FORSCHUNGSZENTRUM JÜLICH Gmbh Programming in C++ Part II
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Standard Template Library
STANDARD TEMPLATE LIBRARY Developed By Mrs. K.M.Sanghavi STANDARD TEMPLATE LIBRARY (STL) Developed by Alexander Stepanov and Meng Lee of HP in 1979. Standard template library accepted in July 1994 into C++ ANSI Standard These are called as collection of General-purpose template classes( data structures) and functions 1 COMPONENTS OF STL Containers Algorithms Iterators 2 COMPONENTS OF STL Algorithms use iterators to interact with objects stored in containers Container Algorithm1 Algorithm 2 Object1 Object2 Iterator 1 Iterator 2 Object3 Iterator 3 Algorithm 3 3 CONTAINER Objects that hold Example : Array data (of same type) Implemented by Template Classes 4 ALGORITHM Example : These are procedures used to process the data Searching, Sorting, contained in containers. Merging, Copying, Initializing Implemented by template functions 5 ITERATOR It is an object that Used to move points to an through the element in a contents of container container They can be Connect Algorithms incremented and with Containers decremented 6 COMPONENTS OF STL Containers 7 CATEGORIES OF CONTAINERS Sequence Containers Derived Associative Containers Containers 8 CONTAINERS STL Defines 10 Containers 9 CATEGORIES OF CONTAINERS Sequence Associative Derived • vector • set • stack • deque • multiset • queue • list • map • Priority_queue • multimap 10 SEQUENCE CONTAINERS Stores elements in a linear sequence Each element is related to other elements by its position along the line They allow insertion of elements Example Element Element Element Last …….. 0 1 2 element 11 THREE TYPES OF SEQUENCE CONTAINERS vector deque list 12 Vector : Sequence Container Expandable and dynamic array Grows and shrinks in size Insertion / Deletion of elements at back Permits direct access to any element 13 Vector : Sequence Container Container Header File Iterator vector <vector> Random Access 14 vector Sequence Container Declarations ◦ vector <type> v; type: int, float, etc. -
Lecture 04 Linear Structures Sort
Algorithmics (6EAP) MTAT.03.238 Linear structures, sorting, searching, etc Jaak Vilo 2018 Fall Jaak Vilo 1 Big-Oh notation classes Class Informal Intuition Analogy f(n) ∈ ο ( g(n) ) f is dominated by g Strictly below < f(n) ∈ O( g(n) ) Bounded from above Upper bound ≤ f(n) ∈ Θ( g(n) ) Bounded from “equal to” = above and below f(n) ∈ Ω( g(n) ) Bounded from below Lower bound ≥ f(n) ∈ ω( g(n) ) f dominates g Strictly above > Conclusions • Algorithm complexity deals with the behavior in the long-term – worst case -- typical – average case -- quite hard – best case -- bogus, cheating • In practice, long-term sometimes not necessary – E.g. for sorting 20 elements, you dont need fancy algorithms… Linear, sequential, ordered, list … Memory, disk, tape etc – is an ordered sequentially addressed media. Physical ordered list ~ array • Memory /address/ – Garbage collection • Files (character/byte list/lines in text file,…) • Disk – Disk fragmentation Linear data structures: Arrays • Array • Hashed array tree • Bidirectional map • Heightmap • Bit array • Lookup table • Bit field • Matrix • Bitboard • Parallel array • Bitmap • Sorted array • Circular buffer • Sparse array • Control table • Sparse matrix • Image • Iliffe vector • Dynamic array • Variable-length array • Gap buffer Linear data structures: Lists • Doubly linked list • Array list • Xor linked list • Linked list • Zipper • Self-organizing list • Doubly connected edge • Skip list list • Unrolled linked list • Difference list • VList Lists: Array 0 1 size MAX_SIZE-1 3 6 7 5 2 L = int[MAX_SIZE] -
SEI CERT C++ Coding Standard (2016 Edition)
SEI CERT C++ Coding Standard Rules for Developing Safe, Reliable, and Secure Systems in C++ 2016 Edition Aaron Ballman Copyright 2017 Carnegie Mellon University This material is based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. Any opinions, findings and conclusions or recommendations expressed in this material are those of the au- thor(s) and do not necessarily reflect the views of the United States Department of Defense. References herein to any specific commercial product, process, or service by trade name, trade mark, manu- facturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This report was prepared for the SEI Administrative Agent AFLCMC/PZM 20 Schilling Circle, Bldg 1305, 3rd floor Hanscom AFB, MA 01731-2125 NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN “AS-IS” BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. [Distribution Statement A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution. -
Yikes! Why Is My Systemverilog Still So Slooooow?
