This document discusses C++ references and reference parameters. It explains that call by value passes a copy of the data to a function, while call by reference allows the function to directly access and modify the original data. A reference is an alias for an argument and is declared using an ampersand. The document provides an example program that demonstrates the difference between call by value and call by reference using references.
High-level Programming Languages: Apache Pig and Pig LatinPietro Michiardi
This slide deck is used as an introduction to the Apache Pig system and the Pig Latin high-level programming language, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
This lecture covers the principles and the architectures of modern cluster schedulers, including Apache Mesos, Apache Yarn, Google Borg and K8s, and some notes on Omega
Compression Options in Hadoop - A Tale of TradeoffsDataWorks Summit
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo!`s Hadoop clusters. A key component that enables this efficient operation is data compression. With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This paper attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented. The paper also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on “Big Data” who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.
Integrating R & Hadoop - Text Mining & Sentiment AnalysisAravind Babu
The document discusses integrating R and Hadoop for big data analytics. It notes that existing statistical applications like R are incapable of handling big data, while data management tools lack analytical capabilities. Integrating R with Hadoop bridges this gap by leveraging R's analytics and statistics functionality with Hadoop's ability to process and store distributed data. RHadoop is introduced as an open source project that allows R programmers to directly use MapReduce functionality in R code. Specific RHadoop packages like rhdfs and rmr2 are described that enable interacting with HDFS and performing statistical analysis via MapReduce on Hadoop clusters. Text analytics use cases with R and Hadoop like sentiment analysis are also briefly outlined.
The document summarizes a presentation on using R and Hadoop together. It includes:
1) An outline of topics to be covered including why use MapReduce and R, options for combining R and Hadoop, an overview of RHadoop, a step-by-step example, and advanced RHadoop features.
2) Code examples from Jonathan Seidman showing how to analyze airline on-time data using different R and Hadoop options - naked streaming, Hive, RHIPE, and RHadoop.
3) The analysis calculates average departure delays by year, month and airline using each method.
Massively Parallel Processing with Procedural Python (PyData London 2014)Ian Huston
The Python data ecosystem has grown beyond the confines of single machines to embrace scalability. Here we describe one of our approaches to scaling, which is already being used in production systems. The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. Using PL/Python we can run parallel queries across terabytes of data using not only pure SQL but also familiar PyData packages such as scikit-learn and nltk. This approach can also be used with PL/R to make use of a wide variety of R packages. We look at examples on Postgres compatible systems such as the Greenplum Database and on Hadoop through Pivotal HAWQ. We will also introduce MADlib, Pivotal’s open source library for scalable in-database machine learning, which uses Python to glue SQL queries to low level C++ functions and is also usable through the PyMADlib package.
Testing Apache Modules with Python and CtypesMarkus Litz
Writing tests for your Apache module is often a developer's least favourite task, especially if you don't like using the Perl Apache Testing Framework! One of the main reasons for this is that it's difficult to test your C code on-the-fly without a running Apache server. Using ctypes, you can test your modules without a running httpd, just by writing and using simple Python scripts! In this talk we will look at how to compile a specific version of the Apache webserver for testing, and how to use a common Python unit testing framework to write and set up tests. We will also cover examples of writing simple test cases for the Catacomb WebDAV Server module.
Talk from Apachecon US 2009
The document provides an overview of the C++ programming language. It discusses that C++ was derived from C and created in the 1980s. It then describes some key aspects of C++ including the compiler, program structure, comments/directives like #include, data types, and the main program. The main program example provided converts miles to kilometers as a simple illustration of a C++ program.
High-level Programming Languages: Apache Pig and Pig LatinPietro Michiardi
This slide deck is used as an introduction to the Apache Pig system and the Pig Latin high-level programming language, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
This lecture covers the principles and the architectures of modern cluster schedulers, including Apache Mesos, Apache Yarn, Google Borg and K8s, and some notes on Omega
Compression Options in Hadoop - A Tale of TradeoffsDataWorks Summit
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo!`s Hadoop clusters. A key component that enables this efficient operation is data compression. With regard to compression algorithms, there is an underlying tension between compression ratio and compression performance. Consequently, Hadoop provides support for several compression algorithms, including gzip, bzip2, Snappy, LZ4 and others. This plethora of options can make it difficult for users to select appropriate codecs for their MapReduce jobs. This paper attempts to provide guidance in that regard. Performance results with Gridmix and with several corpuses of data are presented. The paper also describes enhancements we have made to the bzip2 codec that improve its performance. This will be of particular interest to the increasing number of users operating on “Big Data” who require the best possible ratios. The impact of using the Intel IPP libraries is also investigated; these have the potential to improve performance significantly. Finally, a few proposals for future enhancements to Hadoop in this area are outlined.
