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A Red Hat training course is available for Red Hat Enterprise Linux

An introduction to application development tools in Red Hat Enterprise Linux 6

Abstract

This document describes the different features and utilities that make Red Hat Enterprise Linux 6 an ideal enterprise platform for application development.
Effective revision control is essential to all multi-developer projects. It allows all developers in a team to create, review, revise, and document code in a systematic and orderly manner. Red Hat Enterprise Linux 6 supports three of the most popular open-source revision control systems: Git, SVN, and CVS.
The following sections provide a brief overview and references to relevant documentation for each tool.
Git is a distributed revision control system with a peer-to-peer architecture. As opposed to centralized version control systems with a client-server model, Git ensures that each working copy of a Git repository is its exact copy with complete revision history. This not only allows you to work on and contribute to projects without the need to have permission to push your changes to their official repositories, but also makes it possible for you to work with no network connection.

Installing the git Package

In Red Hat Enterprise Linux 6, Git is provided by the git package. To install the git package and all its dependencies on your system, type the following at a shell prompt as root:

Configuring the Default Text Editor

Certain Git commands, such as git commit, require the user to write a short message or make some changes in an external text editor. To determine which text editor to start, Git attempts to read the value of the GIT_EDITOR environment variable, the core.editor configuration option, the VISUAL environment variable, and finally the EDITOR environment variable in this particular order. If none of these options and variables are specified, the git command starts vi as a reasonable default option.
To change the value of the core.editor configuration option in order to specify a different text editor, type the following at a shell prompt:
Replace command with the command to be used to start the selected text editor.

Example 1.1. Configuring the Default Text Editor

To configure Git to use vim as the default text editor, type the following at a shell prompt:

Setting Up User Information

In Git, each commit (or revision) is associated with the full name and email of the person responsible for it. By default, Git uses an identity based on the user name and the host name.
To change the full name associated with your Git commits, type the following at a shell prompt:
To change the email address associated with your Git commits, type:

Example 1.2. Setting Up User Information

To configure Git to use John Doe as your full name and john@example.com as your email address, type the following at a shell prompt:
A repository is a place where Git stores all files that are under revision control, as well as additional data related to these files, such as the complete history of changes or information about who made those changes and when. Unlike in centralized revision control systems like Subversion or CVS, a Git repository and a working directory are usually the same. A typical Git repository also only stores a single project and when publicly accessible, it allows anyone to create its clone with a complete revision history.

Initializing an Empty Repository

To create a new, empty Git repository, change to the directory in which you want to keep the repository and type the following at a shell prompt:
This creates a hidden directory named .git in which all repository information is stored.

Importing Data to a Repository

To put an existing project under revision control, create a Git repository in the directory with the project and run the following command:
This marks all files and directories in the current working directory as ready to be added to the Git repository. To proceed and actually add this content to the repository, commit the changes by typing the following at a shell prompt:
Replace commit message with a short description of your revision. If you omit the -m option, this command allows you to write the commit message in an external text editor. For information on how to configure the default text editor, see the section called “Configuring the Default Text Editor”.
To clone an existing Git repository, type the following at a shell prompt:
Replace git_repository with a URL or a path to the Git repository you want to clone, and directory with a path to the directory in which you want to store the clone.

Adding Files and Directories

To add an existing file to a Git repository and put it under revision control, change to the directory with your local Git repository and type the following at a shell prompt:
Replace file with the file or files you want to add. This command marks the selected file or files as ready to be added to the Git repository. Similarly, to add all files that are stored in a certain directory to a Git repository, type:
Replace directory with the directory or directories you want to add. This command marks all files in the selected directory or directories as ready to be added to the Git repository.
To proceed and actually add this content to the repository, commit the changes as described in Section 1.1.6, “Committing Changes”.

Renaming Files and Directories

To rename an existing file or directory in a Git repository, change to the directory with your local Git repository and type the following at a shell prompt:
Replace old_name with the current name of the file or directory and new_name with the new name. This command renames the selected file or directory and marks it as ready to be renamed in the Git repository.
To proceed and actually rename the content in the repository, commit the changes as described in Section 1.1.6, “Committing Changes”.

Deleting Files and Directories

To delete an existing file from a Git repository, change to the directory with your local Git repository and type the following at a shell prompt:
Replace file with the file or files you want to delete. This command deletes all selected files and marks them as ready to be deleted form the Git repository. Similarly, to delete all files that are stored in a certain directory from a Git repository, type:
Replace directory with the directory or directories you want to delete. This command deletes all selected directories and marks them as ready to be deleted from the Git repository.
To proceed and actually delete this content from the repository, commit the changes as described in Section 1.1.6, “Committing Changes”.

Viewing the Current Status

To determine the current status of your local Git repository, change to the directory with the repository and type the following command at a shell prompt:
This command displays information about all uncommitted changes in the repository (new file, renamed, deleted, or modified) and tells you which changes will be applied the next time you commit them. For information on how to commit your changes, see Section 1.1.6, “Committing Changes”.

Viewing Differences

To view all changes in a Git repository, change to the directory with the repository and type the following at a shell prompt:
This command displays changes between the files in the repository and their latest revision. If you are only interested in changes in a particular file, supply its name on the command line as follows:
Replace file with the file or files you want to view.
To apply your changes to a Git repository and create a new revision, change to the directory with the repository and type the following command at a shell prompt:
Replace commit message with a short description of your revision. This command commits all changes in files that are explicitly marked as ready to be committed. To commit changes in all files that are under revision control, add the -a command line option as follows:
Note that if you omit the -m option, the command allows you to write the commit message in an external text editor. For information on how to configure the default text editor, see the section called “Configuring the Default Text Editor”.
Unlike in centralized version control systems such as CVS or Subversion, when working with Git, project contributors usually do not make their changes in a single, central repository. Instead, they either create a publicly accessible clone of their local repository, or submit their changes to others over email as patches.

Pushing Changes to a Public Repository

To push your changes to a publicly accessible Git repository, change to the directory with your local repository and type the following at a shell prompt:
Replace remote_repository with the name of the remote repository you want to push your changes to. Note that the repository from which you originally cloned your local copy is automatically named origin.

Creating Patches from Individual Commits

To create patches from your commits, change to the directory with your local Git repository and type the following at a shell prompt:
Replace remote_repository with the name of the remote repository from which you made your local copy. This creates a patch for each commit that is not present in this remote repository.
To update your local copy of a Git repository and get the latest changes from a remote repository, change to the directory with your local Git repository and type the following at a shell prompt:
Replace remote_repository with the name of the remote repository. This command fetches information about the current status of the remote repository, allowing you to review these changes before applying them to your local copy. To proceed and merge these changes with what you have in your local Git repository, type:
Alternatively, you can perform both these steps at the same time by using the following command instead:
A detailed description of Git and its features is beyond the scope of this book. For more information about this revision control system, see the resources listed below.

Installed Documentation

  • gittutorial(7) — The manual page named gittutorial provides a brief introduction to Git and its usage.
  • gittutorial-2(7) — The manual page named gittutorial-2 provides the second part of a brief introduction to Git and its usage.
  • Git User's Manual — HTML documentation for Git is located at /usr/share/doc/git-1.7.1/user-manual.html.

Online Documentation

  • Pro Git — The online version of the Pro Git book provides a detailed description of Git, its concepts and its usage.
Apache Subversion, commonly abbreviated as SVN, is a centralized version control system with a client-server architecture. It is a successor to the older Concurrent Versions System (CVS), preserves the same development model, and addresses problems often encountered with CVS.

Installing the subversion Package

In Red Hat Enterprise Linux 6, Subversion is provided by the subversion package. To install the subversion package and all its dependencies on your system, type the following at a shell prompt as root:
This installs a command line Subversion client, a Subversion server, and other related tools to the system.

Setting Up the Default Editor

When using Subversion on the command line, certain commands such as svn import or svn commit require the user to write a short log message. To determine which text editor to start, the svn client application first reads the contents of the environment variable $SVN_EDITOR, then reads more general environment variables $VISUAL and $EDITOR, and if none of these is set, it reports an error.
To persistently change the value of the $SVN_EDITOR environment variable, run the following command:
This adds the export SVN_EDITOR=command line to your ~/.bashrc file. Replace command with a command that runs the editor of your choice (for example, emacs). Note that for this change to take effect in the current shell session, you must execute the commands in ~/.bashrc by typing the following at a shell prompt:

Example 1.3. Setting up the default text editor

To configure the Subversion client to use Emacs as a text editor, type:
A Subversion repository is a central place to store files and directories that are under revision control, as well as additional data such as a complete history of changes or information about who made those changes and when. A typical Subversion repository stores multiple projects in separate subdirectories. When publicly accessible, it allows several developers to create a working copy of any of the subdirectories, make changes, and share these changes with others by committing them back to the repository.

Initializing an Empty Repository

To create a new, empty Subversion repository in a directory of your choice, run the following command:
Note that path is an absolute or relative path to the directory in which you want to store the repository (for example, /var/svn/). If the directory does not exist, svnadmin create creates it for you.

Example 1.4. Initializing a new Subversion repository

To create an empty Subversion repository in the ~/svn/ directory, type:

Importing Data to a Repository

To put an existing project under revision control, run the following command:
Note that local_path is an absolute or relative path to the directory in which you keep the project (use . for the current working directory), svn_repository is a URL of the Subversion repository, and remote_path is the target directory in the Subversion repository (for example, project/trunk).

Example 1.5. Importing a project to a Subversion repository

Imagine that the directory with your project has the following contents:
Also imagine that you have an empty Subversion repository in ~/svn/ (in this example, /home/john/svn/). To import the project under project/trunk in this repository, type:
To check out a working copy of a project in a Subversion repository, run the following command:
This creates a new directory called directory with a working copy of a project in it. Note that svn_repository is a URL of the Subversion repository and remote_path is the subdirectory in which the project is stored.

Example 1.6. Checking out a working copy

Imagine that you have a Subversion repository in the ~/svn/ directory (in this case, /home/john/svn/) and that this repository contains the latest version of a project in the project/trunk subdirectory. To check out a working copy of this project, type:

Adding a File or Directory

To add an existing file to a Subversion repository and put it under revision control, change to the directory with its working copy and run the following command:
Similarly, to add a directory and all files that are in it, type:
This schedules the files and directories for addition to the Subversion repository. To proceed and actually add this content to the repository, run the svn commit command as described in Section 1.2.6, “Committing Changes”.

Example 1.7. Adding a file to a Subversion repository

Imagine that the directory with your working copy of a Subversion repository has the following contents:
With the exception of ChangeLog, all files and directories within this directory are already under revision control. To schedule this file for addition to the Subversion repository, type:

Renaming a File or Directory

To rename an existing file or directory in a Subversion repository, change to the directory with its working copy and run the following command:
This creates a duplicate of the original file or directory, schedules it for addition, and automatically deletes the original. To proceed and actually rename the content in the Subversion repository, run the svn commit command as described in Section 1.2.6, “Committing Changes”.

Example 1.8. Renaming a file in a Subversion repository

Imagine that the directory with your working copy of a Subversion repository has the following contents:
All files in this directory are under revision control. To schedule the LICENSE file for renaming to COPYING, type:
Note that svn move automatically renames the file in your working copy:

Deleting a File or Directory

To remove a file from a Subversion repository, change to the directory with its working copy and run the following command:
Similarly, to remove a directory and all files that are in it, type:
This schedules the files and directories for removal from the Subversion repository. To proceed and actually remove this content from the repository, run the svn commit command as described in Section 1.2.6, “Committing Changes”.

Example 1.9. Deleting a file from a Subversion repository

Imagine that the directory with your working copy of a Subversion repository has the following contents:
All files in this directory are under revision control. To schedule the TODO file for removal from the SVN repository, type:
Note that svn delete automatically deletes the file from your working copy:

Viewing the Status

To determine the current status of a working copy, change to the directory with the working copy and run the following command:
This displays information about all changes to the working copy (A for a file that is scheduled for addition, D for a file that is scheduled for removal, M for a file that contains local changes, C for a file with unresolved conflicts, ? for a file that is not under revision control).

Example 1.10. Viewing the status of a working copy

Imagine that the directory with your working copy of a Subversion repository has the following contents:
With the exception of ChangeLog, which is scheduled for addition to the Subversion repository, all files and directories within this directory are already under revision control. The TODO file, which is also under revision control, has been scheduled for removal and is no longer present in the working copy. The LICENSE file has been renamed to COPYING, and Makefile contains local changes. To display the status of such a working copy, type:

Viewing Differences

To view differences between a working copy and the checked out content, change to the directory with the working copy and run the following command:
This displays changes to all files in the working copy. If you are only interested in changes to a particular file, supply the file name on the command line.

Example 1.11. Viewing changes to a working copy

Imagine that the directory with your working copy of a Subversion repository has the following contents:
All files in this directory are under revision control and Makefile contains local changes. To view these changes, type:
To share your changes with others and commit them to a Subversion repository, change to the directory with its working copy and run the following command:
Note that unless you specify the commit message on the command line, Subversion opens an external text editor for you to write it. For information on how to determine which editor to start, see Section 1.2.1, “Installing and Configuring Subversion”.

Example 1.12. Committing changes to a Subversion repository

Imagine that the directory with your working copy of a Subversion repository has the following contents:
In this working copy, ChangeLog is scheduled for addition to the Subversion repository, Makefile already is under revision control and contains local changes, and the TODO file, which is also under revision control, has been scheduled for removal and is no longer present in the working copy. Additionally, the LICENSE file has been renamed to COPYING. To commit these changes to the Subversion repository, type:
To update a working copy and get the latest changes from a Subversion repository, change to the directory with the working copy and run the following command:

Example 1.13. Updating a working copy

Imagine that the directory with your working copy of a Subversion repository has the following contents:
Also imagine that somebody recently added ChangeLog to the repository, removed the TODO file from it, changed the name of LICENSE to COPYING, and made some changes to Makefile. To update this working copy, type:
A detailed description of all supported features is beyond the scope of this book. For more information, see the resources listed below.

Installed Documentation

  • svn help — The output of the svn help command provides detailed information on the svn usage.
  • svnadmin help — The output of the svnadmin help command provides detailed information on the svnadmin usage.

Online Documentation

  • Version Control with Subversion — The official Subversion website refers to the Version Control with Subversion manual, which provides an in-depth description of Subversion, its administration and its usage.
Concurrent Versions System, commonly abbreviated as CVS, is a centralized version control system with a client-server architecture. It is a successor to the older Revision Control System (RCS), and allows multiple developers to cooperate on the same project while keeping track of every change made to the files that are under revision control.

Installing the cvs Package

In Red Hat Enterprise Linux 6, CVS is provided by the cvs package. To install the cvs package and all its dependencies on your system, type the following at a shell prompt as root:
This installs a command line CVS client, a CVS server, and other related tools to the system.

