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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. 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 the same process and share resources such as memory, while different processes do not share these resources. In particular, the threads of a process share its instructions (executable code) and its context (the values of its variables at any given moment).
On a single processor, multithreading is generally implemented by time-division multiplexing (as in multitasking), and the central processing unit (CPU) switches between different software threads. This context switching generally happens frequently enough that the user perceives the threads or tasks as running at the same time. On a multiprocessor or multi-core system, threads can be executed in a true concurrent manner, with every processor or core executing a separate thread simultaneously. To implement multiprocessing, the operating system may use hardware threads that exist as a hardware-supported method for better utilization of a particular CPU, and are different from the software threads that are a pure software construct with no CPU-level representation.
Process schedulers of many modern operating systems directly support both time-sliced and multiprocessor threading. The operating system kernel allows programmers to manipulate threads by exposing required functionality through the system call interface. Some threading implementations are called kernel threads, whereas lightweight processes (LWP) are a specific type of kernel threads that shares the same state and information.
Systems such as Windows NT and OS/2 are said to have "cheap" threads and "expensive" processes; in other operating systems there is not so great a difference except the cost of an address space switch which implies a translation lookaside buffer (TLB) flush.
Multithreading is mainly found in multitasking operating systems. Multithreading is a widespread programming and execution model that allows multiple threads to exist within the context of a single process. These threads share the process's resources, but are able to execute independently. The threaded programming model provides developers with a useful abstraction of concurrent execution. Multithreading can also be applied to a single process to enable parallel execution on a multiprocessing system.
Multithreaded applications have the following advantages:
Multithreading has the following drawbacks:
Operating systems schedule threads in one of two ways:
Threads, called tasks, made an early appearance in OS/360 Multiprogramming with a Variable Number of Tasks (MVT) in 1967.
Until the late 1980s, CPUs in desktop computers did not have much support for multithreading, although threads were still used on such computers because switching between threads was generally still quicker than full-process context switches. Processors in embedded systems, which have higher requirements for real-time behaviors, might support multithreading by decreasing the thread-switch time, perhaps by allocating a dedicated register file for each thread instead of saving/restoring a common register file. In the late 1990s, the idea of executing instructions from multiple threads simultaneously, known as simultaneous multithreading, had reached desktops with Intel's Pentium 4 processor, under the name hyper-threading. It has been dropped from Intel Core and Core 2 architectures, but later was re-instated in the Core i7 architectures and some Core i3 and Core i5 CPUs.
Although threads seem to be a small step from sequential computation, in fact, they represent a huge step. They discard the most essential and appealing properties of sequential computation: understandability, predictability, and determinism. Threads, as a model of computation, are wildly non-deterministic, and the job of the programmer becomes one of pruning that nondeterminism.
— The Problem with Threads, Edward A. Lee, UC Berkeley, 2006
A process is a "heavyweight" unit of kernel scheduling. Processes own resources allocated by the operating system. Resources include memory, file handles, sockets, device handles, and windows. Processes do not share address spaces or file resources except through explicit methods such as inheriting file handles or shared memory segments, or mapping the same file in a shared way. Processes are typically preemptively multitasked.
A kernel thread is a "lightweight" unit of kernel scheduling. At least one kernel thread exists within each process. If multiple kernel threads can exist within a process, then they share the same memory and file resources. Kernel threads are preemptively multitasked if the operating system's process scheduler is preemptive. Kernel threads do not own resources except for a stack, a copy of the registers including the program counter, and thread-local storage (if any). The kernel can assign one thread to each logical core in a system (because each processor splits itself up into multiple logical cores if it supports multithreading, or only supports one logical core per physical core if it does not), and can swap out threads that get blocked. However, kernel threads take much longer than user threads to be swapped.