DVCon-2019 San Jose, CA Voted Best Paper 1st Place World Class SystemVerilog & UVM Training Yikes! Why is My SystemVerilog Still So Slooooow? Cliff Cummings John Rose Adam Sherer Sunburst Design, Inc. Cadence Design Systems, Inc. Cadence Design System, Inc. [email protected] [email protected] [email protected] www.sunburst-design.com www.cadence.com www.cadence.com ABSTRACT This paper describes a few notable SystemVerilog coding styles and their impact on simulation performance. Benchmarks were run using the three major SystemVerilog simulation tools and those benchmarks are reported in the paper. Some of the most important coding styles discussed in this paper include UVM string processing and SystemVerilog randomization constraints. Some coding styles showed little or no impact on performance for some tools while the same coding styles showed large simulation performance impact. This paper is an update to a paper originally presented by Adam Sherer and his co-authors at DVCon in 2012. The benchmarking described in this paper is only for coding styles and not for performance differences between vendor tools. DVCon 2019 Table of Contents I. Introduction 4 Benchmarking Different Coding Styles 4 II. UVM is Software 5 III. SystemVerilog Semantics Support Syntax Skills 10 IV. Memory and Garbage Collection – Neither are Free 12 V. It is Best to Leave Sleeping Processes to Lie 14 VI. UVM Best Practices 17 VII. Verification Best Practices 21 VIII. Acknowledgment 25 References 25 Author & Contact Information 25 Page 2 Yikes! Why is -
Programmatic Testing of the Standard Template Library Containers
Programmatic Testing of the Standard Template Library Containers y z Jason McDonald Daniel Ho man Paul Stro op er May 11, 1998 Abstract In 1968, McIlroy prop osed a software industry based on reusable comp onents, serv- ing roughly the same role that chips do in the hardware industry. After 30 years, McIlroy's vision is b ecoming a reality. In particular, the C++ Standard Template Library STL is an ANSI standard and is b eing shipp ed with C++ compilers. While considerable attention has b een given to techniques for developing comp onents, little is known ab out testing these comp onents. This pap er describ es an STL conformance test suite currently under development. Test suites for all of the STL containers have b een written, demonstrating the feasi- bility of thorough and highly automated testing of industrial comp onent libraries. We describ e a ordable test suites that provide go o d co de and b oundary value coverage, including the thousands of cases that naturally o ccur from combinations of b oundary values. We showhowtwo simple oracles can provide fully automated output checking for all the containers. We re ne the traditional categories of black-b ox and white-b ox testing to sp eci cation-based, implementation-based and implementation-dep endent testing, and showhow these three categories highlight the key cost/thoroughness trade- o s. 1 Intro duction Our testing fo cuses on container classes |those providing sets, queues, trees, etc.|rather than on graphical user interface classes. Our approach is based on programmatic testing where the number of inputs is typically very large and b oth the input generation and output checking are under program control. -
Rogue Wave Standard C++ Library User's Guide and Reference
Rogue Wave Standard C++ Library User's Guide and Reference Rogue Wave Software Corvallis, Oregon USA Standard C++ Library User's Guide and Reference for Rogue Wave's implementation of the Standard C++ Library. Based on ANSI's Working Paper for Draft Proposed International Standard for Information Systems--Programming Language C++. April 28, 1995. User's Guide Authors: Timothy A. Budd, Randy Smithey Reference Authors: Wendi Minne, Tom Pearson, and Randy Smithey Product Team: Development: Anna Dahan, Donald Fowler, Marlene Hart, Angelika Langer, Philippe Le Mouel, Randy Smithey Quality Engineering: KevinDjang, Randall Robinson, Chun Zhang Manuals: Wendi Minne, Kristi Moore, Julie Prince,Randy Smithey Support: North Krimsley Significant contributions by: Rodney Mishima Copyright © 1995-96 Rogue Wave Software, Inc. All rights reserved. Printed in the United States of America. Part # RW81-01-2-032596 Printing Date: July 1996 Rogue Wave Software, Inc., 850 SW 35th Street, Corvallis, Oregon, 97333 USA Product Information: (541) 754-5010 (800) 487-3217 Technical Support: (541) 754-2311 FAX: (541) 757-6650 World Wide Web: http://www.roguewave.com Please have your product serial number available when calling for technical support. Table of Contents 1. Introduction .........................................................................1 1.1 What is the Standard C++ Library?............................................................. 2 1.2 Does the Standard C++ Library Differ From Other Libraries? ............... 2 1.3 What are the Effects of -
Lecture 2: Data Structures: a Short Background