Setting Up the Default Editor

When using CVS on the command line, certain commands such as cvs import or cvs commit require the user to write a short log message. To determine which text editor to start, the cvs client application first reads the contents of the environment variable $CVSEDITOR, then reads the more general environment variable $EDITOR, and if none of these is set, it starts vi.
To persistently change the value of the $CVSEDITOR environment variable, run the following command:
This adds the export CVSEDITOR=command line to your ~/.bashrc file. Replace command with a command that runs the editor of your choice (for example, emacs). Note that for this change to take effect in the current shell session, you must execute the commands in ~/.bashrc by typing the following at a shell prompt:

Example 1.14. Setting up the default text editor

To configure the CVS client to use Emacs as a text editor, type:
A CVS repository is a central place to store files and directories that are under revision control, as well as additional data such as a complete history of changes or information about who made those changes and when. A typical CVS repository stores multiple projects in separate subdirectories called modules. When publicly accessible, it allows several developers to create a working copy of any of the modules, make changes, and share these changes with others by committing them back to the repository.

Initializing an Empty Repository

To create a new, empty CVS repository in a directory of your choice, run the following command:
Note that path must be an absolute path to the directory in which you want to store the repository (for example, /var/cvs/). Alternatively, you can specify this path by changing the value of the $CVSROOT environment variable:
This allows you to omit the path from cvs init and other CVS-related commands:

Example 1.15. Initializing a new CVS repository

To create an empty CVS repository in the ~/cvs/ directory, type:

Importing Data to a Repository

To put an existing project under revision control, change to the directory in which the project is stored and run the following command:
Note that cvs_repository is a path to the CVS repository, module is the subdirectory in which you want to import the project (such as project), and vendor_tag and release_tag are vendor and release tags.

Example 1.16. Importing a project to a CVS repository

Imagine that the directory with your project has the following contents:
Also imagine that you have an empty CVS repository in ~/cvs/. To import the project under project in this repository with vendor tag mycompany and release tag init, type:
To check out a working copy of a project in a CVS repository, run the following command:
This creates a new directory called module with a working copy of a project in it. Note that cvs_repository is a URL of the CVS repository and module is the subdirectory in which the project is stored (such as project). Alternatively, you can set the $CVSROOT environment variable as follows:
Then you can use the cvs checkout command without the -d option:

Example 1.17. Checking out a working copy

Imagine that you have a CVS repository in ~/cvs/ and that this repository contains a module named project. To check out a working copy of this module, type:

Adding a File

To add an existing file to a CVS repository and put it under revision control, change to the directory with its working copy and run the following command:
This schedules the file for addition to the CVS repository. To proceed and actually add the file to the repository, run the cvs commit command as described in Section 1.3.6, “Committing Changes”.

Example 1.18. Adding a file to a CVS repository

Imagine that the directory with your working copy of a CVS repository has the following contents:
With the exception of ChangeLog, all files and directories within this directory are already under revision control. To schedule this file for addition to the CVS repository, type:

Deleting a File

To remove a file from a CVS repository, change to the directory with its working copy and delete it locally:
Then schedule this file for removal by using the following command:
To proceed and actually remove the file from the repository, run the cvs commit command as described in Section 1.3.6, “Committing Changes”.

Example 1.19. Removing a file from a CVS repository

Imagine that the directory with your working copy of a CVS repository has the following contents:
All files in this directory are under revision control. To schedule the TODO file for removal from the CVS repository, type:

Viewing the Status

To determine the current status of a working copy, change to the directory with the working copy and run the following command:
This displays detailed information about each file that is under revision control, including its current status (such as Up-to-date, Locally Added, Locally Removed, or Locally Modified) and revision. However, if you are only interested in what has changed in your working copy, you can simplify the output by typing the following at a shell prompt:

Example 1.20. Viewing the status of a working copy

Imagine that the directory with your working copy of a CVS repository has the following contents:
With the exception of ChangeLog, which is scheduled for addition to the CVS repository, all files and directories within this directory are already under revision control. The TODO file, which is also under revision control, has been scheduled for removal and is no longer present in the working copy. Finally, Makefile contains local changes. To display the status of such a working copy, type:

Viewing Differences

To view differences between a working copy and the checked out content, change to the directory with the working copy and run the following command:
This displays changes to all files in the working copy. If you are only interested in changes to a particular file, supply the file name on the command line.

Example 1.21. Viewing changes to a working copy

Imagine that the directory with your working copy of a CVS repository has the following contents:
All files in this directory are under revision control and Makefile contains local changes. To view these changes, type:
To share your changes with others and commit them to a CVS repository, change to the directory with its working copy and run the following command:
Note that unless you specify the commit message on the command line, CVS opens an external text editor (vi by default) for you to write it. For information on how to determine which editor to start, see Section 1.3.1, “Installing and Configuring CVS”.

Example 1.22. Committing changes to a CVS repository

Imagine that the directory with your working copy of a CVS repository has the following contents:
In this working copy, ChangeLog is scheduled for addition to the CVS repository, Makefile already is under revision control and contains local changes, and the TODO file, which is also under revision control, has been scheduled for removal and is no longer present in the working copy. To commit these changes to the CVS repository, type:
To update a working copy and get the latest changes from a CVS repository, change to the directory with the working copy and run the following command:

Example 1.23. Updating a working copy

Imagine that the directory with your working copy of a CVS repository has the following contents:
Also imagine that somebody recently added ChangeLog to the repository, removed the TODO file from it, and made some changes to Makefile. To update this working copy, type:
A detailed description of all supported features is beyond the scope of this book. For more information, see the resources listed below.

Installed Documentation

  • cvs(1) — The manual page for the cvs client program provides detailed information on its usage.
Red Hat Enterprise Linux 6 supports the development of custom applications in a wide variety of programming languages using proven, industrial-strength tools. This chapter describes the runtime support libraries provided in Red Hat Enterprise Linux 6.
Compatibility specifies the portability of binary objects and source code across different instances of a computer operating environment. Officially, Red Hat supports current release and two consecutive prior versions. This means that applications built on Red Hat Enterprise Linux 4 and Red Hat Enterprise Linux 5 will continue to run on Red Hat Enterprise Linux 6 as long as they comply with Red Hat guidelines (using the symbols that have been white-listed, for example).
Red Hat understands that as an enterprise platform, customers rely on long-term deployment of their applications. For this reason, applications built against C/C++ libraries with the help of compatibility libraries continue to be supported for ten years.
Source Compatibility
Source compatibility specifies that code will compile and execute in a consistent and predictable way across different instances of the operating environment. This type of compatibility is defined by conformance with specified Application Programming Interfaces (APIs).
Binary Compatibility
Binary Compatibility specifies that compiled binaries in the form of executables and Dynamic Shared Objects (DSOs) will run correctly across different instances of the operating environment. This type of compatibility is defined by conformance with specified Application Binary Interfaces (ABIs).
Compilation of code using -devel packages from the Optional channel is not supported by Red Hat because libraries in the Optional channel are only provided for internal use by the operating system. They are not intended for general application development.
Among other things, this means that security issues in these libraries are only addressed if base operating system components provided by Red Hat are affected. It also means that you may not be able to easily determine whether the built application only uses libraries supported for use by non-Red Hat applications. Therefore, it is recommended to build software on systems that do not have packages from Optional channels installed.
For further information regarding this and all levels of compatibility between core and non-core libraries, see Red Hat Enterprise Linux Life Cycle and the general Red Hat Enterprise Linux Application Compatibility Policies.
Static linking is emphatically discouraged for all Red Hat Enterprise Linux releases. Static linking causes far more problems than it solves, and should be avoided at all costs.
The main drawback of static linking is that it is only guaranteed to work on the system on which it was built, and even then only until the next release of glibc or libstdc++ (in the case of C++). There is no forward or backward compatibility with a static build. Furthermore, any security fixes (or general-purpose fixes) in subsequent updates to the libraries will not be available unless the affected statically linked executables are re-linked.
A few more reasons why static linking should be avoided are:
  • Slower application startup time.
  • Security measures like load address randomization cannot be used.
  • Dynamic loading of shared objects outside of glibc is not supported.
The compat-glibc package is included with Red Hat Enterprise Linux 6, but it is not a runtime package and therefore not required for running anything. It is solely a development package, containing header files and dummy libraries for linking. This allows compiling and linking packages to run in older Red Hat Enterprise Linux versions (using compat-gcc-* against those headers and libraries). Running rpm -qpi compat-glibc-* will provide some information on how to use this package.
The libstdc++ package contains the GNU C++ Standard Library, which is an ongoing project to implement the ISO 14882 Standard C++ library.
Installing the libstdc++ package will provide just enough to satisfy link dependencies (that is, only shared library files). To make full use of all available libraries and header files for C++ development, you must install libstdc++-devel as well. The libstdc++-devel package also contains a GNU-specific implementation of the Standard Template Library (STL).
For Red Hat Enterprise Linux 4, 5, and 6, the C++ language and runtime implementation has remained stable, and thus no compatibility libraries are required for libstdc++.
To use the man pages for library components, install the libstdc++-docs package. This will provide man page information for nearly all resources provided by the library; for example, to view information about the vector container, use its fully-qualified component name: man std::vector.
The libstdc++-docs package also provides manuals and reference information in HTML form in the following directory: /usr/share/doc/libstdc++-docs-version/html/spine.html.
The boost package contains a large number of free peer-reviewed portable C++ source libraries. These libraries are suitable for tasks such as portable file-system access and time or date abstraction, serialization, unit testing, thread creation and multi-process synchronization, parsing, graphing, regular expression manipulation, and many others.
Installing the boost package will provide just enough libraries to satisfy link dependencies (that is, only shared library files). To make full use of all available libraries and header files for C++ development, you must install boost-devel as well.
The boost package is actually a meta-package, containing many library sub-packages. These sub-packages can also be installed individually to provide finer inter-package dependency tracking.
The meta-package does not include dependencies for packages for static linking or packages that depend on the underlying Message Passing Interface (MPI) support.
MPI support is provided in two forms: one for the default Open MPI implementation (package boost-openmpi) and another for the alternate MPICH2 implementation (package boost-mpich2). The selection of the underlying MPI library in use is up to the user and depends on specific hardware details and user preferences. Please note that these packages can be installed in parallel because installed files have unique directory locations.
If static linkage cannot be avoided, the boost-static package will install the necessary static libraries. Both thread-enabled and single-threaded libraries are provided.
The boost-doc package provides manuals and reference information in HTML form located in the following directory: /usr/share/doc/boost-doc-version/index.html.
The main site for the development of Boost is http://boost.org.
The qt package provides the Qt (pronounced 'cute') cross-platform application development framework used in the development of GUI programs. Aside from being a popular 'widget toolkit', Qt is also used for developing non-GUI programs such as console tools and servers. Qt was used in the development of notable projects such as Google Earth, KDE, Opera, OPIE, VoxOx, Skype, VLC media player and VirtualBox. It is produced by Nokia's Qt Development Frameworks division, which came into being after Nokia's acquisition of the Norwegian company Trolltech, the original producer of Qt, on June 17, 2008.
Qt uses standard C++ but makes extensive use of a special pre-processor called the Meta Object Compiler (MOC) to enrich the language. Qt can also be used in other programming languages via language bindings. It runs on all major platforms and has extensive internationalization support. Non-GUI Qt features include SQL database access, XML parsing, thread management, network support, and a unified cross-platform API for file handling.
Distributed under the terms of the GNU Lesser General Public License (among others), Qt is free and open source software. The Red Hat Enterprise Linux 6 version of Qt supports a wide range of compilers, including the GCC C++ compiler and the Visual Studio suite.
Some of the improvements the Red Hat Enterprise Linux 6 version of Qt include:
    • Advanced Graphics Effects: options for opacity, drop-shadows, blur, colorization, and other similar effects
    • Animation and State Machine: create simple or complex animations without the hassle of managing complex code
  • Support for new platforms
    • Windows 7, Mac OSX 10.6, and other desktop platforms are now supported
    • Added support for mobile development; Qt is optimized for the upcoming Maemo 6 platform, and will soon be ported to Maemo 5. In addition, Qt now supports the Symbian platform, with integration for the S60 framework.
    • Added support for Real-Time Operating Systems such as QNX and VxWorks
  • Improved performance, featuring added support for hardware-accelerated rendering (along with other rendering updates)
For more details on updates to Qt included in Red Hat Enterprise Linux 6, see the following links:
Qt Creator is a cross-platform IDE tailored to the requirements of Qt developers. It includes the following graphical tools:
  • Integrated GUI layout and forms designer
  • Integrated, context-sensitive help system
  • Rapid code navigation tools
The qt-doc package provides HTML manuals and references located in /usr/share/doc/qt4/html/. This package also provides the Qt Reference Documentation, which is an excellent starting point for development within the Qt framework.
You can also install further demos and examples from qt-demos and qt-examples. To get an overview of the capabilities of the Qt framework, see /usr/bin/qtdemo-qt4 (provided by qt-demos).
The kdelibs-devel package provides the KDE libraries, which build on Qt to provide a framework for making application development easier. The KDE development framework also helps provide consistency across the KDE desktop environment.
The KDE development framework's architecture in Red Hat Enterprise Linux 6 uses KDE4, which is built on the following technologies:
Plasma
Plasma replaces KDesktop in KDE4. Its implementation is based on the Qt Graphics View Framework, which was introduced in Qt 4.2. For more information about Plasma, see http://techbase.kde.org/Development/Architecture/KDE4/Plasma.
Sonnet
Sonnet is a multilingual spell-checking application that supports automatic language detection, primary/backup dictionaries, and other useful features. It replaces kspell2 in KDE4.
KIO
The KIO library provides a framework for network-transparent file handling, allowing users to easily access files through network-transparent protocols. It also helps provides standard file dialogs.
KJS/KHTML
KJS and KHTML are fully-fledged JavaScript and HTML engines used by different applications native to KDE4 (such as konqueror).
Solid
Solid is a hardware and network awareness framework that allows you to develop applications with hardware interaction features. Its comprehensive API provides the necessary abstraction to support cross-platform application development. For more information, see http://techbase.kde.org/Development/Architecture/KDE4/Solid.
Phonon
Phonon is a multimedia framework that helps you develop applications with multimedia functionalities. It facilitates the usage of media capabilities within KDE. For more information, see http://techbase.kde.org/Development/Architecture/KDE4/Phonon.
Telepathy
Telepathy provides a real-time communication and collaboration framework within KDE4. Its primary function is to tighten integration between different components within KDE. For a brief overview on the project, see http://community.kde.org/Real-Time_Communication_and_Collaboration.
Akonadi
Akonadi provides a framework for centralizing storage of Parallel Infrastructure Management (PIM) components. For more information, see http://techbase.kde.org/Development/Architecture/KDE4/Akonadi.
Online Help within KDE4
KDE4 also features an easy-to-use Qt-based framework for adding online help capabilities to applications. Such capabilities include tooltips, hover-help information, and khelpcenter manuals. For a brief overview on online help within KDE4, see http://techbase.kde.org/Development/Architecture/KDE4/Providing_Online_Help.
KXMLGUI
KXMLGUI is a framework for designing user interfaces using XML. This framework allows you to design UI elements based on 'actions' (defined by the developer) without having to revise source code. For more information, see https://techbase.kde.org/Development/Architecture/KDE3/XMLGUI_Technology.
Strigi
Strigi is a desktop search daemon compatible with many desktop environments and operating systems. It uses its own jstream system which allows for deep indexing of files. For more information on the development of Strigi, see http://www.vandenoever.info/software/strigi/.
KNewStuff2
KNewStuff2 is a collaborative data sharing library used by many KDE4 applications. For more information, see http://techbase.kde.org/Projects/KNS2.
The kdelibs-apidocs package provides HTML documentation for the KDE development framework in /usr/share/doc/HTML/en/kdelibs4-apidocs/. The following links also provide details on KDE-related programming tasks:
The backend program of the GNOME power management infrastructure is gnome-power-manager. It was introduced in Red Hat Enterprise Linux 5 and provides a complete and integrated solution to power management under the GNOME desktop environment. In Red Hat Enterprise Linux 6, the storage-handling parts of hal was replaced by udisks, and the libgnomeprint stack was replaced by print support in gtk2.
This section will detail what versions of gnome-power-management are shipped with the various Red Hat Enterprise Linux versions.
In general, however, Red Hat Enterprise Linux 4 ships with GNOME 2.8, Red Hat Enterprise Linux 5 ships with GNOME 2.16, and Red Hat Enterprise Linux 6 ships with GNOME 2.28.