Integrating R & Hadoop - Text Mining & Sentiment AnalysisAravind Babu
The document discusses integrating R and Hadoop for big data analytics. It notes that existing statistical applications like R are incapable of handling big data, while data management tools lack analytical capabilities. Integrating R with Hadoop bridges this gap by leveraging R's analytics and statistics functionality with Hadoop's ability to process and store distributed data. RHadoop is introduced as an open source project that allows R programmers to directly use MapReduce functionality in R code. Specific RHadoop packages like rhdfs and rmr2 are described that enable interacting with HDFS and performing statistical analysis via MapReduce on Hadoop clusters. Text analytics use cases with R and Hadoop like sentiment analysis are also briefly outlined.
The document summarizes a presentation on using R and Hadoop together. It includes:
1) An outline of topics to be covered including why use MapReduce and R, options for combining R and Hadoop, an overview of RHadoop, a step-by-step example, and advanced RHadoop features.
2) Code examples from Jonathan Seidman showing how to analyze airline on-time data using different R and Hadoop options - naked streaming, Hive, RHIPE, and RHadoop.
3) The analysis calculates average departure delays by year, month and airline using each method.
Massively Parallel Processing with Procedural Python (PyData London 2014)Ian Huston
The Python data ecosystem has grown beyond the confines of single machines to embrace scalability. Here we describe one of our approaches to scaling, which is already being used in production systems. The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. Using PL/Python we can run parallel queries across terabytes of data using not only pure SQL but also familiar PyData packages such as scikit-learn and nltk. This approach can also be used with PL/R to make use of a wide variety of R packages. We look at examples on Postgres compatible systems such as the Greenplum Database and on Hadoop through Pivotal HAWQ. We will also introduce MADlib, Pivotal’s open source library for scalable in-database machine learning, which uses Python to glue SQL queries to low level C++ functions and is also usable through the PyMADlib package.
Testing Apache Modules with Python and CtypesMarkus Litz
Writing tests for your Apache module is often a developer's least favourite task, especially if you don't like using the Perl Apache Testing Framework! One of the main reasons for this is that it's difficult to test your C code on-the-fly without a running Apache server. Using ctypes, you can test your modules without a running httpd, just by writing and using simple Python scripts! In this talk we will look at how to compile a specific version of the Apache webserver for testing, and how to use a common Python unit testing framework to write and set up tests. We will also cover examples of writing simple test cases for the Catacomb WebDAV Server module.
Talk from Apachecon US 2009
The document provides an overview of the C++ programming language. It discusses that C++ was derived from C and created in the 1980s. It then describes some key aspects of C++ including the compiler, program structure, comments/directives like #include, data types, and the main program. The main program example provided converts miles to kilometers as a simple illustration of a C++ program.
This Tutorial is designed for the users who have exposure to MPI I/O and basic concepts of HDF5 and would like to learn about Parallel HDF5 Library. The Tutorial will cover Parallel HDF5 design and programming model. Several C and Fortran examples will be used to illustrate the basic ideas of the Parallel HDF5 programming model. Some performance issues including collective chunked I/O will be discussed. Participants will work with the Tutorial examples and exercises during the hands-on sessions.
The document discusses OrientDB's transition from a master-slave architecture to a new multi-master distributed architecture. The new architecture allows any node to read and write, improves scalability, and handles conflicts intelligently. It will be released in OrientDB version 1.0 in December 2011.
The document discusses changes to the ApplicationMaster (AM) component architecture in Hadoop to improve container reuse. It proposes separating container and task operations so that containers can be launched independently of tasks and reused for multiple tasks. This would facilitate features like common map output buffers for tasks in the same container and merging map outputs per node or rack. The current state of the AM changes is described, along with requirements for task side changes and a new reuse scheduler. Potential benefits highlighted include simplifying deployment, monitoring large clusters, integrating with other systems, and reducing costs through high availability and training/support services.
DEF CON 27 - workshop - HUGO TROVAO and RUSHIKESH NADEDKAR - scapy dojo v1Felipe Prado
This document provides an overview of Scapy, a tool for sending, sniffing, and dissecting network packets. It discusses the following key points:
- Scapy allows building packets with layers and fields, sending and receiving packets, and dissecting captured packets.