Lecture 2: Data structures: A short background Storing data It turns out that depending on what we wish to do with data, the way we store it can have a signifcant impact on the running time. So in particular, storing data in certain "structured" way can allow for fast implementation. This is the motivation behind the feld of data structures, which is an extensive area of study. In this lecture, we will give a very short introduction, by illustrating a few common data structures, with some motivating problems. In the last lecture, we mentioned that there are two ways of representing graphs (adjacency list and adjacency matrix), each of which has its own advantages and disadvantages. The former is compact (size is proportional to the number of edges + number of vertices), and is faster for enumerating the neighbors of a vertex (which is what one needs for procedures like Breadth-First-Search). The adjacency matrix is great if one wishes to quickly tell if two vertices and have an edge between them. Let us now consider a diferent problem. Example 1: Scrabble problem Suppose we have a "dictionary" of strings whose average length is , and suppose we have a query string . How can we quickly tell if ? Brute force. Note that the naive solution is to iterate over the strings and check if for some . This takes time . If we only care about answering the query for one , this is not bad, as is the amount of time needed to "read the input". But of course, if we have a fxed dictionary and if we wish to check if for multiple , then this is extremely inefcient. -
The Wrapper API's Baseline Requirements
LASER INTERFEROMETER GRAVITATIONAL WAVE OBSERVATORY - LIGO - CALIFORNIA INSTITUTE OF TECHNOLOGY MASSACHUSETTS INSTITUTE OF TECHNOLOGY Document Type LIGO-T990097-11 - E 08/29/2000 The wrapper API’s baseline requirements James Kent Blackburn, Masha Barnes Jolien Creighton Distribution of this document: LIGO LDAS Group This is an internal working document of the LIGO Project. California Institute of Technology Massachusetts Institute of Technology LIGO Project - MS 51-33 LIGO Project - MS 20B-145 Pasadena CA 91125 Cambridge, MA 01239 Phone (818) 395-2129 Phone (617) 253-4824 Fax (818) 304-9834 Fax (617) 253-7014 E-mail: [email protected] E-mail: [email protected] WWW: http://www.ligo.caltech.edu/ Table of Contents Index file \\SIRIUS\kent\documents\LDAS\wrapperAPI\wrapperAPIReqCover.fm - printed August 29, 2000 The wrapper API’s baseline requirements James Kent Blackburn California Institute of Technology LIGO Data Analysis Group August 29, 2000 I. Introduction A. General Description: 1. The wrapperAPI is responsible for executing the advanced analysis pro- cesses which are based on MPI and executing in the LDAS distributed com- puting parallel cluster of nodes using an interpreted command language. 2. The wrapperAPI will be written entirely in C and C++. No TCL/TK will be used in this LDAS API. 3. The wrapperAPI will be initiated using the mpirun command. This command will be started solely by the mpiAPI. The mpiAPI will determine the appro- priate values for the mpirun command, as well as the appropriate commands for the wrapperAPI and pass these as command line arguments to mpirun. The wrapperAPI will interpret its own command line arguments which are automatically passed to the wrapperAPI by the mpirun command. -
Parallelization of Bulk Operations for STL Dictionaries
Parallelization of Bulk Operations for STL Dictionaries Leonor Frias1?, Johannes Singler2 [email protected], [email protected] 1 Dep. de Llenguatges i Sistemes Inform`atics,Universitat Polit`ecnicade Catalunya 2 Institut f¨urTheoretische Informatik, Universit¨atKarlsruhe Abstract. STL dictionaries like map and set are commonly used in C++ programs. We consider parallelizing two of their bulk operations, namely the construction from many elements, and the insertion of many elements at a time. Practical algorithms are proposed for these tasks. The implementation is completely generic and engineered to provide best performance for the variety of possible input characteristics. It features transparent integration into the STL. This can make programs profit in an easy way from multi-core processing power. The performance mea- surements show the practical usefulness on real-world multi-core ma- chines with up to eight cores. 1 Introduction Multi-core processors bring parallel computing power to the customer at virtu- ally no cost. Where automatic parallelization fails and OpenMP loop paralleliza- tion primitives are not strong enough, parallelized algorithms from a library are a sensible choice. Our group pursues this goal with the Multi-Core Standard Template Library [6], a parallel implementation of the C++ STL. To allow best benefit from the parallel computing power, as many operations as possible need to be parallelized. Sequential parts could otherwise severely limit the achievable speedup, according to Amdahl’s law. Thus, it may be profitable to parallelize an operation even if the speedup is considerably less than the number of threads. The STL contains four kinds of generic dictionary types, namely set, map, multiset, and multimap. -
