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A thread in computer science is short for a thread of execution. Threads are a way for a program to fork (or split) itself into two or more simultaneously (or pseudo-simultaneously) running tasks. Threads and processes differ from one operating system to another but, in general, a thread is contained inside a process and different threads of the same process share some resources while different processes do not.

Multiple threads can be executed in parallel on many computer systems. This multithreading generally occurs by time slicing (similar to time-division multiplexing), wherein a single processor switches between different threads, in which case the processing is not literally simultaneous, for the single processor is really doing only one thing at a time. This switching can happen so fast as to give the illusion of simultaneity to an end user. For instance, many PCs today only contain one processor core, but one can run multiple programs at once, such as typing in a document editor while listening to music in an audio playback program; though the user experiences these things as simultaneous, in truth, the processor quickly switches back and forth between these separate processes. On a multiprocessor or multi-core system, now coming into general use, threading can be achieved via multiprocessing, wherein different threads and processes can run literally simultaneously on different processors or cores.

Many modern operating systems directly support both time-sliced and multiprocessor threading with a process scheduler. The operating system kernel allows programmers to manipulate threads via the system call interface. Some implementations are called a kernel thread, whereas a lightweight process is a specific type of kernel thread that shares the same state and information.

Absent that, programs can still implement threading by using timers, signals, or other methods to interrupt their own execution and hence perform a sort of ad hoc time-slicing. These are sometimes called user-space threads.

Contents 1 Threads compared with processes 2 Processes, threads, and fibers 2.1 Thread and fiber issues 2.1.1 Concurrency and data structures 2.1.2 I/O and scheduling 3 Implementations 3.1 Kernel-level implementation examples 3.2 User-level implementation examples 3.3 Hybrid implementation examples 4 See also 5 References 6 External links

Threads are distinguished from traditional multitasking operating system processes in which processes are typically independent, carry considerable state information, have separate address spaces, and interact only through system-provided inter-process communication mechanisms. Multiple threads, on the other hand, typically share the state information of a single process, and share memory and other resources directly. Context switching between threads in the same process is typically faster than context switching between processes. Systems like 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.

Multithreading is a popular programming and execution model that allows multiple threads to exist within the context of a single process, sharing the process' resources but able to execute independently. The threaded programming model provides developers with a useful abstraction of concurrent execution. However, perhaps the most interesting application of the technology is when it is applied to a single process to enable parallel execution on a multiprocessor system.

This advantage of a multithreaded program allows it to operate faster on computer systems that have multiple CPUs, CPUs with multiple cores, or across a cluster of machines. This is because the threads of the program naturally lend themselves to truly concurrent execution. In such a case, the programmer needs to be careful to avoid race conditions, and other non-intuitive behaviors. In order for data to be correctly manipulated, threads will often need to rendezvous in time in order to process the data in the correct order. Threads may also require atomic operations (often implemented using semaphores) in order to prevent common data from being simultaneously modified, or read while in the process of being modified. Careless use of such primitives can lead to deadlocks.

Operating systems schedule threads in one of two ways. Preemptive multithreading is generally considered the superior approach, as it allows the operating system to determine when a context switch should occur. Cooperative multithreading, on the other hand, relies on the threads themselves to relinquish control once they are at a stopping point. This can create problems if a thread is waiting for a resource to become available. The disadvantage to preemptive multithreading is that the system may make a context switch at an inappropriate time, causing priority inversion or other bad effects which may be avoided by cooperative multithreading.

Traditional mainstream computing hardware did not have much support for multithreading as switching between threads was generally already 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 has become known as simultaneous multithreading. This feature was introduced in Intel's Pentium 4 processor, with the name Hyper-threading.

Main articles: Process (computing) and Fiber (computer science)

A process is the "heaviest" 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 pre-emptively multitasked. However, Windows 3.1 and older versions of Mac OS used co-operative or non-preemptive multitasking.

A thread is the "lightest" unit of kernel scheduling. At least one thread exists within each process. If multiple threads can exist within a process, then they share the same memory and file resources. Threads are pre-emptively multitasked if the operating system's process scheduler is pre-emptive. Threads do not own resources except for a stack and a copy of the registers including the program counter.

In some situations, there is a distinction between "kernel threads" and "user threads" -- the former are managed and scheduled by the kernel, whereas the latter are managed and scheduled in userspace. In this article, the term "thread" is used to refer to kernel threads, whereas "fiber" is used to refer to user threads. Fibers are co-operatively scheduled: a running fiber must explicitly "yield" to allow another fiber to run. A fiber can be scheduled to run in any thread in the same process.

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 hazards 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 hazards 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.

Many fiber implementations are entirely in userspace. As a result, context switching between 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 fiber and then loading the registers required by the 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 fibers can be problematic. If a fiber performs a system call that blocks, the other 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 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 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.

Win32 supplies a fiber API. SunOS 4.x implemented "light-weight processes" or LWPs as fibers known as green threads. SunOS 5.x and later, NetBSD 2.x, and DragonFly BSD implement LWPs as threads as well.

The use of kernel threads simplifies user code by moving some of the most complex aspects of threading into the kernel. The program doesn't 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 on uniprocessor systems 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 actually execute concurrently on separate processors.

There are many different and incompatible implementations of threading. These include both kernel-level and user-level implementations.