Threads are sometimes implemented in userspace libraries, thus called user threads. The kernel is unaware of them, so they are managed and scheduled in userspace. Some implementations base their user threads on top of several kernel threads, to benefit from multi-processor machines (M:N model). In this article the term "thread" (without kernel or user qualifier) defaults to referring to kernel threads. User threads as implemented by virtual machines are also called green threads. User threads are generally fast to create and manage, but cannot take advantage of multithreading or multiprocessing and get blocked if all of their associated kernel threads get blocked even if there are some user threads that are ready to run.
Fibers are an even lighter unit of scheduling which are cooperatively scheduled: a running fiber must explicitly "yield" to allow another fiber to run, which makes their implementation much easier than kernel or user threads. A fiber can be scheduled to run in any thread in the same process. This permits applications to gain performance improvements by managing scheduling themselves, instead of relying on the kernel scheduler (which may not be tuned for the application). Parallel programming environments such as OpenMP typically implement their tasks through fibers. Closely related to fibers are coroutines, with the distinction being that coroutines are a language-level construct, while fibers are a system-level construct.
Threads in the same process share the same address space. This allows concurrently running code to couple tightly and conveniently exchange data without the overhead or complexity of an IPC. When shared between threads, however, even simple data structures become prone to race conditions if they require more than one CPU instruction to update: two threads may end up attempting to update the data structure at the same time and find it unexpectedly changing underfoot. Bugs caused by race conditions can be very difficult to reproduce and isolate.
To prevent this, threading APIs offer synchronization primitives such as mutexes to lock data structures against concurrent access. On uniprocessor systems, a thread running into a locked mutex must sleep and hence trigger a context switch. On multi-processor systems, the thread may instead poll the mutex in a spinlock. Both of these may sap performance and force processors in SMP systems to contend for the memory bus, especially if the granularity of the locking is fine.
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 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.  FreeBSD 5 implemented M:N model. FreeBSD 6 supported both 1:1 and M:N, user 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 are in 1-1 correspondence with schedulable entities in the kernel. This is the simplest possible threading implementation. Win32 used this approach from the start. On Linux, the usual C library implements this approach (via the NPTL or older LinuxThreads). The same approach is used by Solaris, NetBSD and FreeBSD.
An N:1 model implies that all application-level threads map to a single kernel-level scheduled entity; 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 multi-threaded processors or multi-processor computers: there is never more than one thread being scheduled at the same time. 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 utilized. The GNU Portable Threads uses User-level threading, as does State Threads.
M:N maps some M number of application threads onto some N number of kernel entities, 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. 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.
Fibers can be implemented without operating system support, although some operating systems or libraries provide explicit support for them.
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++ provide support for threading on their own, but also provide access to the native threading APIs provided by the operating system. Some higher level (and usually cross platform) programming languages such as Java, Python, and .NET, expose threading to the developer while abstracting the platform specific differences in threading implementations in the runtime. A number of other programming languages also try to abstract the concept of concurrency and threading from the developer altogether (Cilk, OpenMP, MPI). Some languages are designed for parallelism (Ateji PX, CUDA).
A few interpreted programming languages such as (the MRI implementation of) Ruby and (the CPython implementation of) Python support threading, but have a limitation that is known as a Global Interpreter Lock (GIL). The GIL is a mutual exclusion lock held by the interpreter that can prevent the interpreter from concurrently interpreting the applications code on two or more threads at the same time, which effectively limits the concurrency on multiple core systems (mostly for processor-bound threads, and not much for network-bound ones).
Other interpreted programming languages such as Tcl (using the Thread extension) avoid the GIL limitation by using an Apartment model where data and code must be explicitly "shared" between threads. In Tcl each thread has at one or more interpreters.
A standardized interface for thread implementation is Pthreads, which is a set of C-function library calls. OS vendors are free to implement the interface as they wish 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 multi-threading, like beginthread. Java provides yet another standardized interface over the host operating system using the java.util.concurrent library.
Multi-threading 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 multi-threading 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 certain number of threads at 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 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 utilize the GPU architecture.
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