Table 2.1. Desktop Components Comparison

Red Hat Enterprise Linux Version
GNOME Power Management Desktop Components
5
hal
0.5.8
udisks
N/A
glib2
2.12.3
gtk2
2.10.4
gnome-vfs2
2.16.2
libglade2
2.6.0
libgnomecanvas
2.14.0
gnome-desktop
2.16.0
gnome-media
2.16.1
gnome-python2
2.16.0
libgnome
2.16.0
libgnomeui
2.16.0
libgnomeprint22
2.12.1
libgnomeprintui22
2.12.1
gnome-session
2.16.0
gnome-power-manager
2.16.0
gnome-applets
2.16.0
gnome-panel
2.16.1
There are a number of API changes for glib between versions.

Some of the differences in glib between version 2.4 and 2.12 (or between Red Hat Enterprise Linux 4 and Red Hat Enterprise Linux 5) are:

  • GKeyFile (a key/ini file parser)
  • GMappedFile (a map wrapper)
  • GBookmarkFile (a bookmark file parser)
  • Native atomic ops on s390
  • Atomic reference counting for GObject

Some of the differences in glib between version 2.12 and 2.22 (or between Red Hat Enterprise Linux 5 and Red Hat Enterprise Linux 6) are:

  • GSequence (a list data structure that is implemented as a balanced tree)
  • Support for monotonic clocks
  • GIO (a VFS library to replace gnome-vfs)
  • GChecksum (support for hash algorithms such as MD5 and SHA-256)
  • Support for sockets and network IO in GIO
  • GMarkup performance improvements
Documentation for glib, including indexes of new and deprecated APIs, is shipped in the glib2-devel package.
There are a number of API changes for GTK+ between versions.

Some of the differences in GTK+ between version 2.4 and 2.10 (or between Red Hat Enterprise Linux 4 and Red Hat Enterprise Linux 5) are:

  • GtkAboutDialog
  • GtkFileChooserButton
  • GtkAssistant
  • GtkRecentChooser
  • GtkCellRendererProgress
  • GtkCellRendererSpin
  • Printing Support
  • Ellipsisation support in labels, progressbars and treeviews
  • Improved themability

Some of the differences in GTK+ between version 2.10 and 2.18 (or between Red Hat Enterprise Linux 4 and Red Hat Enterprise Linux 5) are:

  • GtkVolumeButton
  • GtkBuilder to replace libglade
  • GtkMountOperation
  • Scale marks
  • Support runtime font configuration changes
Documentation for GTK+, including indexes of new and deprecated APIs, is shipped in the gtk2-devel package.
The NSS shared database format, introduced on NSS 3.12, is now available in Red Hat Enterprise Linux 6. This encompasses a number of new features and components to improve access and usability.
Included, is the NSS certificate and key database which are now sqlite-based and allow for concurrent access. The legacy key3.db and cert8.db are also replaced with new SQL databases called key4.db and cert9.db. These new databases will store PKCS #11 token objects, which are the same as what is currently stored in cert8.db and key3.db.
Having support for shared databases enables a system-wide NSS database. It resides in /etc/pki/nssdb where globally trusted CA certificates become accessible to all applications. The command rv = NSS_InitReadWrite('sql:/etc/pki/nssdb'); initializes NSS for applications. If the application is run with root privileges, then the system-wide database is available on a read and write basis. However, if it is run with normal user privileges it becomes read only.
Additionally, a PEM PKCS #11 module for NSS allows applications to load into memory certificates and keys stored in PEM-formatted files (for example, those produced by openssl).
The binary compatibility guarantees made by NSS upstream are preserved in NSS for Red Hat Enterprise Linux 6. This guarantee states that NSS used in Red Hat Enterprise Linux 6 is backwards compatible with all older NSS 3.x shared libraries. Therefore, a program linked with an older NSS 3.x shared library will work without recompiling or relinking, and any applications that restrict the use of NSS APIs to the NSS Public Functions remain compatible with future versions of the NSS shared libraries.
Mozilla's wiki page explains the system-wide database rationale in great detail and can be accessed http://wiki.mozilla.org/NSS_Shared_DB_And_LINUX.
The python package adds support for the Python programming language. This package provides the object and cached bytecode files required to enable runtime support for basic Python programs. It also contains the python interpreter and the pydoc documentation tool. The python-devel package contains the libraries and header files required for developing Python extensions.
Red Hat Enterprise Linux also ships with numerous python-related packages. By convention, the names of these packages have a python prefix or suffix. Such packages are either library extensions or python bindings to an existing library. For instance, dbus-python is a Python language binding for D-Bus.
Note that both cached bytecode (*.pyc/*.pyo files) and compiled extension modules (*.so files) are incompatible between Python 2.4 (used in Red Hat Enterprise Linux 5) and Python 2.6 (used in Red Hat Enterprise Linux 6). As such, you will be required to rebuild any extension modules you use that are not part of Red Hat Enterprise Linux.
The Red Hat Enterprise Linux 6 version of Python features various language changes. For information about these changes, see the following project resources:
  • What's New in Python 2.5: http://docs.python.org/whatsnew/2.5.html
  • What's New in Python 2.6: http://docs.python.org/whatsnew/2.6.html
Both resources also contain advice on porting code developed using previous Python versions.
For more information about Python, see man python. You can also install python-docs, which provides HTML manuals and references in the following location:
file:///usr/share/doc/python-docs-version/html/index.html
For details on library and language components, use pydoc component_name. For example, pydoc math will display the following information about the math Python module:
The main site for the Python development project is hosted on python.org.
The java-1.6.0-openjdk package adds support for the Java programming language. This package provides the java interpreter. The java-1.6.0-openjdk-devel package contains the javac compiler, as well as the libraries and header files required for developing Java extensions.
Similarly, Red Hat Enterprise Linux also provides Java 7 via the java-1.7.0-openjdk* packages and Java 8 via the java-1.8.0-openjdk* packages.
For more information about Java, see man java. Some associated utilities also have their own respective man pages.
You can also install other Java documentation packages for more details about specific Java utilities. By convention, such documentation packages have the javadoc suffix (for example, dbus-java-javadoc).
The main site for the development of Java is hosted on http://openjdk.java.net/. The main site for the library runtime of Java is hosted on http://icedtea.classpath.org.
The ruby package provides the Ruby interpreter and adds support for the Ruby programming language. The ruby-devel package contains the libraries and header files required for developing Ruby extensions.
Red Hat Enterprise Linux also ships with numerous ruby-related packages. By convention, the names of these packages have a ruby or rubygem prefix or suffix. Such packages are either library extensions or Ruby bindings to an existing library.
  • rubygem-flexmock
  • ruby-irb
  • ruby-libs
  • ruby-rdoc
  • ruby-saslwrapper
  • ruby-tcltk
For information about updates to the Ruby language in Red Hat Enterprise Linux 6, see the following resources:
  • file:///usr/share/doc/ruby-version/NEWS-version
For more information about Ruby, see man ruby. You can also install ruby-docs, which provides HTML manuals and references in the following location:
The main site for the development of Ruby is hosted on http://www.ruby-lang.org. The http://www.ruby-doc.org site also contains Ruby documentation.
The perl package adds support for the Perl programming language. This package provides Perl core modules, the Perl Language Interpreter, and the PerlDoc tool.
Red Hat also provides various perl modules in package form; these packages are named with the perl-* prefix. These modules provide stand-alone applications, language extensions, Perl libraries, and external library bindings.
For a full list of the differences between the Perl versions see the following documents:
Perl 5.12 Updates
  • Experimental APIs allow Perl to be extended with 'pluggable' keywords and syntax.
  • Perl will be able to keep accurate time well past the 'Y2038' barrier.
  • Package version numbers can be directly specified in 'package' statements.
  • Perl warns the user about the use of depreciated features by default.
The Perl 5.12 delta can be accessed at http://perldoc.perl.org/perl5120delta.html.
Perl 5.14 Updates
  • Improved support for IPv6.
  • A new /r flag that makes s/// substitutions non-destructive.
  • New regular expression flags to control whether matched strings should be treated as ASCII or Unicode.
  • Less memory and CPU usage than previous releases.
The Perl 5.14 delta can be accessed at http://perldoc.perl.org/perl5140delta.html.
Perl 5.16 Updates
  • $$ variable is writable.
  • Accessing Unicode database files directly is now depreciated; use Unicode::UCD instead.
  • Version::Requirements is depreciated in favor of CPAN::Meta::Requirements.
    • assert.pl
    • bigint.pl
    • cacheout.pl
    • ctime.pl
    • exceptions.pl
    • flush.pl
    • getopt.pl
    • hostname.pl
    • lib/find{,depth}.pl
    • newgetopt.pl
    • open3.pl
    • hellwords.pl
    • tainted.pl
    • timelocal.pl
    The Perl 5.16 delta can be accessed at http://perldoc.perl.org/perl5160delta.html.
Perl's capabilities can be extended by installing additional modules. These modules come in the following forms:
Official Red Hat RPM
The official module packages can be installed with yum or rpm from the Red Hat Enterprise Linux repositories. They are installed to /usr/share/perl5 and either /usr/lib/perl5 for 32bit architectures or /usr/lib64/perl5 for 64bit architectures.
Modules from CPAN
Use the cpan tool provided by the perl-CPAN package to install modules directly from the CPAN website. They are installed to /usr/local/share/perl5 and either /usr/local/lib/perl5 for 32bit architectures or /usr/local/lib64/perl5 for 64bit architectures.
Third party module package
Third party modules are installed to /usr/share/perl5/vendor_perl and either /usr/lib/perl5/vendor_perl for 32bit architectures or /usr/lib64/perl5/vendor_perl for 64bit architectures.
Custom module package / manually installed module
These should be placed in the same directories as third-party modules. That is, /usr/share/perl5/vendor_perl and either /usr/lib/perl5/vendor_perl for 32bit architectures or /usr/lib64/perl5/vendor_perl for 64bit architectures.
If an official version of a module is already installed, installing its non-official version can create conflicts in the /usr/share/man directory.
The perldoc tool provides documentation on language and core modules. To learn more about a module, use perldoc module_name. For example, perldoc CGI will display the following information about the CGI core module:
For details on Perl functions, use perldoc -f function_name. For example, perldoc -f split wil display the following information about the split function:
Current PerlDoc documentation can be found on perldoc.perl.org.
Core and external modules are documented on the Comprehensive Perl Archive Network.
Red Hat Enterprise Linux 6 includes many packages used for software development, including tools for compiling and building source code. This chapter discusses several of these packages and tools used to compile source code.
The GNU Compiler Collection (GCC) is a set of tools for compiling a variety of programming languages (including C, C++, ObjectiveC, ObjectiveC++, Fortran, and Ada) into highly optimized machine code. These tools include various compilers (like gcc and g++), run-time libraries (like libgcc, libstdc++, libgfortran, and libgomp), and miscellaneous other utilities.
Application Binary Interfaces specified by the GNU C, C++, Fortran and Java Compiler include:
  • Calling conventions. These specify how arguments are passed to functions and how results are returned from functions.
  • Register usage conventions. These specify how processor registers are allocated and used.
  • Object file formats. These specify the representation of binary object code.
  • Size, layout, and alignment of data types. These specify how data is laid out in memory.
  • Interfaces provided by the runtime environment. Where the documented semantics do not change from one version to another they must be kept available and use the same name at all times.
The default system C compiler included with Red Hat Enterprise Linux 6 is largely compatible with the C99 ABI standard. Deviations from the C99 standard in GCC 4.4 are tracked online.
In addition to the C ABI, the Application Binary Interface for the GNU C++ Compiler specifies the binary interfaces required to support the C++ language, such as:
  • Creation and propagation of exceptions
  • Constructors and destructors
  • Layout, alignment, and padding of classes and derived classes
  • Virtual function implementation details, such as the layout and alignment of virtual tables
The default system C++ compiler included with Red Hat Enterprise Linux 6 conforms to the C++ ABI defined by the Itanium C++ ABI (1.86).
Although every effort has been made to keep each version of GCC compatible with previous releases, some incompatibilities do exist.
ABI incompatibilities between Red Hat Enterprise Linux 6 and Red Hat Enterprise Linux 5

The following is a list of known incompatibilities between the Red Hat Enterprise Linux 6 and 5 toolchains.

  • Passing/returning structs with flexible array members by value changed in some cases on Intel 64 and AMD64.
  • Passing/returning of unions with long double members by value changed in some cases on Intel 64 and AMD64.
  • Passing/returning structs with complex float member by value changed in some cases on Intel 64 and AMD64.
  • Passing of 256-bit vectors on x86, Intel 64 and AMD64 platforms changed when -mavx is used.
  • There have been multiple changes in passing of _Decimal{32,64,128} types and aggregates containing those by value on several targets.
  • Packing of packed char bitfields changed in some cases.
ABI incompatibilities between Red Hat Enterprise Linux 5 and Red Hat Enterprise Linux 4

The following is a list of known incompatibilities between the Red Hat Enterprise Linux 5 and 4 toolchains.