Note that fibers can be implemented without operating system support, although some operating systems or libraries provide explicit support for them. For example, Microsoft Windows (Windows NT 3.51 SP3 and later) support a fiber API for applications that want to gain performance improvements by managing scheduling themselves, instead of relying on the kernel scheduler (which may not be tuned for the application). Microsoft SQL Server 2000's user mode scheduler, running in fiber mode, is an example of doing this.

• Light Weight Kernel Threads in various BSDs
• M:N threading (in BSDs)
• Native POSIX Thread Library for Linux, an implementation of the POSIX Threads (pthreads) standard
• Apple Multiprocessing Services version 2.0 and later, uses the built-in nanokernel in Mac OS 8.6 and later which was modified to support it.

• GNU Portable Threads
• FSU Pthreads
• Apple Inc.'s Thread Manager
• REALbasic (includes an API for cooperative threading)
• Netscape Portable Runtime (includes a user-space fibers implementation)

• Scheduler activations used by the NetBSD native POSIX threads library implementation (an N:M model as opposed to a 1:1 kernel or userspace implementation model)
• Marcel from the PM2 project.

• Win32 Thread Information Block
• Hardware: Multithreading (computer hardware), Multi-core (computing), Simultaneous multithreading
• Theory: Communicating sequential processes, Computer multitasking, Message passing
• Problems: Thread safety, Priority inversion
• Techniques: Protothreads, Thread pool pattern, Lock-free and wait-free algorithms

• David R. Butenhof: Programming with POSIX Threads, Addison-Wesley, ISBN 0-201-63392-2
• Bradford Nichols, Dick Buttlar, Jacqueline Proulx Farell: Pthreads Programming, O'Reilly & Associates, ISBN 1-56592-115-1
• Charles J. Northrup: Programming with UNIX Threads, John Wiley & Sons, ISBN 0-471-13751-0
• Mark Walmsley: Multi-Threaded Programming in C++, Springer, ISBN 1-85233-146-1
• Paul Hyde: Java Thread Programming, Sams, ISBN 0-672-31585-8
• Bill Lewis: Threads Primer: A Guide to Multithreaded Programming, Prentice Hall, ISBN 0-13-443698-9
• Steve Kleiman, Devang Shah, Bart Smaalders: Programming With Threads, SunSoft Press, ISBN 0-13-172389-8
• Pat Villani: Advanced WIN32 Programming: Files, Threads, and Process Synchronization, Harpercollins Publishers, ISBN 0-87930-563-0
• Jim Beveridge, Robert Wiener: Multithreading Applications in Win32, Addison-Wesley, ISBN 0-201-44234-5
• Thuan Q. Pham, Pankaj K. Garg: Multithreaded Programming with Windows NT, Prentice Hall, ISBN 0-13-120643-5
• Len Dorfman, Marc J. Neuberger: Effective Multithreading in OS/2, McGraw-Hill Osborne Media, ISBN 0-07-017841-0
• Alan Burns, Andy Wellings: Concurrency in ADA, Cambridge University Press, ISBN 0-521-62911-X
• Uresh Vahalia: Unix Internals: the New Frontiers, Prentice Hall, ISBN 0-13-101908-2
• Alan L. Dennis: .Net Multithreading , Manning Publications Company, ISBN 1-930110-54-5
• Tobin Titus, Fabio Claudio Ferracchiati, Srinivasa Sivakumar, Tejaswi Redkar, Sandra Gopikrishna: C# Threading Handbook, Peer Information Inc, ISBN 1-86100-829-5
• Tobin Titus, Fabio Claudio Ferracchiati, Srinivasa Sivakumar, Tejaswi Redkar, Sandra Gopikrishna: Visual Basic .Net Threading Handbook, Wrox Press Inc, ISBN 1-86100-713-2

• Ars Technica article about multithreading, etc
• Answers to frequently asked questions for comp.programming.threads
• The C10K problem
• Article "The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software" by Herb Sutter
• Article "The Problem with Threads" by Edward Lee
• POSIX threads explained by Daniel Robbins

v • d • e Parallel computing topics
General	High-performance computing
Parallelism	Bit-level parallelism · Instruction level parallelism · Data parallelism · Task parallelism
Theory	Speedup · Amdahl's law · Flynn's taxonomy (SISD • SIMD • MISD • MIMD) · Cost efficiency · Gustafson's law · Karp-Flatt metric
Elements	Process · Thread · Fiber · Parallel Random Access Machine
Coordination	Multiprocessing · Multithreading · Multitasking · Memory coherency · Cache coherency · Barrier · Synchronization · Distributed computing · Grid computing
Programming	Programming model · Implicit parallelism · Explicit parallelism
Hardware	Computer cluster · Beowulf · Symmetric multiprocessing · Non-Uniform Memory Access · Cache only memory architecture · Asymmetric multiprocessing · Simultaneous multithreading · Shared memory · Distributed memory · Massively parallel processing · Superscalar processing · Vector processing · Supercomputer · Stream processing · GPGPU
Software	Distributed shared memory  · Application checkpointing · Warewulf
APIs	POSIX Threads · OpenMP · Message Passing Interface (MPI)
Problems	Embarrassingly parallel · Grand Challenge · Software lockout