STL) Is a Collection of Containers and Algorithms Which Is Part of the Standard C++ Library
Containers, algorithms and more... The standard library is the collection of functions and types that is supplied with every standard-compliant C++ compiler What should be in the standard library? And equally important: what should not be? 2 Introduction to Systems Programming The standard library should: Provide basic functionality which the user can have difficulty implementing himself: Memory management Input / output Implementation-dependent information Save and simplify work: Supply useful non-primitive facilities 3 Introduction to Systems Programming The standard library cannot contain problem- specific code For example – a Student class It should contain generic classes and functions which are usable in a variety of problems: Containers – such as list, set, etc. Algorithms – such as sorting, searching Popular classes – such as string 4 Introduction to Systems Programming What are the requirements from the standard library's containers and algorithms? Simple to use Interfaces should be uniform and convenient Complete Reasonable features should be included Efficient Library code might take a large part of the execution time Extensible The user should be able to add new containers and algorithms that work with the given ones 5 Introduction to Systems Programming The containers included in the standard library should be generic We have seen several ways to implement generic containers: Using void* and pointers to functions Using templates Using subtyping and polymorphism 6 Introduction to Systems Programming -
STL – Standard Template Library
STL – Standard Template Library September 22, 2016 CMPE 250 STL – Standard Template Library September 22, 2016 1 / 25 STL – Standard Template Library Collections of useful classes for common data structures Ability to store objects of any type (template) Containers form the basis for treatment of data structures Container – class that stores a collection of data STL consists of 10 container classes: CMPE 250 STL – Standard Template Library September 22, 2016 2 / 25 STL Containers Sequence Container Stores data by position in linear order: First element, second element , etc. All containers Use same names for common operations Have specific operations Associate Container Stores elements by key, such as name, ID number or part number Access an element by its key which may bear no relationship to the location of the element in the container Adapter Container Contains another container as its underlying storage structure CMPE 250 STL – Standard Template Library September 22, 2016 3 / 25 STL Containers Sequence Container Vector Deque List Adapter Containers Stack Queue Priority queue Associative Container Set, multiset Map, multimap CMPE 250 STL – Standard Template Library September 22, 2016 4 / 25 How to access Components - Iterator Iterator is an object that can access a collection of like objects one object at a time. An iterator can traverse the collection of objects. Each container class in STL has a corresponding iterator that functions appropriately for the container For example: an iterator in a vector class allows random access An iterator -
Chapter 10: Efficient Collections (Skip Lists, Trees)
Chapter 10: Efficient Collections (skip lists, trees) If you performed the analysis exercises in Chapter 9, you discovered that selecting a bag- like container required a detailed understanding of the tasks the container will be expected to perform. Consider the following chart: Dynamic array Linked list Ordered array add O(1)+ O(1) O(n) contains O(n) O(n) O(log n) remove O(n) O(n) O(n) If we are simply considering the cost to insert a new value into the collection, then nothing can beat the constant time performance of a simple dynamic array or linked list. But if searching or removals are common, then the O(log n) cost of searching an ordered list may more than make up for the slower cost to perform an insertion. Imagine, for example, an on-line telephone directory. There might be several million search requests before it becomes necessary to add or remove an entry. The benefit of being able to perform a binary search more than makes up for the cost of a slow insertion or removal. What if all three bag operations are more-or-less equal? Are there techniques that can be used to speed up all three operations? Are arrays and linked lists the only ways of organizing a data for a bag? Indeed, they are not. In this chapter we will examine two very different implementation techniques for the Bag data structure. In the end they both have the same effect, which is providing O(log n) execution time for all three bag operations.