- Packets are made of layers like Ethernet, IP, TCP, which contain fields that can be manipulated.
- Common tasks include crafting packets, displaying packet details, sending packets, receiving packets through sniffing, and reading/writing pcap files.
- Examples are provided for tasks like DHCP servers, DNS servers, and protocols like AJP13 and LoRaWAN.
The document provides an overview of the ELF (Executable and Linkable Format) file format used by most Unix operating systems. It discusses how ELF files contain sections and segments that provide information to linkers and loaders. Specifically, it explains that sections contain code and data and are used by linkers to connect pieces at compile time, while segments define memory permissions and locations and are used by loaders to map the binary into memory at runtime. It also gives examples of common sections like .text, .data, .rodata, and describes how dynamic linking with the PLT and GOT tables allows functions to be resolved at load time.
Program Structure in GNU/Linux (ELF Format)Varun Mahajan
The document discusses the processing of a user program in a GNU/Linux system. It describes the steps of preprocessing, compilation, assembly and linking. It then explains the ELF format used in object files, including the ELF header, program header table, section header table, and common sections like .text, .data, .bss, .symtab, and .strtab. Key details covered in each section include type of code or data, addresses, sizes, and other attributes.
Perl is an interpreted programming language used for text processing and web development. It allows for both procedural and object-oriented programming. Perl is efficient for system administration tasks and manipulating text, but efficiency is not a primary focus as interpretation allows for quicker development and changes compared to compiled languages. Perl can be run on many platforms and includes features like database integration, support for HTML/XML, Unicode, and large libraries of third-party modules.
The document discusses the process from compiling source code to executing a program. It covers preprocessing, compilation, assembly, linking, and the ELF file format. Preprocessing handles macros and conditionals. Compilation translates to assembly code. Assembly generates machine code. Linking combines object files and resolves symbols statically or dynamically using libraries. The ELF file format organizes machine code and data into sections in the executable.
Strata NY 2016: The future of column-oriented data processing with Arrow and ...Julien Le Dem
In pursuit of speed, big data is evolving toward columnar execution. The solid foundation laid by Arrow and Parquet for a shared columnar representation across the ecosystem promises a great future. Julien Le Dem and Jacques Nadeau discuss the future of columnar and the hardware trends it takes advantage of, like RDMA, SSDs, and nonvolatile memory.
Perl is a scripting language created by Larry Wall in 1987. It is an interpreted language useful for tasks like system administration, web development, and text processing due to its powerful string and text manipulation capabilities. Perl code does not need to be compiled and can run on many platforms including Unix, Windows, and Linux. It has scalar, list, and hash variable types and supports both procedural and object-oriented programming. Perl is an open source language with over 20,000 third-party modules available to extend its functionality.
This PPT discusses the concept of Dynamic Linker as in Linux and its porting to Solaris ARM platform. It starts from the very basics of linking process
R and Hadoop are changing the way organizations manage and utilize big data. Think Big Analytics and Revolution Analytics are helping clients plan, build, test and implement innovative solutions based on the two technologies that allow clients to analyze data in new ways; exposing new insights for the business. Join us as Jeffrey Breen explains the core technology concepts and illustrates how to utilize R and Revolution Analytics’ RevoR in Hadoop environments.
This document provides an overview of using data mining and text mining techniques to analyze patent documents. It discusses text mining processes like preprocessing, transformation, and feature selection. It also demonstrates various visualizations that can be used, including word clouds, hierarchical clustering, and contour plots. Finally, it compares the R and KNIME tools for performing text mining and data analysis on patent documents.
The document discusses different memory partitioning schemes used in operating systems. It describes fixed partitions where memory is divided into predetermined sized partitions at initialization time. It also describes variable partitions where memory is not pre-partitioned and is allocated on demand, which can cause external fragmentation. Dynamic binding is discussed where the logical to physical address mapping occurs at execution time with hardware support.
HDFS Erasure Code Storage - Same Reliability at Better Storage EfficiencyDataWorks Summit
The document discusses HDFS erasure coding storage, which provides the same reliability as HDFS replication but with better storage efficiency. It describes HDFS's current replication strategy and introduces erasure coding as an alternative. Erasure coding uses k data blocks and m parity blocks to tolerate m failures while using less storage than replication. The document outlines HDFS's technical approach to implementing erasure coding, including striped block encoding, reading and writing pipelines, and reconstruction of missing blocks. It provides details on architecture, development progress, and future work to further enhance erasure coding in HDFS.
