Non-recursive Types 3.2 Folding and Unfolding 3.3 Tree Expansion 3.4 Finite Approximations 4. -
Valloy – Virtual Functions Meet a Relational Language
VAlloy – Virtual Functions Meet a Relational Language Darko Marinov and Sarfraz Khurshid MIT Laboratory for Computer Science 200 Technology Square Cambridge, MA 02139 USA {marinov,khurshid}@lcs.mit.edu Abstract. We propose VAlloy, a veneer onto the first order, relational language Alloy. Alloy is suitable for modeling structural properties of object-oriented software. However, Alloy lacks support for dynamic dis- patch, i.e., function invocation based on actual parameter types. VAlloy introduces virtual functions in Alloy, which enables intuitive modeling of inheritance. Models in VAlloy are automatically translated into Alloy and can be automatically checked using the existing Alloy Analyzer. We illustrate the use of VAlloy by modeling object equality, such as in Java. We also give specifications for a part of the Java Collections Framework. 1 Introduction Object-oriented design and object-oriented programming have become predom- inant software methodologies. An essential feature of object-oriented languages is inheritance. It allows a (sub)class to inherit variables and methods from su- perclasses. Some languages, such as Java, only support single inheritance for classes. Subclasses can override some methods, changing the behavior inherited from superclasses. We use C++ term virtual functions to refer to methods that can be overridden. Virtual functions are dynamically dispatched—the actual function to invoke is selected based on the dynamic types of parameters. Java only supports single dynamic dispatch, i.e., the function is selected based only on the type of the receiver object. Alloy [9] is a first order, declarative language based on relations. Alloy is suitable for specifying structural properties of software. Alloy specifications can be analyzed automatically using the Alloy Analyzer (AA) [8]. -
4 Hash Tables and Associative Arrays
4 FREE Hash Tables and Associative Arrays If you want to get a book from the central library of the University of Karlsruhe, you have to order the book in advance. The library personnel fetch the book from the stacks and deliver it to a room with 100 shelves. You find your book on a shelf numbered with the last two digits of your library card. Why the last digits and not the leading digits? Probably because this distributes the books more evenly among the shelves. The library cards are numbered consecutively as students sign up, and the University of Karlsruhe was founded in 1825. Therefore, the students enrolled at the same time are likely to have the same leading digits in their card number, and only a few shelves would be in use if the leadingCOPY digits were used. The subject of this chapter is the robust and efficient implementation of the above “delivery shelf data structure”. In computer science, this data structure is known as a hash1 table. Hash tables are one implementation of associative arrays, or dictio- naries. The other implementation is the tree data structures which we shall study in Chap. 7. An associative array is an array with a potentially infinite or at least very large index set, out of which only a small number of indices are actually in use. For example, the potential indices may be all strings, and the indices in use may be all identifiers used in a particular C++ program.Or the potential indices may be all ways of placing chess pieces on a chess board, and the indices in use may be the place- ments required in the analysis of a particular game. -
Inheritance & Polymorphism
6.088 Intro to C/C++ Day 5: Inheritance & Polymorphism Eunsuk Kang & JeanYang In the last lecture... Objects: Characteristics & responsibilities Declaring and defining classes in C++ Fields, methods, constructors, destructors Creating & deleting objects on stack/heap Representation invariant Today’s topics Inheritance Polymorphism Abstract base classes Inheritance Types A class defines a set of objects, or a type people at MIT Types within a type Some objects are distinct from others in some ways MIT professors MIT students people at MIT Subtype MIT professor and student are subtypes of MIT people MIT professors MIT students people at MIT Type hierarchy MIT Person extends extends Student Professor What characteristics/behaviors do people at MIT have in common? Type hierarchy MIT Person extends extends Student Professor What characteristics/behaviors do people at MIT have in common? �name, ID, address �change address, display profile Type hierarchy MIT Person extends extends Student Professor What things are special about students? �course number, classes taken, year �add a class taken, change course Type hierarchy MIT Person extends extends Student Professor What things are special about professors? �course number, classes taught, rank (assistant, etc.) �add a class taught, promote Inheritance A subtype inherits characteristics and behaviors of its base type. e.g. Each MIT student has Characteristics: Behaviors: name display profile ID change address address add a class taken course number change course classes taken year Base type: MITPerson -
Parametric Polymorphism Parametric Polymorphism
Parametric Polymorphism Parametric Polymorphism • is a way to make a language more expressive, while still maintaining full static type-safety (every Haskell expression has a type, and types are all checked at compile-time; programs with type errors will not even compile) • using parametric polymorphism, a function or a data type can be written generically so that it can handle values identically without depending on their type • such functions and data types are called generic functions and generic datatypes Polymorphism in Haskell • Two kinds of polymorphism in Haskell – parametric and ad hoc (coming later!) • Both kinds involve type variables, standing for arbitrary types. • Easy to spot because they start with lower case letters • Usually we just use one letter on its own, e.g. a, b, c • When we use a polymorphic function we will usually do so at a specific type, called an instance. The process is called instantiation. Identity function Consider the identity function: id x = x Prelude> :t id id :: a -> a It does not do anything with the input other than returning it, therefore it places no constraints on the input's type. Prelude> :t id id :: a -> a Prelude> id 3 3 Prelude> id "hello" "hello" Prelude> id 'c' 'c' Polymorphic datatypes • The identity function is the simplest possible polymorphic function but not very interesting • Most useful polymorphic functions involve polymorphic types • Notation – write the name(s) of the type variable(s) used to the left of the = symbol Maybe data Maybe a = Nothing | Just a • a is the type variable • When we instantiate a to something, e.g. -
INF 102 CONCEPTS of PROG. LANGS Type Systems
INF 102 CONCEPTS OF PROG. LANGS Type Systems Instructors: James Jones Copyright © Instructors. What is a Data Type? • A type is a collection of computational entities that share some common property • Programming languages are designed to help programmers organize computational constructs and use them correctly. Many programming languages organize data and computations into collections called types. • Some examples of types are: o the type Int of integers o the type (Int→Int) of functions from integers to integers Why do we need them? • Consider “untyped” universes: • Bit string in computer memory • λ-expressions in λ calculus • Sets in set theory • “untyped” = there’s only 1 type • Types arise naturally to categorize objects according to patterns of use • E.g. all integer numbers have same set of applicable operations Use of Types • Identifying and preventing meaningless errors in the program o Compile-time checking o Run-time checking • Program Organization and documentation o Separate types for separate concepts o Indicates intended use declared identifiers • Supports Optimization o Short integers require fewer bits o Access record component by known offset Type Errors • A type error occurs when a computational entity, such as a function or a data value, is used in a manner that is inconsistent with the concept it represents • Languages represent values as sequences of bits. A "type error" occurs when a bit sequence written for one type is used as a bit sequence for another type • A simple example can be assigning a string to an integer