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In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system.[1] The implementation of threads and processes differs between operating systems, but in most cases a thread is a component of a process. Multiple threads can exist within one process, executing concurrently and sharing resources such as memory, while different processes do not share these resources. In particular, the threads of a process share its executable code and the values of its dynamically allocated variables and non-thread-local global variables at any given time.

User thread or fiber implementations are typically entirely in userspace. As a result, context switching between user threads or fibers within the same process is extremely efficient because it does not require any interaction with the kernel at all: a context switch can be performed by locally saving the CPU registers used by the currently executing user thread or fiber and then loading the registers required by the user thread or fiber to be executed. Since scheduling occurs in userspace, the scheduling policy can be more easily tailored to the requirements of the program's workload.

However, the use of blocking system calls in user threads (as opposed to kernel threads) or fibers can be problematic. If a user thread or a fiber performs a system call that blocks, the other user threads and fibers in the process are unable to run until the system call returns. A typical example of this problem is when performing I/O: most programs are written to perform I/O synchronously. When an I/O operation is initiated, a system call is made, and does not return until the I/O operation has been completed. In the intervening period, the entire process is "blocked" by the kernel and ca

However, the use of blocking system calls in user threads (as opposed to kernel threads) or fibers can be problematic. If a user thread or a fiber performs a system call that blocks, the other user threads and fibers in the process are unable to run until the system call returns. A typical example of this problem is when performing I/O: most programs are written to perform I/O synchronously. When an I/O operation is initiated, a system call is made, and does not return until the I/O operation has been completed. In the intervening period, the entire process is "blocked" by the kernel and cannot run, which starves other user threads and fibers in the same process from executing.

A common solution to this problem is providing an I/O API that implements a synchronous interface by using non-blocking I/O internally, and scheduling another user thread or fiber while the I/O operation is in progress. Similar solutions can be provided for other blocking system calls. Alternatively, the program can be written to avoid the use of synchronous I/O or other blocking system calls.

SunOS 4.x implemented light-weight processes or LWPs. NetBSD 2.x+, and DragonFly BSD implement LWPs as kernel threads (1:1 model). SunOS 5.2 through SunOS 5.8 as well as NetBSD 2 to NetBSD 4 implemented a two level model, multiplexing one or more user level threads on each kernel thread (M:N model). SunOS 5.9 and later, as well as NetBSD 5 eliminated user threads support, returning to a 1:1 model.[9] FreeBSD 5 implemented M:N model. FreeBSD 6 supported both 1:1 and M:N, users could choose which one should be used with a given program using /etc/libmap.conf. Starting with FreeBSD 7, the 1:1 became the default. FreeBSD 8 no longer supports the M:N model.

The use of kernel threads simplifies user code by moving some of the most complex aspects of threading into the kernel. The program does not need to schedule threads or explicitly yield the processor. User code can be written in a familiar procedural style, including calls to blocking APIs, without starving other threads. However, kernel threading may force a context switch between threads at any time, and thus expose race hazards and concurrency bugs that would otherwise lie latent. On SMP systems, this is further exacerbated because kernel threads may literally execute on separate processors in parallel.

Threads created by the user in a 1:1 correspondence with schedulable entities in the kernel[10] are the simplest possible threading implementation. OS/2 and Win32 used this approach from the start, while on Linux the usual C library implements this approach (via the NPTL or older LinuxThreads). This approach is also used by Solaris, NetBSD, FreeBSD, macOS, and iOS.

N:1 (user-level threading)

An N:1 model implies that all application-level threads map to one kernel-level scheduled entity;[10] the kernel has no knowledge of the application threads. With this approach, context sw

An N:1 model implies that all application-level threads map to one kernel-level scheduled entity;[10] the kernel has no knowledge of the application threads. With this approach, context switching can be done very quickly and, in addition, it can be implemented even on simple kernels which do not support threading. One of the major drawbacks, however, is that it cannot benefit from the hardware acceleration on multithreaded processors or multi-processor computers: there is never more than one thread being scheduled at the same time.[10] For example: If one of the threads needs to execute an I/O request, the whole process is blocked and the threading advantage cannot be used. The GNU Portable Threads uses User-level threading, as does State Threads.