Hadoop Summit 2012 | HBase Consistency and Performance ImprovementsCloudera, Inc.
The latest Apache HBase releases, 0.92 and 0.94, contain many improvements over prior releases in terms of correctness and performance improvements. We discuss a couple of these improvements from a development and operations perspective. For correctness, we discuss the ACID guarantees of HBase, give a case study of problems with earlier releases, and give an overview of the implementation internals that were improved to fix the issues. For performance, we discuss recent improvements in 0.94 and how to monitor the performance of a cluster with new metrics.
When two of the most powerful innovations in modern analytics come together, the result is revolutionary.
This presentation covers:
- An overview of R, the Open Source programming language used by more than 2 million users that was specifically developed for statistical analysis and data visualization.
- The ways that R and Hadoop have been integrated.
- A use case that provides real-world experience.
- A look at how enterprises can take advantage of both of these industry-leading technologies.
Presented at Hadoop World 2011 by:
David Champagne
CTO, Revolution Analytics
David Champagne is a top software architect, programmer and product manager with over 20 years experience in enterprise and web application development for business customers across a wide range of industries. As Principal Architect/Engineer for SPSS, Champagne led the development teams and created and led the text mining team.
Leveraging Open Source to Manage SAN Performancebrettallison
Scope - The primary focus of this presentation is how to leverage open source software to help in managing Shared Storage performance. The storage server will be the focus with particular emphasis on ESS. This solution is a small one-off solution.
This document discusses Chapter 18 from a C++ textbook. It covers various C++ features including enhancements from C, the C++ standard library, header files, inline functions, references and reference parameters, function overloading, and default arguments. The chapter objectives are listed, which are to learn about these C++ features and object-oriented programming concepts like classes. Examples are provided throughout to demonstrate concepts like a simple integer addition program, using an inline function to calculate cube volumes, and passing arguments by reference versus by value.
C++ was designed by Bjarne Stroupstrup to provide Simula's facilities for program organization with C's efficiency and flexibility for systems programming. It is a compiled language that supports classes, templates, operator overloading, references, and exceptions. A C++ program consists of source files that are compiled into object files and then linked together into an executable file by the compiler and linker. An example program demonstrates including libraries, using namespaces, defining and calling a main function, and compiling and executing the program.
This Tutorial is designed for the users who have exposure to MPI I/O and basic concepts of HDF5 and would like to learn about Parallel HDF5 Library. The Tutorial will cover Parallel HDF5 design and programming model. Several C and Fortran examples will be used to illustrate the basic ideas of the Parallel HDF5 programming model. Some performance issues including collective chunked I/O will be discussed. Participants will work with the Tutorial examples and exercises during the hands-on sessions.
The document discusses OrientDB's transition from a master-slave architecture to a new multi-master distributed architecture. The new architecture allows any node to read and write, improves scalability, and handles conflicts intelligently. It will be released in OrientDB version 1.0 in December 2011.
The document discusses changes to the ApplicationMaster (AM) component architecture in Hadoop to improve container reuse. It proposes separating container and task operations so that containers can be launched independently of tasks and reused for multiple tasks. This would facilitate features like common map output buffers for tasks in the same container and merging map outputs per node or rack. The current state of the AM changes is described, along with requirements for task side changes and a new reuse scheduler. Potential benefits highlighted include simplifying deployment, monitoring large clusters, integrating with other systems, and reducing costs through high availability and training/support services.
DEF CON 27 - workshop - HUGO TROVAO and RUSHIKESH NADEDKAR - scapy dojo v1Felipe Prado
This document provides an overview of Scapy, a tool for sending, sniffing, and dissecting network packets. It discusses the following key points:
- Scapy allows building packets with layers and fields, sending and receiving packets, and dissecting captured packets.
- Packets are made of layers like Ethernet, IP, TCP, which contain fields that can be manipulated.
- Common tasks include crafting packets, displaying packet details, sending packets, receiving packets through sniffing, and reading/writing pcap files.
- Examples are provided for tasks like DHCP servers, DNS servers, and protocols like AJP13 and LoRaWAN.
The document provides an overview of the ELF (Executable and Linkable Format) file format used by most Unix operating systems. It discusses how ELF files contain sections and segments that provide information to linkers and loaders. Specifically, it explains that sections contain code and data and are used by linkers to connect pieces at compile time, while segments define memory permissions and locations and are used by loaders to map the binary into memory at runtime. It also gives examples of common sections like .text, .data, .rodata, and describes how dynamic linking with the PLT and GOT tables allows functions to be resolved at load time.