  • There have been changes in the library interface specified by the C++ ABI for thread-safe initialization of function-scope static variables.
  • On Intel 64 and AMD64, the medium model for building applications where data segment exceeds 4GB, was redesigned to match the latest ABI draft at the time. The ABI change results in incompatibility among medium model objects.
The compiler flag -Wabi can be used to get diagnostics indicating where these constructs appear in source code, though it will not catch every single case. This flag is especially useful for C++ code to warn whenever the compiler generates code that is known to be incompatible with the vendor-neutral C++ ABI.
Excluding the incompatibilities listed above, the GCC C and C++ language ABIs are mostly ABI compatible. The vast majority of source code will not encounter any of the known issues, and can be considered compatible.
Compatible ABIs allow the objects created by compiling source code to be portable to other systems. In particular, for Red Hat Enterprise Linux, this allows for upward compatibility. Upward compatibility is defined as the ability to link shared libraries and objects, created using a version of the compilers in a particular Red Hat Enterprise Linux release, with no problems. This includes new objects compiled on subsequent Red Hat Enterprise Linux releases.
The C ABI is considered to be stable, and has been so since at least Red Hat Enterprise Linux 3 (again, barring any incompatibilities mentioned in the above lists). Libraries built on Red Hat Enterprise Linux 3 and later can be linked to objects created on a subsequent environment (Red Hat Enterprise Linux 4, Red Hat Enterprise Linux 5, and Red Hat Enterprise Linux 6).
The C++ ABI is considered to be stable, but less stable than the C ABI, and only as of Red Hat Enterprise Linux 4 (corresponding to GCC version 3.4 and above.). As with C, this is only an upward compatibility. Libraries built on Red Hat Enterprise Linux 4 and above can be linked to objects created on a subsequent environment (Red Hat Enterprise Linux 5, and Red Hat Enterprise Linux 6).
To force GCC to generate code compatible with the C++ ABI in Red Hat Enterprise Linux releases prior to Red Hat Enterprise Linux 4, some developers have used the -fabi-version=1 option. This practice is not recommended. Objects created this way are indistinguishable from objects conforming to the current stable ABI, and can be linked (incorrectly) amongst the different ABIs, especially when using new compilers to generate code to be linked with old libraries that were built with tools prior to Red Hat Enterprise Linux 4.
The above incompatibilities make it incredibly difficult to maintain ABI shared library sanity between releases, especially when developing custom libraries with multiple dependencies outside of the core libraries. Therefore, if shared libraries are developed, it is highly recommend that a new version is built for each Red Hat Enterprise Linux release.
Two items that are important are the changes and enhancements in the underlying tools used by the compiler, and the compatibility between the different versions of a language's compiler.
Changes and new features in tools like ld (distributed as part of the binutils package) or in the dynamic loader (ld.so, distributed as part of the glibc package) can subtly change the object files that the compiler produces. These changes mean that object files moving to the current release of Red Hat Enterprise Linux from previous releases may lose functionality, behave differently at runtime, or otherwise interoperate in a diminished capacity. Known problem areas include:
  • In Red Hat Enterprise Linux 6 this is passed to ld by default, whereas Red Hat Enterprise Linux 5 ld doesn't recognize it.
  • In Red Hat Enterprise Linux 6 this directive allows .debug_frame, .eh_frame or both to be omitted from .cfi* directives. In Red Hat Enterprise Linux 5 only .eh_frame is omitted.
  • as, ld, ld.so, and gdbSTB_GNU_UNIQUE and %gnu_unique_symbol support
    In Red Hat Enterprise Linux 6 more debug information is generated and stored in object files. This information relies on new features detailed in the DWARF standard, and also on new extensions not yet standardized. In Red Hat Enterprise Linux 5, tools like as, ld, gdb, objdump, and readelf may not be prepared for this new information and may fail to interoperate with objects created with the newer tools. In addition, Red Hat Enterprise Linux 5 produced object files do not support these new features; these object files may be handled by Red Hat Enterprise Linux 6 tools in a sub-optimal manner.
    An outgrowth of this enhanced debug information is that the debuginfo packages that ship with system libraries allow you to do useful source level debugging into system libraries if they are installed. See Section 4.2, “Installing Debuginfo Packages” for more information on debuginfo packages.
Object file changes, such as the ones listed above, may interfere with the portable use of prelink.
To compile using GCC tools, first install the binutils and gcc packages. Doing so will also install several dependencies.
In brief, the tools work via the gcc command. This is the main driver for the compiler. It can be used from the command line to pre-process or compile a source file, link object files and libraries, or perform a combination thereof. By default, gcc takes care of the details and links in the provided libgcc library.
Conversely, using GCC tools from the command line interface consumes less system resources. This also allows finer-grained control over compilers; GCC's command line tools can even be used outside of the graphical mode (runlevel 5).
Basic compilation of a C language program using GCC is easy. Start with the following simple program:

Example 3.1. hello.c

The following procedure illustrates the compilation process for C in its most basic form.

Procedure 3.1. Compiling a 'Hello World' C Program

  1. Compile Example 3.1, “hello.c” into an executable with:
    Ensure that the resulting binary hello is in the same directory as hello.c.
Basic compilation of a C++ language program using GCC is similar. Start with the following simple program:

Example 3.2. hello.cc

The following procedure illustrates the compilation process for C++ in its most basic form.

Procedure 3.2. Compiling a 'Hello World' C++ Program

  1. Compile Example 3.2, “hello.cc” into an executable with:
    Ensure that the resulting binary hello is in the same directory as hello.cc.
To use basic compilation involving multiple files or object files, start with the following two source files:

Example 3.3. one.c

Example 3.4. two.c

The following procedure illustrates a simple, multi-file compilation process in its most basic form.

Procedure 3.3. Compiling a Program with Multiple Source Files

  1. Compile Example 3.3, “one.c” into an executable with:
    Ensure that the resulting binary one.o is in the same directory as one.c.
  2. Compile Example 3.4, “two.c” into an executable with:
    Ensure that the resulting binary two.o is in the same directory as two.c.
  3. Compile the two object files one.o and two.o into a single executable with:
    Ensure that the resulting binary hello is in the same directory as one.o and two.o.
Different projects require different optimization options. There is no one-size-fits-all approach when it comes to optimization, but here are a few guidelines to keep in mind.

It is very important to choose the correct architecture for instruction scheduling. By default GCC produces code optimized for the most common processors, but if the CPU on which your code will run is known, the corresponding -mtune= option to optimize the instruction scheduling, and -march= option to optimize the instruction selection should be used.

The option -mtune= optimizes instruction scheduling to fit your architecture by tuning everything except the ABI and the available instruction set. This option will not choose particular instructions, but instead will tune your program in such a way that executing on a particular architecture will be optimized. For example, if an Intel Core2 CPU will predominantly be used, choose -mtune=core2. If the wrong choice is made, the program will still run, but not optimally on the given architecture. The architecture on which the program will most likely run should always be chosen.
The option -march= optimizes instruction selection. As such, it is important to choose correctly as choosing incorrectly will cause your program to fail. This option selects the instruction set used when generating code. For example, if the program will be run on an AMD K8 core based CPU, choose -march=k8. Specifying the architecture with this option will imply -mtune=.
The -mtune= and -march= commands should only be used for tuning and selecting instructions within a given architecture, not to generate code for a different architecture (also known as cross-compiling). For example, this is not to be used to generate PowerPC code from an Intel 64 and AMD64 platform.
For a complete list of the available options for both -march= and -mtune=, see the GCC documentation available here: GCC 4.4.4 Manual: Hardware Models and Configurations

The compiler flag -O2 is a good middle of the road option to generate fast code. It produces the best optimized code when the resulting code size is not large. Use this when unsure what would best suit.

When code size is not an issue, -O3 is preferable. This option produces code that is slightly larger but runs faster because of a more frequent inline of functions. This is ideal for floating point intensive code.
The other general purpose optimization flag is -Os. This flag also optimizes for size, and produces faster code in situations where a smaller footprint will increase code locality, thereby reducing cache misses.
Use -frecord-gcc-switches when compiling objects. This records the options used to build objects into objects themselves. After an object is built, it determines which set of options were used to build it. The set of options are then recorded in a section called .GCC.command.line within the object and can be examined with the following:
It is very important to test and try different options with a representative data set. Often, different modules or objects can be compiled with different optimization flags in order to produce optimal results. See Section 3.1.3.5, “Using Profile Feedback to Tune Optimization Heuristics” for additional optimization tuning.

3.1.3.5. Using Profile Feedback to Tune Optimization Heuristics

During the transformation of a typical set of source code into an executable, tens of hundreds of choices must be made about the importance of speed in one part of code over another, or code size as opposed to code speed. By default, these choices are made by the compiler using reasonable heuristics, tuned over time to produce the optimum runtime performance. However, GCC also has a way to teach the compiler to optimize executables for a specific machine in a specific production environment. This feature is called profile feedback.
Profile feedback is used to tune optimizations such as:
  • Branch prediction
  • Inter-procedural constant propagation
Profile feedback compiles a program first to generate a program that is run and analyzed and then a second time to optimize with the gathered data.

Procedure 3.4. Using Profile Feedback

  1. The application must be instrumented to produce profiling information by compiling it with -fprofile-generate.
  2. Run the application to accumulate and save the profiling information.
Step three will use the profile information gathered in step one to tune the compiler's heuristics while optimizing the code into a final executable.

Procedure 3.5. Compiling a Program with Profiling Feedback

  1. Compile source.c to include profiling instrumentation:
  2. Run executable to gather profiling information:
  3. Recompile and optimize source.c with profiling information gathered in step one:
Multiple data collection runs, as seen in step two, will accumulate data into the profiling file instead of replacing it. This allows the executable in step two to be run multiple times with additional representative data in order to collect even more information.
The executable must run with representative levels of both the machine being used and a respective data set large enough for the input required. This ensures optimal results are achieved.
By default, GCC will generate the profile data into the directory where step one was performed. To generate this information elsewhere, compile with -fprofile-dir=DIR where DIR is the preferred output directory.
The format of the compiler feedback data file changes between compiler versions. It is imperative that the program compilation is repeated with each version of the compiler.
On a 64-bit host, GCC will build executables that can only run on 64-bit hosts. However, GCC can be used to build executables that will run both on 64-bit hosts and on 32-bit hosts.
To build 32-bit binaries on a 64-bit host, first install 32-bit versions of any supporting libraries the executable may require. This must at least include supporting libraries for glibc and libgcc, and libstdc++ if the program is a C++ program. On Intel 64 and AMD64, this can be done with:
yum install glibc-devel.i686 libgcc.i686 libstdc++-devel.i686
There may be cases where it is useful to to install additional 32-bit libraries that a program may require. For example, if a program uses the db4-devel libraries to build, the 32-bit version of these libraries can be installed with:
The .i686 suffix on the x86 platform (as opposed to x86-64) specifies a 32-bit version of the given package. For PowerPC architectures, the suffix is ppc (as opposed to ppc64).
After the 32-bit libraries have been installed, the -m32 option can be passed to the compiler and linker to produce 32-bit executables. Provided the supporting 32-bit libraries are installed on the 64-bit system, this executable will be able to run on both 32-bit systems and 64-bit systems.

Procedure 3.6. Compiling a 32-bit Program on a 64-bit Host

  1. On a 64-bit system, compile hello.c into a 64-bit executable with:
  2. Ensure that the resulting executable is a 64-bit binary:
    The command file on a 64-bit executable will include ELF 64-bit in its output, and ldd will list /lib64/libc.so.6 as the main C library linked.
  3. On a 64-bit system, compile hello.c into a 32-bit executable with:
  4. Ensure that the resulting executable is a 32-bit binary:
    The command file on a 32-bit executable will include ELF 32-bit in its output, and ldd will list /lib/libc.so.6 as the main C library linked.
If you have not installed the 32-bit supporting libraries you will get an error similar to this for C code:
A similar error would be triggered on C++ code:
These errors indicate that the supporting 32-bit libraries have not been properly installed as explained at the beginning of this section.
Also important is to note that building with -m32 will in not adapt or convert a program to resolve any issues arising from 32/64-bit incompatibilities. For tips on writing portable code and converting from 32-bits to 64-bits, see the paper entitled Porting to 64-bit GNU/Linux Systems in the Proceedings of the 2003 GCC Developers Summit.
For more information about GCC compilers, see the man pages for cpp, gcc, g++, gcj, and gfortran.
The following online user manuals are also available:
The main site for the development of GCC is gcc.gnu.org.
GNU Autotools is a suite of command line tools that allow developers to build applications on different systems, regardless of the installed packages or even Linux distribution. These tools aid developers in creating a configure script. This script runs prior to builds and creates the top-level Makefiles required to build the application. The configure script may perform tests on the current system, create additional files, or run other directives as per parameters provided by the builder.
The Autotools suite's most commonly-used tools are:
autoconf
Generates the configure script from an input file (configure.ac, for example)
automake
Creates the Makefile for a project on a specific system
autoscan
Generates a preliminary input file (that is, configure.scan), which can be edited to create a final configure.ac to be used by autoconf
All tools in the Autotools suite are part of the Development Tools group package. You can install this package group to install the entire Autotools suite, or use yum to install any tools in the suite as you wish.
The Autotools suite is also integrated into the Eclipse IDE via the Autotools plug-in. This plug-in provides an Eclipse graphical user interface for Autotools, which is suitable for most C/C++ projects.
As of Red Hat Enterprise Linux 6, this plug-in only supports two templates for new C/C++ projects:
The empty project template is used when importing projects into the C/C++ Development Toolkit that already support Autotools. Future updates to the Autotools plug-in will include additional graphical user interfaces (wizards, for example) for creating shared libraries and other complex scenarios.
The Red Hat Enterprise Linux 6 version of the Autotools plug-in also does not integrate git or mercurial into Eclipse. As such, Autotools projects that use git repositories will be required to be checked out outside the Eclipse workspace. Afterwards, you can specify the source location for such projects in Eclipse. Any repository manipulation (commits, or updates for example) are done via the command line.
The most crucial function of Autotools is the creation of the configure script. This script tests systems for tools, input files, and other features it can use in order to build the project [1]. The configure script generates a Makefile which allows the make tool to build the project based on the system configuration.
To create the configure script, first create an input file. Then feed it to an Autotools utility in order to create the configure script. This input file is typically configure.ac or Makefile.am; the former is usually processed by autoconf, while the later is fed to automake.
If a Makefile.am input file is available, the automake utility creates a Makefile template (that is, Makefile. in), which may see information collected at configuration time. For example, the Makefile may have to link to a particular library if and only if that library is already installed. When the configure script runs, automake will use the Makefile. in templates to create a Makefile.
If a configure.ac file is available instead, then autoconf will automatically create the configure script based on the macros invoked by configure.ac. To create a preliminary configure.ac, use the autoscan utility and edit the file accordingly.
Red Hat Enterprise Linux 6 includes man pages for autoconf, automake, autoscan and most tools included in the Autotools suite. In addition, the Autotools community provides extensive documentation on autoconf and automake on the following websites:
The following is an online book describing the use of Autotools. Although the above online documentation is the recommended and most up to date information on Autotools, this book is a good alternative and introduction.
For information on how to create Autotools input files, see:
The following upstream example also illustrates the use of Autotools in a simple hello program:
Each executable or shared library built with Red Hat Enterprise Linux Server 6 or later is assigned a unique identification 160-bit SHA-1 string, generated as a checksum of selected parts of the binary. This allows two builds of the same program on the same host to always produce consistent build-ids and binary content.
Display the build-id of a binary with the following command:
Unique identificators of binaries are useful in cases such as analysing core files, documented Section 4.2.1, “Installing Debuginfo Packages for Core Files Analysis”.

