Data Structures, Buffers, and Interprocess Communication
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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. -
Create Union Variables Difference Between Unions and Structures
Union is a user-defined data type similar to a structure in C programming. How to define a union? We use union keyword to define unions. Here's an example: union car { char name[50]; int price; }; The above code defines a user define data type union car. Create union variables When a union is defined, it creates a user-define type. However, no memory is allocated. To allocate memory for a given union type and work with it, we need to create variables. Here's how we create union variables: union car { char name[50]; int price; } car1, car2, *car3; In both cases, union variables car1, car2, and a union pointer car3 of union car type are created. How to access members of a union? We use . to access normal variables of a union. To access pointer variables, we use ->operator. In the above example, price for car1 can be accessed using car1.price price for car3 can be accessed using car3->price Difference between unions and structures Let's take an example to demonstrate the difference between unions and structures: #include <stdio.h> union unionJob { //defining a union char name[32]; float salary; int workerNo; } uJob; struct structJob { char name[32]; float salary; int workerNo; } sJob; main() { printf("size of union = %d bytes", sizeof(uJob)); printf("\nsize of structure = %d bytes", sizeof(sJob)); } Output size of union = 32 size of structure = 40 Why this difference in size of union and structure variables? The size of structure variable is 40 bytes. It's because: size of name[32] is 32 bytes size of salary is 4 bytes size of workerNo is 4 bytes However, the size of union variable is 32 bytes. -
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 ..................................................................................................... -
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. -
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. -
Bits, Bytes and Integers ‘
Bits, Bytes and Integers ‘- Karthik Dantu Ethan Blanton Computer Science and Engineering University at Buffalo [email protected] 1 Karthik Dantu Administrivia • PA1 due this Friday – test early and often! We cannot help everyone on Friday! Don’t expect answers on Piazza into the night and early morning • Avoid using actual numbers (80, 24 etc.) – use macros! • Lab this week is on testing ‘- • Programming best practices • Lab Exam – four students have already failed class! Lab exams are EXAMS – no using the Internet, submitting solutions from dorm, home Please don’t give code/exam to friends – we will know! 2 Karthik Dantu Everything is Bits • Each bit is 0 or 1 • By encoding/interpreting sets of bits in various ways Computers determine what to do (instructions) … and represent and manipulate numbers, sets, strings, etc… • Why bits? Electronic Implementation‘- Easy to store with bistable elements Reliably transmitted on noisy and inaccurate wires 0 1 0 1.1V 0.9V 0.2V 0.0V 3 Karthik Dantu Memory as Bytes • To the computer, memory is just bytes • Computer doesn’t know data types • Modern processor can only manipulate: Integers (Maybe only single bits) Maybe floating point numbers ‘- … repeat ! • Everything else is in software 4 Karthik Dantu Reminder: Computer Architecture ‘- 5 Karthik Dantu Buses • Each bus has a width, which is literally the number of wires it has • Each wire transmits one bit per transfer • Each bus transfer is of that width, though some bits might be ignored • Therefore, memory has a word size from‘-the viewpoint of -
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. -
CSE 307: Principles of Programming Languages Classes and Inheritance
OOP Introduction Type & Subtype Inheritance Overloading and Overriding CSE 307: Principles of Programming Languages Classes and Inheritance R. Sekar 1 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Topics 1. OOP Introduction 3. Inheritance 2. Type & Subtype 4. Overloading and Overriding 2 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Section 1 OOP Introduction 3 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding OOP (Object Oriented Programming) So far the languages that we encountered treat data and computation separately. In OOP, the data and computation are combined into an “object”. 4 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP more convenient: collects related information together, rather than distributing it. Example: C++ iostream class collects all I/O related operations together into one central place. Contrast with C I/O library, which consists of many distinct functions such as getchar, printf, scanf, sscanf, etc. centralizes and regulates access to data. If there is an error that corrupts object data, we need to look for the error only within its class Contrast with C programs, where access/modification code is distributed throughout the program 5 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP (Continued) Promotes reuse. by separating interface from implementation. We can replace the implementation of an object without changing client code. Contrast with C, where the implementation of a data structure such as a linked list is integrated into the client code by permitting extension of new objects via inheritance. Inheritance allows a new class to reuse the features of an existing class. -
Data Structures Using “C”