Program Structure in GNU/Linux (ELF Format)Varun Mahajan
The document discusses the processing of a user program in a GNU/Linux system. It describes the steps of preprocessing, compilation, assembly and linking. It then explains the ELF format used in object files, including the ELF header, program header table, section header table, and common sections like .text, .data, .bss, .symtab, and .strtab. Key details covered in each section include type of code or data, addresses, sizes, and other attributes.
Perl is an interpreted programming language used for text processing and web development. It allows for both procedural and object-oriented programming. Perl is efficient for system administration tasks and manipulating text, but efficiency is not a primary focus as interpretation allows for quicker development and changes compared to compiled languages. Perl can be run on many platforms and includes features like database integration, support for HTML/XML, Unicode, and large libraries of third-party modules.
The document discusses the process from compiling source code to executing a program. It covers preprocessing, compilation, assembly, linking, and the ELF file format. Preprocessing handles macros and conditionals. Compilation translates to assembly code. Assembly generates machine code. Linking combines object files and resolves symbols statically or dynamically using libraries. The ELF file format organizes machine code and data into sections in the executable.
Strata NY 2016: The future of column-oriented data processing with Arrow and ...Julien Le Dem
In pursuit of speed, big data is evolving toward columnar execution. The solid foundation laid by Arrow and Parquet for a shared columnar representation across the ecosystem promises a great future. Julien Le Dem and Jacques Nadeau discuss the future of columnar and the hardware trends it takes advantage of, like RDMA, SSDs, and nonvolatile memory.
Perl is a scripting language created by Larry Wall in 1987. It is an interpreted language useful for tasks like system administration, web development, and text processing due to its powerful string and text manipulation capabilities. Perl code does not need to be compiled and can run on many platforms including Unix, Windows, and Linux. It has scalar, list, and hash variable types and supports both procedural and object-oriented programming. Perl is an open source language with over 20,000 third-party modules available to extend its functionality.
This PPT discusses the concept of Dynamic Linker as in Linux and its porting to Solaris ARM platform. It starts from the very basics of linking process
R and Hadoop are changing the way organizations manage and utilize big data. Think Big Analytics and Revolution Analytics are helping clients plan, build, test and implement innovative solutions based on the two technologies that allow clients to analyze data in new ways; exposing new insights for the business. Join us as Jeffrey Breen explains the core technology concepts and illustrates how to utilize R and Revolution Analytics’ RevoR in Hadoop environments.
This document provides an overview of using data mining and text mining techniques to analyze patent documents. It discusses text mining processes like preprocessing, transformation, and feature selection. It also demonstrates various visualizations that can be used, including word clouds, hierarchical clustering, and contour plots. Finally, it compares the R and KNIME tools for performing text mining and data analysis on patent documents.
The document discusses different memory partitioning schemes used in operating systems. It describes fixed partitions where memory is divided into predetermined sized partitions at initialization time. It also describes variable partitions where memory is not pre-partitioned and is allocated on demand, which can cause external fragmentation. Dynamic binding is discussed where the logical to physical address mapping occurs at execution time with hardware support.
HDFS Erasure Code Storage - Same Reliability at Better Storage EfficiencyDataWorks Summit
The document discusses HDFS erasure coding storage, which provides the same reliability as HDFS replication but with better storage efficiency. It describes HDFS's current replication strategy and introduces erasure coding as an alternative. Erasure coding uses k data blocks and m parity blocks to tolerate m failures while using less storage than replication. The document outlines HDFS's technical approach to implementing erasure coding, including striped block encoding, reading and writing pipelines, and reconstruction of missing blocks. It provides details on architecture, development progress, and future work to further enhance erasure coding in HDFS.
Hadoop Summit 2012 | HBase Consistency and Performance ImprovementsCloudera, Inc.
The latest Apache HBase releases, 0.92 and 0.94, contain many improvements over prior releases in terms of correctness and performance improvements. We discuss a couple of these improvements from a development and operations perspective. For correctness, we discuss the ACID guarantees of HBase, give a case study of problems with earlier releases, and give an overview of the implementation internals that were improved to fix the issues. For performance, we discuss recent improvements in 0.94 and how to monitor the performance of a cluster with new metrics.
When two of the most powerful innovations in modern analytics come together, the result is revolutionary.
This presentation covers:
- An overview of R, the Open Source programming language used by more than 2 million users that was specifically developed for statistical analysis and data visualization.
- The ways that R and Hadoop have been integrated.