[1] For information about tests that configure can perform, see the following link:
Useful, well-written software generally goes through several different phases of application development, allowing ample opportunity for mistakes to be made. Some phases come with their own set of mechanisms to detect errors. For example, during compilation an elementary semantic analysis is often performed to make sure objects, such as variables and functions, are adequately described.
The error-checking mechanisms performed during each application development phase aims to catch simple and obvious mistakes in code. The debugging phase helps to bring more subtle errors to light that fell through the cracks during routine code inspection.
Red Hat Enterprise Linux uses ELF for executable binaries, shared libraries, or debuginfo files. Within these debuginfo ELF files, the DWARF format is used. Version 3 of DWARF is used in ELF files (that is, gcc -g is equivalent to gcc -gdwarf-3). DWARF debuginfo includes:
  • names of all the compiled functions and variables, including their target addresses in binaries
  • source files used for compilation, including their source line numbers
STABS is occasionally used with UNIX. STABS is an older, less capable format. Its use is discouraged by Red Hat. GCC and GDB support STABS production and consumption on a best effort basis only.
Within these ELF files, the GCC debuginfo level is also used. The default is level 2, where macro information is not present; level 3 has C/C++ macro definitions included, but the debuginfo can be very large with this setting. The command for the default gcc -g is the same as gcc -g2. To change the macro information to level three, use gcc -g3.
There are multiple levels of debuginfo available. Use the command readelf -WS file to see which sections are used in a file.

Table 4.1. debuginfo levels

Command
Stripped
or
Only the symbols required for runtime linkage with shared libraries are present.
ELF symbols
Only the names of functions and variables are present, no binding to the source files and no types.
DWARF debuginfo with macros
The source file names and line numbers are known, including types.
DWARF debuginfo with macros
Similar to gcc -g but the macros are known to GDB.
GDB never interprets the source files, it only displays them as text. Use gcc -g and its variants to store the information into DWARF.
Compiling a program or library with gcc -rdynamic is discouraged. For specific symbols, use gcc -Wl, --dynamic-list=... instead. If gcc -rdynamic is used, the strip command or -s gcc option have no effect. This is because all ELF symbols are kept in the binary for possible runtime linkage with shared libraries.
ELF symbols can be read by the readelf -s file command.
DWARF symbols are read by the readelf -w file command.
The command readelf -wi file is a good verification of debuginfo, compiled within your program. The commands strip file or gcc -s are commonly accidentally executed on the output during various compilation stages of the program.
The readelf -w file command can also be used to show a special section called .eh_frame with a format and purpose is similar to the DWARF section .debug_frame. The .eh_frame section is used for runtime C++ exception resolution and is present even if -g gcc option was not used. It is kept in the primary RPM and is never present in the debuginfo RPMs.
Debuginfo RPMs contain the sections .symtab and .debug_*. Neither .eh_frame, .eh_frame_hdr, nor .dynsym are moved or present in debuginfo RPMs as those sections are needed during program runtime.
Red Hat Enterprise Linux also provides -debuginfo packages for all architecture-dependent RPMs included in the operating system. A packagename-debuginfo-version-release.architecture.rpm package contains detailed information about the relationship of the package source files and the final installed binary. The debuginfo packages contain both .debug files, which in turn contain DWARF debuginfo and the source files used for compiling the binary packages.
Most of the debugger functionality is missed if attempting to debug a package without having its debuginfo equivalent installed. For example, the names of exported shared library functions will still be available, but the matching source file lines will not be without the debuginfo package installed.
Use gcc compilation option -g for your own programs. The debugging experience is better if no optimizations (gcc option -O, such as -O2) is applied with -g.
For Red Hat Enterprise Linux 6, the debuginfo packages are now available on a new channel on the Red Hat Network. To install the -debuginfo package of a package (that is, typically packagename-debuginfo), first the machine has to be subscribed to the corresponding Debuginfo channel. For example, for Red Hat Enterprise Server 6, the corresponding channel would be Red Hat Enterprise Linux Server Debuginfo (v. 6).
Red Hat Enterprise Linux system packages are compiled with optimizations (gcc option -O2). This means that some variables will be displayed as <optimized out>. Stepping through code will 'jump' a little but a crash can still be analyzed. If some debugging information is missing because of the optimizations, the right variable information can be found by disassembling the code and matching it to the source manually. This is applicable only in exceptional cases and is not suitable for regular debugging.
For system packages, GDB informs the user if it is missing some debuginfo packages that limit its functionality.
If the system package to be debugged is known, use the command suggested by GDB above. It will also automatically install all the debug packages packagename depends on.

4.2.1. Installing Debuginfo Packages for Core Files Analysis

A core file is a representation of the memory image at the time of a process crash. For bug reporting of system program crashes, Red Hat recommends the use of the ABRT tool, explained in the Automatic Bug Reporting Tool chapter in the Red Hat Deployment Guide. If ABRT is not suitable for your purposes, the steps it automates are explained here.
If the ulimit -c unlimited setting is in use when a process crashes, the core file is dumped into the current directory. The core file contains only the memory areas modified by the process from the original state of disk files. In order to perform a full analysis of a crash, a core file is required to have:
  • the executable binary which has crashed, such as /usr/sbin/sendmail
  • all the shared libraries loaded in the binary when it crashed
  • .debug files and source files (both stored in debuginfo RPMs) for the executable and all of its loaded libraries
For a proper analysis, either the exact version-release.architecture for all the RPMs involved or the same build of your own compiled binaries is needed. At the time of the crash, the application may have already recompiled or been updated by yum on the disk, rendering the files inappropriate for the core file analysis.
The core file contains build-ids of all the binaries involved. For more information on build-id, see Section 3.3, “build-id Unique Identification of Binaries”. The contents of the core file can be displayed by:
  • The in-memory address where the specific binary was mapped to (for example, 0x400000 in the first line).
  • The size of the binary (for example, +0x207000 in the first line).
  • The 160-bit SHA-1 build-id of the binary (for example, 2818b2009547f780a5639c904cded443e564973e in the first line).
  • The in-memory address where the build-id bytes were stored (for example, @0x400284 in the first line).
  • The on-disk binary file, if available (for example, /bin/sleep in the first line). This was found by eu-unstrip for this module.
  • The on-disk debuginfo file, if available (for example, /usr/lib/debug/bin/sleep.debug). However, best practice is to use the binary file reference instead.
  • The shared library name as stored in the shared library list in the core file (for example, libc.so.6 in the third line).
For each build-id (for example, ab/cdef0123456789012345678901234567890123) a symbolic link is included in its debuginfo RPM. Using the /bin/sleep executable above as an example, the coreutils-debuginfo RPM contains, among other files:
In some cases (such as loading a core file), GDB does not know the name, version, or release of a name-debuginfo-version-release.rpm package; it only knows the build-id. In such cases, GDB suggests a different command:
The version-release.architecture of the binary package packagename-debuginfo-version-release.architecture.rpm must be an exact match. If it differs then GDB cannot use the debuginfo package. Even the same version-release.architecture from a different build leads to an incompatible debuginfo package. If GDB reports a missing debuginfo, ensure to recheck:
rpm -q packagename packagename-debuginfo
The version-release.architecture definitions should match.
rpm -V packagename packagename-debuginfo
This command should produce no output, except possibly modified configuration files of packagename, for example.
rpm -qi packagename packagename-debuginfo
The version-release.architecture should display matching information for Vendor, Build Date, and Build Host. For example, using a CentOS debuginfo RPM for a Red Hat Enterprise Linux RPM package will not work.
If the required build-id is known, the following command can query which RPM contains it:
For example, a version of an executable which matches the core file can be installed by:
Similar methods are available if the binaries are not packaged into RPMs and stored in yum repositories. It is possible to create local repositories with custom application builds by using /usr/bin/createrepo.
Fundamentally, like most debuggers, GDB manages the execution of compiled code in a very closely controlled environment. This environment makes possible the following fundamental mechanisms necessary to the operation of GDB:
  • Inspect and modify memory within the code being debugged (for example, reading and setting variables).
  • Control the execution state of the code being debugged, principally whether it's running or stopped.
  • Detect the execution of particular sections of code (for example, stop running code when it reaches a specified area of interest to the programmer).
  • Detect access to particular areas of memory (for example, stop running code when it accesses a specified variable).
  • Execute portions of code (from an otherwise stopped program) in a controlled manner.
  • Detect various programmatic asynchronous events such as signals.
The operation of these mechanisms rely mostly on information produced by a compiler. For example, to view the value of a variable, GDB has to know:
  • The nature of the variable
This means that displaying a double-precision floating point value requires a very different process from displaying a string of characters. For something complex like a structure, GDB has to know not only the characteristics of each individual elements in the structure, but the morphology of the structure as well.
GDB requires the following items in order to fully function:
Debug Information
Much of GDB's operations rely on a program's debug information. While this information generally comes from compilers, much of it is necessary only while debugging a program, that is, it is not used during the program's normal execution. For this reason, compilers do not always make that information available by default — GCC, for instance, must be explicitly instructed to provide this debugging information with the -g flag.
To make full use of GDB's capabilities, it is highly advisable to make the debug information available first to GDB. GDB can only be of very limited use when run against code with no available debug information.
Source Code
One of the most useful features of GDB (or any other debugger) is the ability to associate events and circumstances in program execution with their corresponding location in source code. This location normally refers to a specific line or series of lines in a source file. This, of course, would require that a program's source code be available to GDB at debug time.
GDB literally contains dozens of commands. This section describes the most fundamental ones.
br (breakpoint)
The breakpoint command instructs GDB to halt execution upon reaching a specified point in the execution. That point can be specified a number of ways, but the most common are just as the line number in the source file, or the name of a function. Any number of breakpoints can be in effect simultaneously. This is frequently the first command issued after starting GDB.
r (run)
The run command starts the execution of the program. If run is executed with any arguments, those arguments are passed on to the executable as if the program has been started normally. Users normally issue this command after setting breakpoints.
Before an executable is started, or once the executable stops at, for example, a breakpoint, the state of many aspects of the program can be inspected. The following commands are a few of the more common ways things can be examined.
p (print)
The print command displays the value of the argument given, and that argument can be almost anything relevant to the program. Usually, the argument is the name of a variable of any complexity, from a simple single value to a structure. An argument can also be an expression valid in the current language, including the use of program variables and library functions, or functions defined in the program being tested.
bt (backtrace)
The backtrace displays the chain of function calls used up until the execution was terminated. This is useful for investigating serious bugs (such as segmentation faults) with elusive causes.
l (list)
When execution is stopped, the list command shows the line in the source code corresponding to where the program stopped.
The execution of a stopped program can be resumed in a number of ways. The following are the most common.
c (continue)
The continue command restarts the execution of the program, which will continue to execute until it encounters a breakpoint, runs into a specified or emergent condition (for example, an error), or terminates.
n (next)
Like continue, the next command also restarts execution; however, in addition to the stopping conditions implicit in the continue command, next will also halt execution at the next sequential line of code in the current source file.
s (step)
Like next, the step command also halts execution at each sequential line of code in the current source file. However, if execution is currently stopped at a source line containing a function call, GDB stops execution after entering the function call (rather than executing it).
fini (finish)
Like the aforementioned commands, the finish command resumes executions, but halts when execution returns from a function.
q (quit)
h (help)
The help command provides access to its extensive internal documentation. The command takes arguments: help breakpoint (or h br), for example, shows a detailed description of the breakpoint command. See the help output of each command for more detailed information.
This section will describe a basic execution of GDB, using the following simple program:
The following procedure illustrates the debugging process in its most basic form.

Procedure 4.1. Debugging a 'Hello World' Program

  1. Compile hello.c into an executable with the debug flag set, as in:
    Ensure that the resulting binary hello is in the same directory as hello.c.
  2. Run gdb on the hello binary, that is, gdb hello.
  3. After several introductory comments, gdb will display the default GDB prompt:
  4. The variable hello is global, so it can be seen even before the main procedure starts:
    Note that the print targets hello[0] and *hello require the evaluation of an expression, as does, for example, *(hello + 1):
  5. Next, list the source:
    The list reveals that the fprintf call is on line 8. Apply a breakpoint on that line and resume the code:
  6. Finally, use the next command to step past the fprintf call, executing it:
The following sections describe more complex applications of GDB.
In many real-world cases, a program may perform its task well during the first few thousand times; it may then start crashing or encountering errors during its eight thousandth iteration of the task. Debugging programs like this can be difficult, as it is hard to imagine a programmer with the patience to issue a continue command thousands of times just to get to the iteration that crashed.
Situations like this are common in real life, which is why GDB allows programmers to attach conditions to a breakpoint. For example, consider the following program:
To set a conditional breakpoint at the GDB prompt:
With this condition, the program execution will eventually stop with the following output:
Inspect the breakpoint information (using info br) to review the breakpoint status:
Among the more challenging bugs confronting programmers is where one program (the parent) makes an independent copy of itself (a fork). That fork then creates a child process which, in turn, fails. Debugging the parent process may or may not be useful. Often the only way to get to the bug may be by debugging the child process, but this is not always possible.
The set follow-fork-mode feature is used to overcome this barrier allowing programmers to follow a a child process instead of the parent process.
set follow-fork-mode parent
The original process is debugged after a fork. The child process runs unimpeded. This is the default.
set follow-fork-mode child
The new process is debugged after a fork. The parent process runs unimpeded.
show follow-fork-mode
Display the current debugger response to a fork call.
Use the set detach-on-fork command to debug both the parent and the child processes after a fork, or retain debugger control over them both.
set detach-on-fork on
The child process (or parent process, depending on the value of follow-fork-mode) will be detached and allowed to run independently. This is the default.
set detach-on-fork off
Both processes will be held under the control of GDB. One process (child or parent, depending on the value of follow-fork-mode) is debugged as usual, while the other is suspended.
show detach-on-fork
Consider the following program:
This program, compiled with the command gcc -g fork.c -o fork -lpthread and examined under GDB will show:
GDB followed the parent process and allowed the child process (process 3840) to continue execution.
The following is the same test using set follow-fork-mode child.
This can be permanent by adding the setting to the appropriate .gdbinit.
For example, if set follow-fork-mode ask is added to ~/.gdbinit, then ask mode becomes the default mode.
GDB has the ability to debug individual threads, and to manipulate and examine them independently. This functionality is not enabled by default. To do so use set non-stop on and set target-async on. These can be added to .gdbinit. Once that functionality is turned on, GDB is ready to conduct thread debugging.
For example, the following program creates two threads. These two threads, along with the original thread executing main makes a total of three threads.
Compile this program in order to examine it under GDB.
First set breakpoints on all thread functions; thread1, thread2, and main.
Note that the command info threads provides a summary of the program's threads and some details about their current state. In this case there is only one thread that has been created so far.
Here, two more threads are created. The star indicates the thread currently under focus. Also, the newly created threads have hit the breakpoint set for them in their initialization functions. Namely, thread2() and thread3().
To begin real thread debugging, use the thread <thread number> command to switch the focus to another thread.
Thread 2 stopped at line 20 in its function thread2().
Above, a few lines of thread2 printed the counter count2 and left thread 2 at line 23 as is seen by the output of 'info threads'.
Thread three is a little different in that it has a sleep statement and executes slowly. Think of it as a representation of an uninteresting IO thread. Because this thread is uninteresting, continue its execution uninterrupted, using the continue.
Take note of the & at the end of the continue. This allows the GDB prompt to return so other commands can be executed. Using the interrupt, execution can be stopped should thread 3 become interesting again.
It is also possible to go back to the original main thread and examine it some more.
As can be seen from the output of info threads, the other threads are where they were left, unaffected by the debugging of thread 1.
GDB uses the command line as its default interface. However, it also has an API called machine interface (MI). MI allows IDE developers to create other user interfaces to GDB.
Eclipse (CDT)
A graphical debugger interface integrated with the Eclipse development environment. More information can be found at the Eclipse website.
Nemiver
A graphical debugger interface which is well suited to the GNOME Desktop Environment. More information can be found at the Nemiver website
Emacs
A GDB interface which is integrated with the emacs. More information can be found at the Emacs website
Variable Tracking at Assignments (VTA) is a new infrastructure included in GCC used to improve variable tracking during optimizations. This allows GCC to produce more precise, meaningful, and useful debugging information for GDB, SystemTap, and other debugging tools.
When GCC compiles code with optimizations enabled, variables are renamed, moved around, or even removed altogether. As such, optimized compiling can cause a debugger to report that some variables have been <optimized out>. With VTA enabled, optimized code is internally annotated to ensure that optimization passes to transparently keep track of each variable's value, regardless of whether the variable is moved or removed. The effect of this is more parameter and variable values available, even for the optimized (gcc -O2 -g built) code. It also displays the <optimized out> message less.
VTA's benefits are more pronounced when debugging applications with inlined functions. Without VTA, optimization could completely remove some arguments of an inlined function, preventing the debugger from inspecting its value. With VTA, optimization will still happen, and appropriate debugging information will be generated for any missing arguments.
VTA is enabled by default when compiling code with optimizations and debugging information enabled (that is, gcc -O -g or, more commonly, gcc -O2 -g). To disable VTA during such builds, add the -fno-var-tracking-assignments. In addition, the VTA infrastructure includes the new gcc option -fcompare-debug. This option tests code compiled by GCC with debug information and without debug information: the test passes if the two binaries are identical. This test ensures that executable code is not affected by any debugging options, which further ensures that there are no hidden bugs in the debug code. Note that -fcompare-debug adds significant cost in compilation time. See man gcc for details about this option.
For more information about the infrastructure and development of VTA, see A Plan to Fix Local Variable Debug Information in GCC, available at the following link:
A slide deck version of this whitepaper is also available at http://people.redhat.com/aoliva/papers/vta/slides.pdf.
The GDB command print outputs comprehensive debugging information for a target application. GDB aims to provide as much debugging data as it can to users; however, this means that for highly complex programs the amount of data can become very cryptic.
In addition, GDB does not provide any tools that help decipher GDB print output. GDB does not even empower users to easily create tools that can help decipher program data. This makes the practice of reading and understanding debugging data quite arcane, particularly for large, complex projects.
For most developers, the only way to customize GDB print output (and make it more meaningful) is to revise and recompile GDB. However, very few developers can actually do this. Further, this practice will not scale well, particularly if the developer must also debug other programs that are heterogeneous and contain equally complex debugging data.
To address this, the Red Hat Enterprise Linux 6 version of GDB is now compatible with Python pretty-printers. This allows the retrieval of more meaningful debugging data by leaving the introspection, printing, and formatting logic to a third-party Python script.
Compatibility with Python pretty-printers gives you the chance to truly customize GDB output as you see fit. This makes GDB a more viable debugging solution to a wider range of projects, since you now have the flexibility to adapt GDB output as required, and with greater ease. Further, developers with intimate knowledge of a project and a specific programming language are best qualified in deciding what kind of output is meaningful, allowing them to improve the usefulness of that output.
Ltspice could not open include file
The Python pretty-printers implementation allows users to automatically inspect, format, and print program data according to specification. These specifications are written as rules implemented via Python scripts. This offers the following benefits:

To pass program data to a set of registered Python pretty-printers, the GDB development team added hooks to the GDB printing code. These hooks were implemented with safety in mind: the built-in GDB printing code is still intact, allowing it to serve as a default fallback printing logic. As such, if no specialized printers are available, GDB will still print debugging data the way it always did. This ensures that GDB is backwards-compatible; users who do not require pretty-printers can still continue using GDB.

This new 'Python-scripted' approach allows users to distill as much knowledge as required into specific printers. As such, a project can have an entire library of printer scripts that parses program data in a unique manner specific to its user's requirements. There is no limit to the number of printers a user can build for a specific project; what's more, being able to customize debugging data script by script offers users an easier way to re-use and re-purpose printer scripts — or even a whole library of them.

The best part about this approach is its lower barrier to entry. Python scripting is comparatively easy to learn and has a large library of free documentation available online. In addition, most programmers already have basic to intermediate experience in Python scripting, or in scripting in general.

Here is a small example of a pretty printer. Consider the following C++ program:
This is compiled with the command g++ -g fruit.cc -o fruit. Now, examine this program with GDB.
The output of {fruit = 1} is correct because that is the internal representation of 'fruit' in the data structure 'Fruit'. However, this is not easily read by humans as it is difficult to tell which fruit the integer 1 represents.
To solve this problem, write the following pretty printer:
The line gdb.pretty_printers.append (lookup_type) adds the function lookup_type to GDB's list of printer lookup functions.
The function lookup_type is responsible for examining the type of object to be printed, and returning an appropriate pretty printer. The object is passed by GDB in the parameter val. val.type is an attribute which represents the type of the pretty printer.
FruitPrinter is where the actual work is done. More specifically in the to_string function of that Class. In this function, the integer fruit is retrieved using the python dictionary syntax self.val['fruit']. Then the name is determined using that value. The string returned by this function is the string that will be printed to the user.
After creating fruit.py, it must then be loaded into GDB with the following command:
The GDB and Python Pretty-Printers whitepaper provides more details on this feature. This whitepaper also includes details and examples on how to write your own Python pretty-printer as well as how to import it into GDB. See the following link for more information:
Developers profile programs to focus attention on the areas of the program that have the largest impact on performance. The types of data collected include what section of the program consumes the most processor time, and where memory is allocated. Profiling collects data from the actual program execution. Thus, the quality of the data collect is influenced by the actual tasks being performed by the program. The tasks performed during profiling should be representative of actual use; this ensures that problems arising from realistic use of the program are addressed during development.
Red Hat Enterprise Linux 6 includes a number of different tools (Valgrind, OProfile, perf, and SystemTap) to collect profiling data. Each tool is suitable for performing specific types of profile runs, as described in the following sections.
Valgrind is an instrumentation framework for building dynamic analysis tools that can be used to profile applications in detail. The default installation alrready provides five standard tools. Valgrind tools are generally used to investigate memory management and threading problems. The Valgrind suite also includes tools that allow the building of new profiling tools as required.
Valgrind provides instrumentation for user-space binaries to check for errors, such as the use of uninitialized memory, improper allocation/freeing of memory, and improper arguments for systemcalls. Its profiling tools can be used by normal users on most binaries; however, compared to other profilers, Valgrind profile runs are significantly slower. To profile a binary, Valgrind rewrites its executable and instruments the rewritten binary. Valgrind's tools are most useful for looking for memory-related issues in user-space programs; it is not suitable for debugging time-specific issues or kernel-space instrumentation and debugging.
Valgrind reports are most useful and accurate whhen debuginfo packages are installed for the programs or libraries under investigation. See Section 4.2, “Installing Debuginfo Packages”.
The Valgrind suite is composed of the following tools:
memcheck
This tool detects memory management problems in programs by checking all reads from and writes to memory and intercepting all system calls to malloc, new, free, and delete. memcheck is perhaps the most used Valgrind tool, as memory management problems can be difficult to detect using other means. Such problems often remain undetected for long periods, eventually causing crashes that are difficult to diagnose.
cachegrind
cachegrind is a cache profiler that accurately pinpoints sources of cache misses in code by performing a detailed simulation of the I1, D1 and L2 caches in the CPU. It shows the number of cache misses, memory references, and instructions accruing to each line of source code; cachegrind also provides per-function, per-module, and whole-program summaries, and can even show counts for each individual machine instructions.
callgrind
Like cachegrind, callgrind can model cache behavior. However, the main purpose of callgrind is to record callgraphs data for the executed code.
massif
massif is a heap profiler; it measures how much heap memory a program uses, providing information on heap blocks, heap administration overheads, and stack sizes. Heap profilers are useful in finding ways to reduce heap memory usage. On systems that use virtual memory, programs with optimized heap memory usage are less likely to run out of memory, and may be faster as they require less paging.
helgrind
In programs that use the POSIX pthreads threading primitives, helgrind detects synchronization errors. Such errors are:
  • Potential deadlocks arising from lock ordering problems
  • Data races (that is, accessing memory without adequate locking)
Valgrind also allows you to develop your own profiling tools. In line with this, Valgrind includes the lackey tool, which is a sample that can be used as a template for generating your own tools.
The valgrind package and its dependencies install all the necessary tools for performing a Valgrind profile run. To profile a program with Valgrind, use:
See Section 5.1.1, “Valgrind Tools” for a list of arguments for toolname. In addition to the suite of Valgrind tools, none is also a valid argument for toolname; this argument allows you to run a program under Valgrind without performing any profiling. This is useful for debugging or benchmarking Valgrind itself.
You can also instruct Valgrind to send all of its information to a specific file. To do so, use the option --log-file=filename. For example, to check the memory usage of the executable file hello and send profile information to output, use:
See Section 5.1.3, “Additional information” for more information on Valgrind, along with other available documentation on the Valgrind suite of tools.
For more extensive information on Valgrind, see man valgrind. Red Hat Enterprise Linux also provides a comprehensive Valgrind Documentation book available as PDF and HTML in:
  • /usr/share/doc/valgrind-version/valgrind_manual.pdf
OProfile is a low overhead, system-wide performance monitoring tool provided by the oprofile package. It uses the performance monitoring hardware on the processor to retrieve information about the kernel and executables on the system, such as when memory is referenced, the number of second-level cache requests, and the number of hardware interrupts received. OProfile is also able to profile applications that run in a Java Virtual Machine (JVM).
The following is a selection of the tools provided by OProfile. Note that the legacy opcontrol tool and the new operf tool are mutually exclusive.
ophelp
Displays available events for the system’s processor along with a brief description of each.
operf
Intended to replace opcontrol. The operf tool uses the Linux Performance Events subsystem, allowing you to target your profiling more precisely, as a single process or system-wide, and allowing OProfile to co-exist better with other tools using the performance monitoring hardware on your system. Unlike opcontrol, no initial setup is required, and it can be used without the root privileges unless the --system-wide option is in use.
opimport
Converts sample database files from a foreign binary format to the native format for the system. Only use this option when analyzing a sample database from a different architecture.
opannotate
Creates an annotated source for an executable if the application was compiled with debugging symbols.
opreport
opcontrol
This tool is used to start and stop the OProfile daemon (oprofiled) and configure a profile session.
oprofiled
Runs as a daemon to periodically write sample data to disk.
Legacy mode (opcontrol, oprofiled, and post-processing tools) remains available, but it is no longer the recommended profiling method. For a detailed description of the legacy mode, see the Configuring OProfile Using Legacy Mode chapter in the System Administrator's Guide.
operf is the recommended tool for collecting profiling data. The tool does not require any initial configuration, and all options are passed to it on the command line. Unlike the legacy opcontrol tool, operf can run without root privileges. See the Using operf chapter in the System Administrator's Guide for detailed instructions on how to use the operf tool.

Example 5.1. Using operf to Profile a Java Program

In the following example, the operf tool is used to collect profiling data from a Java (JIT) program, and the opreport tool is then used to output per-symbol data.
  1. Install the demonstration Java program used in this example. It is a part of the java-1.8.0-openjdk-demo package, which is included in the Optional channel. See Enabling Supplementary and Optional Repositories for instructions on how to use the Optional channel. When the Optional channel is enabled, install the package:
  2. Install the oprofile-jit package for OProfile to be able to collect profiling data from Java programs:
  3. Change into the directory with the demonstration program:
  4. Change into the home directory and analyze the collected data:
OProfile 0.9.9, which is included in Red Hat Enterprise Linux 7, contains a number of improvements over previous versions. The new version also supports the utilization of the Linux Performance Events subsystem using the new operf command.
A new operf program is now available that allows non-root users to profile single processes. This can also be used for system-wide profiling, but in this case, root authority is required.
  • Intel Haswell processors
  • AMD Generic Performance Events
OProfile 0.9.9 has a few known problems and limitations. These are:
  • AMD Instruction Based Sampling (IBS) is not currently supported with the new operf program. Use the legacy opcontrol commands for IBS profiling.
  • The type of the sample header mtime field has changed to u64, which makes it impossible to process sample data acquired using previous versions of OProfile.
  • opcontrol fails to allocate the hardware performance counters it needs if the NMI watchdog is enabled. The NMI watchdog, which monitors system interrupts, uses the perf tool, which reserves all performance counters.
For more extensive information on OProfile, see the oprofile(1) manual page. Red Hat Enterprise Linux also provides two comprehensive guides to OProfile in file:///usr/share/doc/oprofile-version/:
OProfile Manual
A comprehensive manual with detailed instructions on the setup and use of OProfile is found at file:///usr/share/doc/oprofile-version/oprofile.html
OProfile Internals
Documentation on the internal workings of OProfile, useful for programmers interested in contributing to the OProfile upstream, can be found at file:///usr/share/doc/oprofile-version/internals.html
SystemTap is a useful instrumentation platform for probing running processes and kernel activity on the Linux system. To execute a probe:
  1. Write SystemTap scripts that specify which system events (for example, virtual file system reads, packet transmissions) should trigger specified actions (for example, print, parse, or otherwise manipulate data).
  2. SystemTap translates the script into a C program, which it compiles into a kernel module.
  3. SystemTap loads the kernel module to perform the actual probe.
SystemTap scripts are useful for monitoring system operation and diagnosing system issues with minimal intrusion into the normal operation of the system. You can quickly instrument running system test hypotheses without having to recompile and re-install instrumented code. To compile a SystemTap script that probes kernel-space, SystemTap uses information from three different kernel information packages:
  • kernel-variant-debuginfo-version
The kernel information package in Red Hat Enterprise Linux 6 is now named kernel-debuginfo-common-arch-version. It was originally kernel-debuginfo-common-version in Red Hat Enterprise Linux 5.
These kernel information packages must match the kernel to be probed. In addition, to compile SystemTap scripts for multiple kernels, the kernel information packages of each kernel must also be installed.
For more detailed information about SystemTap, see the following Red Hat documentation:

5.4. Performance Counters for Linux (PCL) Tools and perf

Performance Counters for Linux (PCL) is a new kernel-based subsystem that provides a framework for collecting and analyzing performance data. These events will vary based on the performance monitoring hardware and the software configuration of the system. Red Hat Enterprise Linux 6 includes this kernel subsystem to collect data and the user-space tool perf to analyze the collected performance data.
The PCL subsystem can be used to measure hardware events, including retired instructions and processor clock cycles. It can also measure software events, including major page faults and context switches. For example, PCL counters can compute the Instructions Per Clock (IPC) from a process's counts of instructions retired and processor clock cycles. A low IPC ratio indicates the code makes poor use of the CPU. Other hardware events can also be used to diagnose poor CPU performance.
Performance counters can also be configured to record samples. The relative frequency of samples can be used to identify which regions of code have the greatest impact on performance.
Useful perf commands include the following:
perf stat
This perf command provides overall statistics for common performance events, including instructions executed and clock cycles consumed. Options allow selection of events other than the default measurement events.
perf record
This perf command records performance data into a file which can be later analyzed using perf report.
perf report
This perf command reads the performance data from a file and analyzes the recorded data.
perf list
Not
This perf command lists the events available on a particular machine. These events will vary based on the performance monitoring hardware and the software configuration of the system.
Use perf help to obtain a complete list of perf commands. To retrieve man page information on each perf command, use perf help command.
Using the basic PCL infrastructure for collecting statistics or samples of program execution is relatively straightforward. This section provides simple examples of overall statistics and sampling.
To collect statistics on make and its children, use the following command:
The perf command collects a number of different hardware and software counters. It then prints the following information:
The perf tool can also record samples. For example, to record data on the make command and its children, use:
This prints out the file in which the samples are stored, along with the number of samples collected:
As of Red Hat Enterprise Linux 6.4, a new functionality to the {} group syntax has been added that allows the creation of event groups based on the way they are specified on the command line.
The current --group or -g options remain the same; if it is specified for record, stat, or top command, all the specified events become members of a single group with the first event as a group leader.
The new {} group syntax allows the creation of a group like:
The above results in a single event group containing cycles and faults events, with the cycles event as the group leader.
All groups are created with regards to threads and CPUs. As such, recording an event group within two threads on a server with four CPUs will create eight separate groups.
It is possible to use a standard event modifier for a group. This spans over all events in the group and updates each event modifier settings.
The above command results in the :kp modifier being used for faults, and the :p modifier being used for the cache-references event.
Performance Counters for Linux (PCL) Tools conflict with OProfile

Both OProfile and Performance Counters for Linux (PCL) use the same hardware Performance Monitoring Unit (PMU). If OProfile is currently running while attempting to use the PCL perf command, an error message like the following occurs when starting OProfile:

To use the perf command, first shut down OProfile:
You can then analyze perf.data to determine the relative frequency of samples. The report output includes the command, object, and function for the samples. Use perf report to output an analysis of perf.data. For example, the following command produces a report of the executable that consumes the most time:
The column on the left shows the relative frequency of the samples. This output shows that make spends most of this time in xsltproc and the pdfxmltex. To reduce the time for the make to complete, focus on xsltproc and pdfxmltex. To list the functions executed by xsltproc, run:
The ftrace framework provides users with several tracing capabilities, accessible through an interface much simpler than SystemTap's. This framework uses a set of virtual files in the debugfs file system; these files enable specific tracers. The ftrace function tracer outputs each function called in the kernel in real time; other tracers within the ftrace framework can also be used to analyze wakeup latency, task switches, kernel events, and the like.
You can also add new tracers for ftrace, making it a flexible solution for analyzing kernel events. The ftrace framework is useful for debugging or analyzing latencies and performance issues that take place outside of user-space. Unlike other profilers documented in this guide, ftrace is a built-in feature of the kernel.
The Red Hat Enterprise Linux 6 kernels have been configured with the CONFIG_FTRACE=y option. This option provides the interfaces required by ftrace. To use ftrace, mount the debugfs file system as follows:
All the ftrace utilities are located in /sys/kernel/debug/tracing/. View the /sys/kernel/debug/tracing/available_tracers file to find out what tracers are available for your kernel:
To use a specific tracer, write it to /sys/kernel/debug/tracing/current_tracer. For example, wakeup traces and records the maximum time it takes for the highest-priority task to be scheduled after the task wakes up. To use it:
echo wakeup > /sys/kernel/debug/tracing/current_tracer
To start or stop tracing, write to /sys/kernel/debug/tracing/tracing_on, as in:
echo 1 > /sys/kernel/debug/tracing/tracing_on (enables tracing)
echo 0 > /sys/kernel/debug/tracing/tracing_on (disables tracing)
The results of the trace can be viewed from the following files:
/sys/kernel/debug/tracing/trace
/sys/kernel/debug/tracing/trace_pipe
This file contains the same output as /sys/kernel/debug/tracing/trace, but is meant to be piped into a command. Unlike /sys/kernel/debug/tracing/trace, reading from this file consumes its output.
The ftrace framework is fully documented in the following files:
  • ftrace - Function Tracer: file:///usr/share/doc/kernel-doc-version/Documentation/trace/ftrace.txt
  • function tracer guts: file:///usr/share/doc/kernel-doc-version/Documentation/trace/ftrace-design.txt
Red Hat Enterprise Linux 6 offers the Doxygen tool for generating documentation from source code and for writing standalone documentation.
Doxygen is a documentation tool that creates reference material both online in HTML and offline in Latex. It does this from a set of documented source files which makes it easy to keep the documentation consistent and correct with the source code.
Doxygen has support for output in:
  • PostScript
  • Compressed HTML
Doxygen supports the following programming languages:
  • C++
  • Objective -C
  • Java
  • PHP
  • Fortran
Doxygen uses a configuration file to determine its settings, therefore it is paramount that this be created correctly. Each project requires its own configuration file. The most painless way to create the configuration file is with the command doxygen -g config-file. This creates a template configuration file that can be easily edited. The variable config-file is the name of the configuration file. If it is committed from the command it is called Doxyfile by default. Another useful option while creating the configuration file is the use of a minus sign (-) as the file name. This is useful for scripting as it will cause Doxygen to attempt to read the configuration file from standard input (stdin).
The configuration file consists of a number of variables and tags, similar to a simple Makefile. For example:
For the most part these can be left alone but should it be required to edit them see the configuration page of the Doxygen documentation website for an extensive explanation of all the tags available. There is also a GUI interface called doxywizard. If this is the preferred method of editing then documentation for this function can be found on the Doxywizard usage page of the Doxygen documentation website.
There are eight tags that are useful to become familiar with.

For small projects consisting mainly of C or C++ source and header files it is not required to change anything. However, if the project is large and consists of a source directory or tree, then assign the root directory or directories to the INPUT tag.

File patterns (for example, *.cpp or *.h) can be added to this tag allowing only files that match one of the patterns to be parsed.

Setting this to yes will allow recursive parsing of a source tree.

These are used to further fine-tune the files that are parsed by adding file patterns to avoid. For example, to omit all test directories from a source tree, use EXCLUDE_PATTERNS = */test/*.

When this is set to yes, doxygen will pretend that everything in the source files is documented to give an idea of how a fully documented project would look. However, warnings regarding undocumented members will not be generated in this mode; set it back to no when finished to correct this.

By setting the SOURCE_BROWSER tag to yes doxygen will generate a cross-reference to analyze a piece of software's definition in its source files with the documentation existing about it. These sources can also be included in the documentation by setting INLINE_SOURCES to yes.

Running doxygen config-file creates html, rtf, latex, xml, and / or man directories in whichever directory doxygen is started in, containing the documentation for the corresponding filetype.

This documentation can be viewed with a HTML browser that supports cascading style sheets (CSS), as well as DHTML and Javascript for some sections. Point the browser (for example, Mozilla, Safari, Konqueror, or Internet Explorer 6) to the index.html in the html directory.

Doxygen writes a Makefile into the latex directory in order to make it easy to first compile the Latex documentation. To do this, use a recent teTeX distribution. What is contained in this directory depends on whether the USE_PDFLATEX is set to no. Where this is true, typing make while in the latex directory generates refman.dvi. This can then be viewed with xdvi or converted to refman.ps by typing make ps. Note that this requires dvips.

There are a number of commands that may be useful. The command make ps_2on1 prints two pages on one physical page. It is also possible to convert to a PDF if a ghostscript interpreter is installed by using the command make pdf. Another valid command is make pdf_2on1. When doing this set PDF_HYPERLINKS and USE_PDFLATEX tags to yes as this will cause Makefile will only contain a target to build refman.pdf directly.

This file is designed to import into Microsoft Word by combining the RTF output into a single file: refman.rtf. Some information is encoded using fields but this can be shown by selecting all (CTRL+A or Edit -> select all) and then right-click and select the toggle fields option from the drop down menu.

The output into the xml directory consists of a number of files, each compound gathered by doxygen, as well as an index.xml. An XSLT script, combine.xslt, is also created that is used to combine all the XML files into a single file. Along with this, two XML schema files are created, index.xsd for the index file, and compound.xsd for the compound files, which describe the possible elements, their attributes, and how they are structured.

The documentation from the man directory can be viewed with the man program after ensuring the manpath has the correct man directory in the man path. Be aware that due to limitations with the man page format, information such as diagrams, cross-references and formulas will be lost.

There are three main steps to document the sources.
  1. First, ensure that EXTRACT_ALL is set to no so warnings are correctly generated and documentation is built properly. This allows doxygen to create documentation for documented members, files, classes and namespaces.
  2. There are two ways this documentation can be created:
    A special documentation block
    This comment block, containing additional marking so Doxygen knows it is part of the documentation, is in either C or C++. It consists of a brief description, or a detailed description. Both of these are optional. What is not optional, however, is the in body description. This then links together all the comment blocks found in the body of the method or function.
    While more than one brief or detailed description is allowed, this is not recommended as the order is not specified.
    The following will detail the ways in which a comment block can be marked as a detailed description:
    • C-style comment block, starting with two asterisks (*) in the JavaDoc style.
    • C-style comment block using the Qt style, consisting of an exclamation mark (!) instead of an extra asterisk.
    • The beginning asterisks on the documentation lines can be left out in both cases if that is preferred.
    • A blank beginning and end line in C++ also acceptable, with either three forward slashes or two forward slashes and an exclamation mark.
    • Alternatively, in order to make the comment blocks more visible a line of asterisks or forward slashes can be used.
      Note that the two forwards slashes at the end of the normal comment block start a special comment block.
    There are three ways to add a brief description to documentation.
    • To add a brief description use brief above one of the comment blocks. This brief section ends at the end of the paragraph and any further paragraphs are the detailed descriptions.
    • By setting JAVADOC_AUTOBRIEF to yes, the brief description will only last until the first dot followed by a space or new line. Consequentially limiting the brief description to a single sentence.
      This can also be used with the above mentioned three-slash comment blocks (///).
    • The third option is to use a special C++ style comment, ensuring this does not span more than one line.
      The blank line in the above example is required to separate the brief description and the detailed description, and JAVADOC_AUTOBRIEF must to be set to no.
    Examples of how a documented piece of C++ code using the Qt style can be found on the Doxygen documentation website
    It is also possible to have the documentation after members of a file, struct, union, class, or enum. To do this add a < marker in the comment block.
    or
    For brief descriptions after a member use:
    Examples of these and how the HTML is produced can be viewed on the Doxygen documentation website
    Documentation at other places
    While it is preferable to place documentation in front of the code it is documenting, at times it is only possible to put it in a different location, especially if a file is to be documented; after all it is impossible to place the documentation in front of a file. This is best avoided unless it is absolutely necessary as it can lead to some duplication of information.
    To do this it is important to have a structural command inside the documentation block. Structural commands start with a backslash () or an at-sign (@) for JavaDoc and are followed by one or more parameters.
    In the above example the command class is used. This indicates that the comment block contains documentation for the class 'Test'. Others are:
    • union: document a union
    • fn: document a function
    • var: document a variable, typedef, or enum value
    • typedef: document a type definition
    • namespace: document a namespace
    • interface: document an IDL interface
  3. Next, the contents of a special documentation block is parsed before being written to the HTML and / Latex output directories. This includes:
    1. Any white space and asterisks (*) are removed.
    2. Words are linked to their corresponding documentation. Where the word is preceded by a percent sign (%) the percent sign is removed and the word remains.
    3. Where certain patterns are found in the text, links to members are created. Examples of this can be found on the automatic link generation page on the Doxygen documentation website.
    4. When the documentation is for Latex, HTML tags are interpreted and converted to Latex equivalents. A list of supported HTML tags can be found on the HTML commands page on the Doxygen documentation website.
More information can be found on the Doxygen website.
mallopt is a library call that allows a program to change the behavior of the malloc memory allocator.

Example A.1. Allocator heuristics

An allocator has heuristics to determine long versus short lived objects. For the former, it attempts to allocate with mmap. For the later, it attempts to allocate with sbrk.
In order to override these heuristics, set M_MMAP_THRESHOLD.
In multi-threaded applications, the allocator creates multiple arenas in response to lock contention in existing arenas. This can improve the performance significantly for some multi-threaded applications at the cost of an increase in memory usage. To keep this under control, limit the number of arenas that can be created by using the mallopt interface.
The allocator has limits on the number of arenas it can create. For 32bit targets, it will create 2 * # core arenas; for 64bit targets, it will create 8 * # core arenas. mallopt allows the developer to override those limits.

Example A.2. mallopt

To ensure no more than eight arenas are created, issue the following library call:
  • M_TRIM_THRESHOLD
  • M_MMAP_THRESHOLD
  • M_CHECK_ACTION
  • M_ARENA_TEST
Specific definitions for the above can be found at http://www.makelinux.net/man/3/M/mallopt.
malloc_trim is a library call that requests the allocator return any unused memory back to the operating system. This is normally automatic when an object is freed. However, in some cases when freeing small objects, glibc might not immediately release the memory back to the operating system. It does this so that the free memory can be used to satisfy upcoming memory allocation requests as it is expensive to allocate from and release memory back to the operating system.
malloc_stats is used to dump information about the allocator's internal state to stderr. Using mallinfo is similar to this, but it places the state into a structure instead.
More information on mallopt can be found at http://www.makelinux.net/man/3/M/mallopt and http://www.gnu.org/software/libc/manual/html_node/Malloc-Tunable-Parameters.html.
Revision History
Revision 6-9.3Thu 25 May 2017
Removal of section on distributed compiling.
Revision 6-9.2Mon 3 April 2017
Release of the Developer Guide for Red Hat_Enterprise Linux 6.9
Revision 2-60Wed 4 May 2016
Release of the Developer Guide for Red Hat_Enterprise Linux 6.8
Revision 2-56Tue Jul 6 2015
Release of the Developer Guide for Red Hat_Enterprise Linux 6.7
Revision 2-55Wed Apr 15 2015
Release of the Developer Guide for Red Hat_Enterprise Linux 6.7 Beta
Revision 2-54Tue Dec 16 2014
Update to sort order on the Red Hat Customer Portal.
Revision 2-52Wed Nov 11 2014
Re-release for RHSCL 1.2 and DTS 3.0.
Revision 2-51Fri Oct 10 2014
Release of the Developer Guide for Red Hat_Enterprise Linux 6.6

A

advantages
Python pretty-printers
debugging, Python Pretty-Printers
Akonadi
KDE Development Framework
libraries and runtime support, KDE4 Architecture
architecture, KDE4
KDE Development Framework
libraries and runtime support, KDE4 Architecture
Autotools
compiling and building, Autotools
Include

