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This article lists concurrent and parallel programming languages, categorizing them by a defining paradigm.Concurrent and parallel programming languages involve multiple timelines.
This type of multithreading is known as block, cooperative or coarse-grained multithreading. The goal of multithreading hardware support is to allow quick switching between a blocked thread and another thread ready to run. Switching from one thread to another means the hardware switches from using one register set to another.
Many implementations of C and C++ support threading, and provide access to the native threading APIs of the operating system. A standardized interface for thread implementation is POSIX Threads (Pthreads), which is a set of C-function library calls. OS vendors are free to implement the interface as desired, but the application developer should ...
pthreads defines a set of C programming language types, functions and constants. It is implemented with a pthread.h header and a thread library.. There are around 100 threads procedures, all prefixed pthread_ and they can be categorized into five groups:
std::this_thread::yield() in the language C++, introduced in C++11. The Yield method is provided in various object-oriented programming languages with multithreading support, such as C# and Java. [2] OOP languages generally provide class abstractions for thread objects. yield in Kotlin
OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, [3] on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows.
C++ (Open Source/Apache 2.0): RaftLib; C, C++, Objective-C, Swift (Apple): Grand Central Dispatch; D: tasks and fibers; Delphi (System.Threading.TParallel) Go: goroutines; Java: Java concurrency.NET: Task Parallel Library; Examples of fine-grained task-parallel languages can be found in the realm of Hardware Description Languages like Verilog ...
The number of threads may be dynamically adjusted during the lifetime of an application based on the number of waiting tasks. For example, a web server can add threads if numerous web page requests come in and can remove threads when those requests taper down. [disputed – discuss] The cost of having a larger thread pool is increased resource ...