- A use case that provides real-world experience.
- A look at how enterprises can take advantage of both of these industry-leading technologies.
Presented at Hadoop World 2011 by:
David Champagne
CTO, Revolution Analytics
David Champagne is a top software architect, programmer and product manager with over 20 years experience in enterprise and web application development for business customers across a wide range of industries. As Principal Architect/Engineer for SPSS, Champagne led the development teams and created and led the text mining team.
Leveraging Open Source to Manage SAN Performancebrettallison
Scope - The primary focus of this presentation is how to leverage open source software to help in managing Shared Storage performance. The storage server will be the focus with particular emphasis on ESS. This solution is a small one-off solution.
This document discusses Chapter 18 from a C++ textbook. It covers various C++ features including enhancements from C, the C++ standard library, header files, inline functions, references and reference parameters, function overloading, and default arguments. The chapter objectives are listed, which are to learn about these C++ features and object-oriented programming concepts like classes. Examples are provided throughout to demonstrate concepts like a simple integer addition program, using an inline function to calculate cube volumes, and passing arguments by reference versus by value.
C++ was designed by Bjarne Stroupstrup to provide Simula's facilities for program organization with C's efficiency and flexibility for systems programming. It is a compiled language that supports classes, templates, operator overloading, references, and exceptions. A C++ program consists of source files that are compiled into object files and then linked together into an executable file by the compiler and linker. An example program demonstrates including libraries, using namespaces, defining and calling a main function, and compiling and executing the program.
The document introduces programming concepts in C++ including:
- The software development cycle of compile, link, and execute source code using an IDE.
- Key programming language elements like keywords, variables, operators, and constructs and how every language has a defined syntax.
- Object-oriented programming concepts in C++ like classes, objects, and inheritance hierarchies.
- A simple "Hello World" C++ program structure and basic data types and output statements.
05 Lecture - PARALLEL Programming in C ++.pdfalivaisi1
Parallel programming provides opportunities for improving performance by distributing work across multiple processors. However, it also presents challenges related to concurrency control, scalability, load balancing, and complexity. Innovation in parallel programming approaches includes task parallelism, GPU acceleration, asynchronous models, and specialized libraries and frameworks. Future trends may involve quantum computing, distributed systems, and heterogeneous computing.
Introduction to cpp language and all the required information relating to itPushkarNiroula1
C++ is an object-oriented programming language developed in the early 1980s as an extension of C with additional features like classes, inheritance, and function overloading. A simple C++ program prints a string to the screen using the cout output stream and iostream header. C++ programs typically contain functions, comments, and use operators like << for output and >> for input.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- Programs go through preprocessing, compilation, linking, and execution. Development environments help manage these steps and provide tools like debugging.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- Programs go through preprocessing, compilation, linking, and execution. Development environments help manage these steps and provide tools like editors, compilers, debuggers.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- C++ code is compiled into object files then linked together into an executable program. Development environments help with building, debugging, and submitting code.
C++ programming: Basic introduction to C++.pptyp02
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- C++ code is compiled into object files then linked together with libraries to create an executable program. Development environments help with building, debugging, and submitting code.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- Programs go through preprocessing, compilation, linking, and execution. Development environments help manage these steps and provide tools like debugging.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- Programs are compiled from source code into object files, then linked together with libraries to create an executable. Development environments help with tasks like editing, compiling, debugging.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files with .cpp extensions containing function definitions, and header files with .h extensions containing declarations.
- The main() function is where program execution begins. It can take command line arguments which are passed via the argc and argv parameters.
- Common elements of C++ programs include #include directives to import headers, namespaces like std, and output streams like cout.
- C++ code is compiled into object files then linked together into an executable program. Development environments help with building, debugging, and submitting code.
This document discusses the basic structure of C++ programs. It covers preprocessor directives, header files, the main function, and return statements. It provides examples of a simple Hello World program structure and explains each part. It also lists common C++ header files and their purposes.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files (.cpp) containing definitions and header files (.h) containing declarations.
- The main() function is where execution begins. It can take arguments like the number of command line parameters and their values.
- Common elements of C++ programs include using namespaces, including header files, and defining functions like main().
- The development process involves writing code, compiling, linking, and running the executable. Integrated development environments and command line tools can be used.
- Studios provide time to work through exercises to develop C++ skills and understanding tested in exams and labs.
This document discusses the structure of C++ programs and the development environment. It covers:
- C++ programs are made up of source code files (.cpp) containing definitions and header files (.h) containing declarations.