B

backtrace
tools
GNU debugger, Simple GDB
Boost
libraries and runtime support, Boost
boost-doc
Boost
libraries and runtime support, Additional Information
breakpoint
fundamentals
GNU debugger, Simple GDB
breakpoints (conditional)
GNU debugger, Conditional Breakpoints
build-id
compiling and building, build-id Unique Identification of Binaries
building
compiling and building, Compiling and Building

C

C++ Standard Library, GNU
libraries and runtime support, The GNU C++ Standard Library
cachegrind
tools
Valgrind, Valgrind Tools
callgrind
tools
Valgrind, Valgrind Tools
Collaborating, Collaborating
commands
fundamentals
GNU debugger, Simple GDB
profiling
Valgrind, Valgrind Tools
tools
Performance Counters for Linux (PCL) and perf, Perf Tool Commands
commonly-used commands
Autotools
compiling and building, Autotools
compatibility
libraries and runtime support, Compatibility
compiling a C Hello World program
usage
GCC, Simple C Usage
compiling a C++ Hello World program
usage
GCC, Simple C++ Usage
compiling and building
Autotools, Autotools
commonly-used commands, Autotools
configuration script, Configuration Script
documentation, Autotools Documentation
plug-in for Eclipse, Autotools Plug-in for Eclipse
templates (supported), Autotools Plug-in for Eclipse
build-id, build-id Unique Identification of Binaries
GNU Compiler Collection, GNU Compiler Collection (GCC)
documentation, GCC Documentation
required packages, Running GCC
usage, Running GCC
introduction, Compiling and Building
conditional breakpoints
GNU debugger, Conditional Breakpoints
configuration script
Autotools
compiling and building, Configuration Script
continue
tools
GNU debugger, Simple GDB

D

debugfs file system
profiling
ftrace, ftrace
debugging
debuginfo-packages, Installing Debuginfo Packages
installation, Installing Debuginfo Packages
GNU debugger, GDB
fundamental mechanisms, GDB
GDB, GDB
requirements, GDB
introduction, Debugging
Python pretty-printers, Python Pretty-Printers
advantages, Python Pretty-Printers
debugging output (formatted), Python Pretty-Printers
documentation, Python Pretty-Printers
pretty-printers, Python Pretty-Printers
variable tracking at assignments (VTA), Variable Tracking at Assignments
debugging a Hello World program
usage
GNU debugger, Running GDB
debugging output (formatted)
Python pretty-printers
debugging, Python Pretty-Printers
debuginfo-packages
debugging, Installing Debuginfo Packages
documentation
Autotools
compiling and building, Autotools Documentation
Boost
libraries and runtime support, Additional Information
GNU C++ Standard Library
libraries and runtime support, Additional information
GNU Compiler Collection
compiling and building, GCC Documentation
Java
libraries and runtime support, Java Documentation
KDE Development Framework
libraries and runtime support, kdelibs Documentation
OProfile
profiling, OProfile Documentation
Perl
libraries and runtime support, Perl Documentation
profiling
ftrace, ftrace Documentation
Python
libraries and runtime support, Python Documentation
Python pretty-printers
debugging, Python Pretty-Printers
Qt
libraries and runtime support, Qt Library Documentation
Ruby
libraries and runtime support, Ruby Documentation
SystemTap
profiling, Additional Information
Valgrind
profiling, Additional information
Documentation
Doxygen, Doxygen
Docment sources, Documenting the Sources
Getting Started, Getting Started
Resources, Resources
Running Doxygen, Running Doxygen
Supported output and languages, Doxygen Supported Output and Languages
Documentation Tools, Documentation Tools
Doxygen
Documentation, Doxygen
document sources, Documenting the Sources
Getting Started, Getting Started
Resources, Resources
Running Doxygen, Running Doxygen
Supported output and languages, Doxygen Supported Output and Languages

F

finish
tools
GNU debugger, Simple GDB
forked execution
GNU debugger, Forked Execution
formatted debugging output
Python pretty-printers
debugging, Python Pretty-Printers
framework (ftrace)
profiling
ftrace, ftrace
ftrace
profiling, ftrace
debugfs file system, ftrace
documentation, ftrace Documentation
framework (ftrace), ftrace
usage, Using ftrace
function tracer
profiling
ftrace, ftrace
fundamental commands
fundamentals
GNU debugger, Simple GDB
fundamental mechanisms
GNU debugger
debugging, GDB
fundamentals
GNU debugger, Simple GDB

G

gcc
GNU Compiler Collection
compiling and building, GNU Compiler Collection (GCC)
GCC C
usage
compiling a C Hello World program, Simple C Usage
GCC C++
usage
compiling a C++ Hello World program, Simple C++ Usage
GDB
GNU debugger
debugging, GDB
Git
configuration, Installing and Configuring Git
documentation, Additional Resources
installation, Installing and Configuring Git
overview, Git
usage, Creating a New Repository
GNOME Power Manager
libraries and runtime support, GNOME Power Manager
gnome-power-manager
GNOME Power Manager
libraries and runtime support, GNOME Power Manager
GNU C++ Standard Library
libraries and runtime support, The GNU C++ Standard Library
GNU Compiler Collection
compiling and building, GNU Compiler Collection (GCC)
GNU debugger
conditional breakpoints, Conditional Breakpoints
debugging, GDB
execution (forked), Forked Execution
forked execution, Forked Execution
fundamentals, Simple GDB
breakpoint, Simple GDB
commands, Simple GDB
halting an executable, Simple GDB
inspecting the state of an executable, Simple GDB
starting an executable, Simple GDB
interfaces (CLI and machine), Alternative User Interfaces for GDB
thread and threaded debugging, Debugging Individual Threads
tools, Simple GDB
backtrace, Simple GDB
continue, Simple GDB
finish, Simple GDB
help, Simple GDB
list, Simple GDB
next, Simple GDB
print, Simple GDB
quit, Simple GDB
step, Simple GDB
usage, Running GDB
debugging a Hello World program, Running GDB
variations and environments, Alternative User Interfaces for GDB

H

halting an executable
fundamentals
GNU debugger, Simple GDB
helgrind
tools
Valgrind, Valgrind Tools
help
tools
GNU debugger, Simple GDB

I

inspecting the state of an executable
fundamentals
GNU debugger, Simple GDB
installation
debuginfo-packages
debugging, Installing Debuginfo Packages
interfaces (CLI and machine)
GNU debugger, Alternative User Interfaces for GDB
introduction
compiling and building, Compiling and Building
debugging, Debugging
libraries and runtime support, Libraries and Runtime Support
profiling, Profiling
SystemTap, SystemTap
ISO 14482 Standard C++ library
GNU C++ Standard Library
libraries and runtime support, The GNU C++ Standard Library

K

KDE Development Framework
libraries and runtime support, KDE Development Framework
KDE4 architecture
KDE Development Framework
libraries and runtime support, KDE4 Architecture
kdelibs-devel
KDE Development Framework
libraries and runtime support, KDE Development Framework
kernel information packages
profiling
SystemTap, SystemTap
KHTML
KDE Development Framework
libraries and runtime support, KDE4 Architecture
KIO
KDE Development Framework
libraries and runtime support, KDE4 Architecture
KJS
KDE Development Framework
libraries and runtime support, KDE4 Architecture
KNewStuff2
KDE Development Framework
libraries and runtime support, KDE4 Architecture
KXMLGUI
KDE Development Framework
libraries and runtime support, KDE4 Architecture

L

libraries
runtime support, Libraries and Runtime Support
libraries and runtime support
Boost, Boost
boost-doc, Additional Information
documentation, Additional Information
message passing interface (MPI), Boost
meta-package, Boost
C++ Standard Library, GNU, The GNU C++ Standard Library
compatibility, Compatibility
GNOME Power Manager, GNOME Power Manager
gnome-power-manager, GNOME Power Manager
GNU C++ Standard Library, The GNU C++ Standard Library
documentation, Additional information
ISO 14482 Standard C++ library, The GNU C++ Standard Library
libstdc++-devel, The GNU C++ Standard Library
libstdc++-docs, Additional information
Standard Template Library, The GNU C++ Standard Library
introduction, Libraries and Runtime Support
Java, Java
documentation, Java Documentation
KDE Development Framework, KDE Development Framework
Akonadi, KDE4 Architecture
documentation, kdelibs Documentation
KDE4 architecture, KDE4 Architecture
kdelibs-devel, KDE Development Framework
KHTML, KDE4 Architecture
KIO, KDE4 Architecture
KJS, KDE4 Architecture
KNewStuff2, KDE4 Architecture
KXMLGUI, KDE4 Architecture
Phonon, KDE4 Architecture
Plasma, KDE4 Architecture
Solid, KDE4 Architecture
Sonnet, KDE4 Architecture
Strigi, KDE4 Architecture
Telepathy, KDE4 Architecture
libstdc++, The GNU C++ Standard Library
Perl, Perl
documentation, Perl Documentation
module installation, Installation
updates, Perl Updates
Python, Python
documentation, Python Documentation
updates, Python Updates
Qt, Qt
documentation, Qt Library Documentation
meta object compiler (MOC), Qt
Qt Creator, Qt Creator
qt-doc, Qt Library Documentation
updates, Qt Updates
widget toolkit, Qt
Ruby, Ruby
documentation, Ruby Documentation
ruby-devel, Ruby
Library and Runtime Details
NSS Shared Databases, NSS Shared Databases
Backwards Compatibility, Backwards Compatibility
Documentation, NSS Shared Databases Documentation
libstdc++
libraries and runtime support, The GNU C++ Standard Library
libstdc++-devel
GNU C++ Standard Library
libraries and runtime support, The GNU C++ Standard Library
libstdc++-docs
GNU C++ Standard Library
libraries and runtime support, Additional information
list
tools
GNU debugger, Simple GDB
Performance Counters for Linux (PCL) and perf, Perf Tool Commands

M

machine interface
GNU debugger, Alternative User Interfaces for GDB
mallopt, mallopt
massif
tools
Valgrind, Valgrind Tools
mechanisms
GNU debugger
debugging, GDB
memcheck
tools
Valgrind, Valgrind Tools
message passing interface (MPI)
Boost
libraries and runtime support, Boost
meta object compiler (MOC)
Qt
libraries and runtime support, Qt
meta-package
Boost
libraries and runtime support, Boost
module installation
Perl
libraries and runtime support, Installation

N

next
tools
GNU debugger, Simple GDB
NSS Shared Datagbases
Library and Runtime Details, NSS Shared Databases
Backwards Compatibility, Backwards Compatibility
Documentation, NSS Shared Databases Documentation

O

OProfile
profiling, OProfile
documentation, OProfile Documentation
usage, Using OProfile

P

perf
profiling
Performance Counters for Linux (PCL) and perf, Performance Counters for Linux (PCL) Tools and perf
usage
Performance Counters for Linux (PCL) and perf, Using Perf
Performance Counters for Linux (PCL) and perf
profiling, Performance Counters for Linux (PCL) Tools and perf
subsystem (PCL), Performance Counters for Linux (PCL) Tools and perf
tools, Perf Tool Commands
commands, Perf Tool Commands
list, Perf Tool Commands
record, Perf Tool Commands
report, Perf Tool Commands
stat, Perf Tool Commands
usage, Using Perf
perf, Using Perf
Perl
libraries and runtime support, Perl
Phonon
KDE Development Framework
libraries and runtime support, KDE4 Architecture
Plasma
KDE Development Framework
libraries and runtime support, KDE4 Architecture
plug-in for Eclipse
Autotools
compiling and building, Autotools Plug-in for Eclipse
pretty-printers
Python pretty-printers
debugging, Python Pretty-Printers
print
tools
GNU debugger, Simple GDB
profiling
conflict between perf and oprofile, Using Perf
ftrace, ftrace
introduction, Profiling
OProfile, OProfile
Performance Counters for Linux (PCL) and perf, Performance Counters for Linux (PCL) Tools and perf
SystemTap, SystemTap
Valgrind, Valgrind
Python
libraries and runtime support, Python
Python pretty-printers
debugging, Python Pretty-Printers

Q

Qt
libraries and runtime support, Qt
Qt Creator
Qt
libraries and runtime support, Qt Creator
qt-doc
Qt
libraries and runtime support, Qt Library Documentation
quit
tools
GNU debugger, Simple GDB

R

record
tools
Performance Counters for Linux (PCL) and perf, Perf Tool Commands
report
tools
Performance Counters for Linux (PCL) and perf, Perf Tool Commands
required packages
GNU Compiler Collection
compiling and building, Running GCC
profiling
SystemTap, SystemTap
requirements
GNU debugger
debugging, GDB
Revision control, Collaborating
Ruby
libraries and runtime support, Ruby
ruby-devel
Ruby
libraries and runtime support, Ruby
runtime support
libraries, Libraries and Runtime Support

S

scripts (SystemTap scripts)
profiling
SystemTap, SystemTap
Solid
KDE Development Framework
libraries and runtime support, KDE4 Architecture
Sonnet
KDE Development Framework
libraries and runtime support, KDE4 Architecture
Standard Template Library
GNU C++ Standard Library
libraries and runtime support, The GNU C++ Standard Library
starting an executable
fundamentals
GNU debugger, Simple GDB
stat
tools
Performance Counters for Linux (PCL) and perf, Perf Tool Commands
step
tools
GNU debugger, Simple GDB
Strigi
KDE Development Framework
libraries and runtime support, KDE4 Architecture
subsystem (PCL)
profiling
Performance Counters for Linux (PCL) and perf, Performance Counters for Linux (PCL) Tools and perf
supported templates
Autotools
compiling and building, Autotools Plug-in for Eclipse
SystemTap
profiling, SystemTap
documentation, Additional Information
introduction, SystemTap
kernel information packages, SystemTap
required packages, SystemTap
scripts (SystemTap scripts), SystemTap

T

Telepathy
KDE Development Framework
libraries and runtime support, KDE4 Architecture
templates (supported)
Autotools
compiling and building, Autotools Plug-in for Eclipse
thread and threaded debugging
GNU debugger, Debugging Individual Threads
tools
GNU debugger, Simple GDB
Performance Counters for Linux (PCL) and perf, Perf Tool Commands
profiling
Valgrind, Valgrind Tools
Valgrind, Valgrind Tools

U

updates
Perl
libraries and runtime support, Perl Updates
Python
libraries and runtime support, Python Updates
Qt
libraries and runtime support, Qt Updates
usage
GNU Compiler Collection
compiling and building, Running GCC
GNU debugger, Running GDB
fundamentals, Simple GDB
Performance Counters for Linux (PCL) and perf, Using Perf
profiling
ftrace, Using ftrace
OProfile, Using OProfile
Valgrind
profiling, Using Valgrind

V

Valgrind
profiling, Valgrind
commands, Valgrind Tools
documentation, Additional information
tools, Valgrind Tools
usage, Using Valgrind
tools
cachegrind, Valgrind Tools
callgrind, Valgrind Tools
helgrind, Valgrind Tools
massif, Valgrind Tools
memcheck, Valgrind Tools
variable tracking at assignments (VTA)
debugging, Variable Tracking at Assignments
variations and environments
GNU debugger, Alternative User Interfaces for GDB
Version control, Collaborating

W

widget toolkit
Qt
libraries and runtime support, Qt
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