DATA STRUCTURES USING “C” DATA STRUCTURES USING “C” LECTURE NOTES Prepared by Dr. Subasish Mohapatra Department of Computer Science and Application College of Engineering and Technology, Bhubaneswar Biju Patnaik University of Technology, Odisha SYLLABUS BE 2106 DATA STRUCTURE (3-0-0) Module – I Introduction to data structures: storage structure for arrays, sparse matrices, Stacks and Queues: representation and application. Linked lists: Single linked lists, linked list representation of stacks and Queues. Operations on polynomials, Double linked list, circular list. Module – II Dynamic storage management-garbage collection and compaction, infix to post fix conversion, postfix expression evaluation. Trees: Tree terminology, Binary tree, Binary search tree, General tree, B+ tree, AVL Tree, Complete Binary Tree representation, Tree traversals, operation on Binary tree-expression Manipulation. Module –III Graphs: Graph terminology, Representation of graphs, path matrix, BFS (breadth first search), DFS (depth first search), topological sorting, Warshall’s algorithm (shortest path algorithm.) Sorting and Searching techniques – Bubble sort, selection sort, Insertion sort, Quick sort, merge sort, Heap sort, Radix sort. Linear and binary search methods, Hashing techniques and hash functions. Text Books: 1. Gilberg and Forouzan: “Data Structure- A Pseudo code approach with C” by Thomson publication 2. “Data structure in C” by Tanenbaum, PHI publication / Pearson publication. 3. Pai: ”Data Structures & Algorithms; Concepts, Techniques & Algorithms -
1 Abstract Data Types, Cost Specifications, and Data Structures
Parallel and Sequential Data Structures and Algorithms — Lecture 5 15-210 (Fall 2012) Lecture 5 — Data Abstraction and Sequences I Parallel and Sequential Data Structures and Algorithms, 15-210 (Fall 2012) Lectured by Guy Blelloch — 11 Septermber 2012 Material in this lecture: - Relationship between ADTs, cost specifications and data structures. - The sequence ADT - The scan operation: examples and implementation - Using contraction : an algorithmic technique 1 Abstract Data Types, Cost Specifications, and Data Structures So far in class we have defined several “problems” and discussed algorithms for solving them. The idea is that the problem is an abstract definition of what we want in terms of a function specification, and the algorithms are particular ways to solve/implement the problem. In addition to abstract functions we also often need to define abstractions over data. In such an abstraction we define a set of functions (abstractly) over a common data type. As mentioned in the first lecture, we will refer to the abstractions as abstract data types and their implementations as data structures. An example of an abstract data type, or ADT, you should have seen before is a priority queue. Let’s consider a slight extension where in addition to insert, and deleteMin, we will add a function that joins two priority queues into a single one. For historical reasons, we will call such a join a meld, and the ADT a "meldable priority queue". As with a problem, we like to have a formal definition of the abstract data type. Definition 1.1. Given a totally -
A Metaobject Protocol for Fault-Tolerant CORBA Applications
A Metaobject Protocol for Fault-Tolerant CORBA Applications Marc-Olivier Killijian*, Jean-Charles Fabre*, Juan-Carlos Ruiz-Garcia*, Shigeru Chiba** *LAAS-CNRS, 7 Avenue du Colonel Roche **Institute of Information Science and 31077 Toulouse cedex, France Electronics, University of Tsukuba, Tennodai, Tsukuba, Ibaraki 305-8573, Japan Abstract The corner stone of a fault-tolerant reflective The use of metalevel architectures for the architecture is the MOP. We thus propose a special implementation of fault-tolerant systems is today very purpose MOP to address the problems of general-purpose appealing. Nevertheless, all such fault-tolerant systems ones. We show that compile-time reflection is a good have used a general-purpose metaobject protocol (MOP) approach for developing a specialized runtime MOP. or are based on restricted reflective features of some The definition and the implementation of an object-oriented language. According to our past appropriate runtime metaobject protocol for implementing experience, we define in this paper a suitable metaobject fault tolerance into CORBA applications is the main protocol, called FT-MOP for building fault-tolerant contribution of the work reported in this paper. This systems. We explain how to realize a specialized runtime MOP, called FT-MOP (Fault Tolerance - MetaObject MOP using compile-time reflection. This MOP is CORBA Protocol), is sufficiently general to be used for other aims compliant: it enables the execution and the state evolution (mobility, adaptability, migration, security). FT-MOP of CORBA objects to be controlled and enables the fault provides a mean to attach dynamically fault tolerance tolerance metalevel to be developed as CORBA software. strategies to CORBA objects as CORBA metaobjects, enabling thus these strategies to be implemented as 1 . -
Fixed-Size Integer Types Bit Manipulation Integer Types
SISTEMI EMBEDDED AA 2012/2013 Fixed-size integer types Bit Manipulation Integer types • 2 basic integer types: char, int • and some type-specifiers: – sign: signed, unsigned – size: short, long • The actual size of an integer type depends on the compiler implementation – sizeof(type) returns the size (in number of bytes) used to represent the type argument – sizeof(char) ≤ sizeof(short) ≤ sizeof(int) ≤ sizeof(long)... ≤ sizeof(long long) Fixed-size integers (1) • In embedded system programming integer size is important – Controlling minimum and maximum values that can be stored in a variable – Increasing efficiency in memory utilization – Managing peripheral registers • To increase software portability, fixed-size integer types can be defined in a header file using the typedef keyword Fixed-size integers (2) • C99 update of the ISO C standard defines a set of standard names for signed and unsigned fixed-size integer types – 8-bit: int8_t, uint8_t – 16-bit: int16_t, uint16_t – 32-bit: int32_t, uint32_t – 64-bit: int64_t, uint64_t • These types are defined in the library header file stdint.h Fixed-size integers (3) • Altera HAL provides the header file alt_types.h with definition of fixed-size integer types: typedef signed char alt_8; typedef unsigned char alt_u8; typedef signed short alt_16; typedef unsigned short alt_u16; typedef signed long alt_32; typedef unsigned long alt_u32; typedef long long alt_64; typedef unsigned long long alt_u64; Logical operators • Integer data can be interpreted as logical values in conditions (if, while,