- The main() function is where execution begins. It can take arguments like the number of command line parameters and their values.
- Common elements of C++ programs include using namespaces, including header files, and defining functions like main().
- The development process involves writing code, compiling, linking, and running the executable. Integrated development environments and command line tools can be used.
- C++ classes and templates are also discussed at a high level.
Object oriented programming 7 first steps in oop using c++Vaibhav Khanna
Advantages of C++
Portability. C++ offers the feature of portability or platform independence which allows the user to run the same program on different operating systems or interfaces at ease. ...
Object-oriented. ...
Multi-paradigm. ...
Low-level Manipulation. ...
Memory Management. ...
Large Community Support. ...
Compatibility with C. ...
Scalability.
Introduction to C Language (By: Shujaat Abbas)Shujaat Abbas
This document provides an introduction to programming in C++. It explains that a program is a sequence of instructions that a computer can interpret and execute. C++ is a general-purpose, compiled programming language that supports procedural, object-oriented, and generic programming. It was developed in 1979 as an enhancement to the C language. The document outlines the basic elements of a C++ program, including preprocessor directives, header files, functions, return statements, and data types. It also discusses setting up the environment, writing and compiling a simple "Hello World" program, and the roles of editors, compilers, and linkers.
This document outlines the basics of C++ programming, including:
- The history and evolution of C++ and other programming languages.
- How a C++ source code is compiled into an executable program by the compiler in multiple steps like preprocessing, compiling, linking etc.
- The structure of a basic C++ program and key elements like main function, headers, comments.
- How to use input/output streams, variables, arithmetic operations, and conditional statements like if-else in a C++ program.
The carbon cycle plays an important role in regulating carbon levels on Earth. As humans cut down more trees and burn fossil fuels, carbon dioxide levels increase in the atmosphere, exacerbating global warming. The carbon cycle involves carbon moving from the atmosphere to plants through photosynthesis, then to animals that eat plants, and eventually back to the atmosphere through respiration and decomposition. Maintaining the balance of the carbon cycle is critical for sustaining the food chain and regulating carbon levels in oceans, forests, and the atmosphere.
The document discusses the Downs Process for extracting sodium through the electrolysis of molten sodium chloride, where sodium ions are reduced to sodium atoms at the cathode and chlorine gas is produced at the anode. It also describes how sodium hydroxide is produced through the electrolysis of sodium chloride solutions, and lists some common uses of sodium and sodium hydroxide such as in soap production, as a drain cleaner, and for lighting.
Soaps are made through saponification, the process of reacting fats or oils with alkalis. They are cheap and biodegradable but do not work well in hard water which causes insoluble scum. Detergents are made through chemical processes using petroleum products or natural esters reacted with alkalis. They contain surfactants, phosphates, and other additives. Detergents are more effective than soaps for hard water, but many varieties are non-biodegradable and can cause environmental harm. Both products have advantages like low costs but also disadvantages like limited cleaning power or environmental impacts.
This document discusses different types of nitrogen fertilizers including organic and inorganic varieties. Organic nitrogen is slow-acting but supplies nitrogen for longer. Inorganic nitrogen exists as ammonium or nitrate, with ammonium bonding tightly to soil and nitrate being quickly absorbed but also leached. The document outlines nitrate, ammonium, and combined nitrate/ammonium inorganic fertilizers and amide organic fertilizers. It also discusses slow-release fertilizers and the nitrogen cycle. Benefits of fertilizers include maintaining soil nitrogen levels for strong plant growth, though overuse can harm plants or contaminate water supplies.
Chlorine is produced industrially through the electrolysis of sodium chloride brine. There are two main methods: mercury cell electrolysis and membrane cell electrolysis. Mercury cell electrolysis was historically used and involves liquid mercury and sodium amalgamation but consumes large amounts of energy. Membrane cell electrolysis uses a semi-permeable membrane to separate the anode and cathode compartments, producing chlorine gas and sodium hydroxide more efficiently. Chlorine has various industrial and medical uses including water treatment, bleaching, and antiseptic applications.
The document describes the process for extracting aluminum from bauxite ore. It involves crushing and grinding the bauxite, mixing it with a caustic soda solution in digesters, cooling the slurry, settling out the impurities, precipitating out the aluminum hydroxide, and calcining it to remove water. The aluminum hydroxide is then smelted using the Hall-Heroult process, which involves dissolving alumina in a molten cryolite bath using an electric current passing between carbon anodes and cathodes to produce molten aluminum.
