All normal disclaimers aside, multithreaded C++ application are only supported when libstdc++ and all user code was built with compilers which report (via gcc/g++ -v ) the same thread model and that model is not single. As long as your final application is actually single-threaded, then it should be safe to mix user code built with a thread model of single with a libstdc++ and other C++ libraries built with another thread model useful on the platform. Other mixes may or may not work but are not considered supported. (Thus, if you distribute a shared C++ library in binary form only, it may be best to compile it with a GCC configured with --enable-threads for maximal interchangeability and usefulness with a user population that may have built GCC with either --enable-threads or --disable-threads.)

When you link a multithreaded application, you will probably need to add a library or flag to g++. This is a very non-standardized area of GCC across ports. Some ports support a special flag (the spelling isn't even standardized yet) to add all required macros to a compilation (if any such flags are required then you must provide the flag for all compilations not just linking) and link-library additions and/or replacements at link time. The documentation is weak. On several targets (including GNU/Linux, Solaris and various BSDs) -pthread is honored. Some other ports use other switches. This is not well documented anywhere other than in "gcc -dumpspecs" (look at the 'lib' and 'cpp' entries).

Some uses of std::atomic also require linking to libatomic.

In the terms of the 2011 C++ standard a thread-safe program is one which does not perform any conflicting non-atomic operations on memory locations and so does not contain any data races. The standard places requirements on the library to ensure that no data races are caused by the library itself or by programs which use the library correctly (as described below). The C++11 memory model and library requirements are a more formal version of the SGI STL definition of thread safety, which the library used prior to the 2011 standard.

The library strives to be thread-safe when all of the following conditions are met:

The user code must guard against concurrent function calls which access any particular library object's state when one or more of those accesses modifies the state. An object will be modified by invoking a non-const member function on it or passing it as a non-const argument to a library function. An object will not be modified by invoking a const member function on it or passing it to a function as a pointer- or reference-to-const. Typically, the application programmer may infer what object locks must be held based on the objects referenced in a function call and whether the objects are accessed as const or non-const. Without getting into great detail, here is an example which requires user-level locks:

     library_class_a shared_object_a;

     void thread_main () {
       library_class_b *object_b = new library_class_b;
       shared_object_a.add_b (object_b);   // must hold lock for shared_object_a
       shared_object_a.mutate ();          // must hold lock for shared_object_a
     }

     // Multiple copies of thread_main() are started in independent threads.

Under the assumption that object_a and object_b are never exposed to another thread, here is an example that does not require any user-level locks:

     void thread_main () {
       library_class_a object_a;
       library_class_b *object_b = new library_class_b;
       object_a.add_b (object_b);
       object_a.mutate ();
     } 

All library types are safe to use in a multithreaded program if objects are not shared between threads or as long each thread carefully locks out access by any other thread while it modifies any object visible to another thread. Unless otherwise documented, the only exceptions to these rules are atomic operations on the types in <atomic> and lock/unlock operations on the standard mutex types in <mutex>. These atomic operations allow concurrent accesses to the same object without introducing data races.

The following member functions of standard containers can be considered to be const for the purposes of avoiding data races: begin, end, rbegin, rend, front, back, data, find, lower_bound, upper_bound, equal_range, at and, except in associative or unordered associative containers, operator[]. In other words, although they are non-const so that they can return mutable iterators, those member functions will not modify the container. Accessing an iterator might cause a non-modifying access to the container the iterator refers to (for example incrementing a list iterator must access the pointers between nodes, which are part of the container and so conflict with other accesses to the container).

Programs which follow the rules above will not encounter data races in library code, even when using library types which share state between distinct objects. In the example below the shared_ptr objects share a reference count, but because the code does not perform any non-const operations on the globally-visible object, the library ensures that the reference count updates are atomic and do not introduce data races:

    std::shared_ptr<int> global_sp;

    void thread_main() {
      auto local_sp = global_sp;  // OK, copy constructor's parameter is reference-to-const

      int i = *global_sp;         // OK, operator* is const
      int j = *local_sp;          // OK, does not operate on global_sp

      // *global_sp = 2;          // NOT OK, modifies int visible to other threads
      // *local_sp = 2;           // NOT OK, modifies int visible to other threads

      // global_sp.reset();       // NOT OK, reset is non-const
      local_sp.reset();           // OK, does not operate on global_sp
    }

    int main() {
      global_sp.reset(new int(1));
      std::thread t1(thread_main);
      std::thread t2(thread_main);
      t1.join();
      t2.join();
    }
      

For further details of the C++11 memory model see Hans-J. Boehm's Threads and memory model for C++ pages, particularly the introduction and FAQ.

This gets a bit tricky. Please read carefully, and bear with me.

This section discusses issues surrounding the design of multithreaded applications which use Standard C++ containers. All information in this section is current as of the gcc 3.0 release and all later point releases. Although earlier gcc releases had a different approach to threading configuration and proper compilation, the basic code design rules presented here were similar. For information on all other aspects of multithreading as it relates to libstdc++, including details on the proper compilation of threaded code (and compatibility between threaded and non-threaded code), see Chapter 17.

Two excellent pages to read when working with the Standard C++ containers and threads are SGI's https://web.archive.org/web/20171225062613/http://www.sgi.com/tech/stl/thread_safety.html and SGI's https://web.archive.org/web/20171225062613/http://www.sgi.com/tech/stl/Allocators.html.

However, please ignore all discussions about the user-level configuration of the lock implementation inside the STL container-memory allocator on those pages. For the sake of this discussion, libstdc++ configures the SGI STL implementation, not you. This is quite different from how gcc pre-3.0 worked. In particular, past advice was for people using g++ to explicitly define _PTHREADS or other macros or port-specific compilation options on the command line to get a thread-safe STL. This is no longer required for any port and should no longer be done unless you really know what you are doing and assume all responsibility.

Since the container implementation of libstdc++ uses the SGI code, we use the same definition of thread safety as SGI when discussing design. A key point that beginners may miss is the fourth major paragraph of the first page mentioned above (For most clients...), which points out that locking must nearly always be done outside the container, by client code (that'd be you, not us). There is a notable exceptions to this rule. Allocators called while a container or element is constructed uses an internal lock obtained and released solely within libstdc++ code (in fact, this is the reason STL requires any knowledge of the thread configuration).

For implementing a container which does its own locking, it is trivial to provide a wrapper class which obtains the lock (as SGI suggests), performs the container operation, and then releases the lock. This could be templatized to a certain extent, on the underlying container and/or a locking mechanism. Trying to provide a catch-all general template solution would probably be more trouble than it's worth.

The library implementation may be configured to use the high-speed caching memory allocator, which complicates thread safety issues. For all details about how to globally override this at application run-time see here. Also useful are details on allocator options and capabilities.