M:N (hybrid threading)

MM:N maps some M number of application threads onto some N number of kernel entities,[10] or "virtual processors." This is a compromise between kernel-level ("1:1") and user-level ("N:1") threading. In general, "M:N" threading systems are more complex to implement than either kernel or user threads, because changes to both kernel and user-space code are required[clarification needed]. In the M:N implementation, the threading library is responsible for scheduling user threads on the available schedulable entities; this makes context switching of threads very fast, as it avoids system calls. However, this increases complexity and the likelihood of priority inversion, as well as suboptimal scheduling without extensive (and expensive) coordination between the userland scheduler and the kernel scheduler.

Hybrid implementation examples

Programming language support

IBM PL/I(F) included support for multithreading (called multitasking) in the late 1960s, and this was continued in the Optimizing Compiler and later versions. The IBM Enterprise PL/I compiler introduced a new model "thread" API. Neither version was part of the PL/I standard.

Many programming languages support threading in some capacity. Many implementations of C and C++ support threading, and provide access to the native threading APIs of the operating system. Some higher level (and usually cross-platform) programming languages, such as Java, PL/I(F) included support for multithreading (called multitasking) in the late 1960s, and this was continued in the Optimizing Compiler and later versions. The IBM Enterprise PL/I compiler introduced a new model "thread" API. Neither version was part of the PL/I standard.

Many programming languages support threading in some capacity. Many implementations of C and C++ support threading, and provide access to the native threading APIs of the operating system. Some higher level (and usually cross-platform) programming languages, such as Java, Python, and .NET Framework languages, expose threading to developers while abstracting the platform specific differences in threading implementations in the runtime. Sev

Many programming languages support threading in some capacity. Many implementations of C and C++ support threading, and provide access to the native threading APIs of the operating system. Some higher level (and usually cross-platform) programming languages, such as Java, Python, and .NET Framework languages, expose threading to developers while abstracting the platform specific differences in threading implementations in the runtime. Several other programming languages and language extensions also try to abstract the concept of concurrency and threading from the developer fully (Cilk, OpenMP, Message Passing Interface (MPI)). Some languages are designed for sequential parallelism instead (especially using GPUs), without requiring concurrency or threads (Ateji PX, CUDA).

A few interpreted programming languages have implementations (e.g., Ruby MRI for Ruby, CPython for Python) which support threading and concurrency but not parallel execution of threads, due to a global interpreter lock (GIL). The GIL is a mutual exclusion lock held by the interpreter that can prevent the interpreter from simultaneously interpreting the applications code on two or more threads at once, which effectively limits the parallelism on multiple core systems. This limits performance mostly for processor-bound threads, which require the processor, and not much for I/O-bound or network-bound ones.

Other implementations of interpreted programming languages, such as Tcl using the Thread extension, avoid the GIL limit by using an Apartment model where data and code must be explicitly "shared" between threads. In Tcl each thread has one or more interpreters.

Event-driven programming hardware description languages such as Verilog have a different threading model that supports extremely large numbers of threads (for modeling hardware).

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 be able to use the same interface across multiple platforms. Most Unix platforms including Linux support Pthreads. Microsoft Windows has its own set of thread functions in the process.h interface for multithreading, like beginthread. Java provides yet another standardized interface over the host operating system using the Java concurrency library java.util.concurrent.

Multithreading libraries provide a function call to create a new thread, which takes a function as a parameter. A concurrent thread is then created which starts running the passed function and ends when the function returns. The thread libraries also offer synchronization functions which make it possible to implement Multithreading libraries provide a function call to create a new thread, which takes a function as a parameter. A concurrent thread is then created which starts running the passed function and ends when the function returns. The thread libraries also offer synchronization functions which make it possible to implement race condition-error free multithreading functions using mutexes, condition variables, critical sections, semaphores, monitors and other synchronization primitives.

Another paradigm of thread usage is that of thread pools where a set number of threads are created at startup that then wait for a task to be assigned. When a new task arrives, it wakes up, completes the task and goes back to waiting. This avoids the relatively expensive thread creation and destruction functions for every task performed and takes thread management out of the application developer's hand and leaves it to a library or the operating system that is better suited to optimize thread management. For example, frameworks like Grand Central Dispatch and Threading Building Blocks.

In programming models such as CUDA designed for data parallel computation, an array of threads run the same code in parallel using only its ID to find its data in memory. In essence, the application must be designed so that each thread performs the same operation on different segments of memory so that they can operate in parallel and use the GPU architecture.