Sulphuric acid is a colorless, viscous liquid that is highly corrosive. It is produced via the contact process, which involves burning sulfur and reacting the sulfur dioxide with a vanadium catalyst at high temperature to produce sulfur trioxide, which is then dissolved in sulfuric acid. Sulphuric acid is non-flammable but releases toxic fumes when heated and its corrosiveness can cause irritation upon short-term exposure. It has a variety of industrial uses such as in fertilizer, rubber, and oil production.
This document discusses the C++ preprocessor. It describes preprocessor directives like #include, #define, and #ifdef which are used to include files, define macros and symbolic constants, and conditionally compile code. It explains how the preprocessor works before compilation to perform text substitution and control program compilation. Specific directives and operators like #error, #line, and # and ## are also covered.
This document summarizes the contents of Chapter 12 from a textbook on data structures. The chapter covers dynamic data structures like linked lists, stacks, and queues that allow for insertions and removals. It discusses self-referential structures using pointers to link nodes together into different data structures. Specific data structures covered include linked lists, which connect nodes via pointer links, stacks which only allow insertions and removals from one end, and queues which allow insertions at the back and removals from the front. The chapter also discusses dynamic memory allocation using functions like malloc and free to dynamically allocate and free memory for linked data structures. Sample code is provided to demonstrate creating and manipulating each of these common linked data structures.
This document discusses C structures, unions, bit manipulations, and enumerations. It covers defining and initializing structures, accessing structure members, passing structures to functions, the typedef keyword, and an example program for simulating card shuffling and dealing using structures. Unions are defined as containing one data member at a time to conserve storage. Bitwise operators for manipulating bits are also introduced.
This chapter discusses formatted input/output in C using printf and scanf. It covers:
- Streams for input/output
- Formatting output with printf, including printing integers, floats, strings, and other data types
- Using field widths, precisions, and flags to control formatting
- Formatting input with scanf
The key goals are to learn how to use precise output formatting with printf and input formatting with scanf.
The document discusses functions for handling characters and strings in C. It describes character handling functions in the ctype.h library for testing and manipulating characters. It also describes string conversion functions in the stdlib.h library for converting between strings and integer/floating point values. Examples are provided to demonstrate using functions like isdigit(), isalpha(), atoi(), atof() and others.
The document is a chapter about pointers from a textbook on C programming. It discusses pointer variable definitions and initialization, the pointer operators & and *, calling functions by reference using pointers, and the relationships between pointers, arrays, and strings. The chapter objectives are to learn how to use pointers, pass arguments to functions using call by reference with pointers, understand the relationships among pointers, arrays, and strings, and use pointers to functions and arrays of strings.
The document is a chapter about arrays from a programming textbook. It discusses what arrays are, how to declare and initialize arrays, examples of using arrays, passing arrays to functions, sorting arrays, and searching arrays. The chapter contains code examples to demonstrate array concepts like initializing arrays, accessing array elements, and using arrays in programs.
This document outlines a chapter about functions in C programming. It discusses how functions allow programs to be modularized by breaking them into smaller pieces called modules. Functions can be user-defined or come from standard libraries. Functions take parameters as input, perform operations, and return output. Functions allow for abstraction, reusability, and avoidance of code repetition. The chapter covers defining functions, function prototypes, parameters, return values, and calling functions. It also provides examples of commonly used math library functions.
This document discusses control structures in C programming, including repetition statements. It introduces the for and do-while repetition statements, the switch multiple selection statement, and the break and continue program control statements. The key aspects of the for statement are explained, including its initialization, loop continuation test, and increment components. Examples are provided to illustrate counter-controlled repetition using while and for loops.
The document discusses structured program development and control structures in programming. It introduces algorithms, pseudocode, and the basic control structures of sequence, selection (if and if/else statements), and repetition (while loop). It provides examples of using these control structures, including flowcharts, to develop structured programs through stepwise refinement of algorithms.
This document provides an overview of introductory C programming concepts including writing simple programs, using input/output statements, understanding fundamental data types and memory concepts, performing arithmetic operations, and making simple decisions using relational operators. It presents several short example programs to demonstrate key C programming elements like defining variables, accepting user input, performing calculations, and conditional output based on equality comparisons. The document is intended to teach basic C syntax, structures, and programming techniques to newcomers of the language.
This chapter introduces computers and programming. It discusses the history and components of computers, operating systems, and networking. It also covers the evolution of programming languages from machine languages to high-level languages like C and C++. The chapter concludes with an overview of typical software and hardware trends.