This context switching generally happens very often and rapidly enough that users perceive the threads or tasks as running in parallel. On a multiprocessor or multi-core system, multiple threads can execute in parallel , with every processor or core executing a separate thread simultaneously; on a processor or core with hardware threads , separate software threads can also be executed concurrently by separate hardware threads. Saltzer credits Victor A. Vyssotsky with the term "thread". Some threading implementations are called kernel threads, whereas light-weight processes LWP are a specific type of kernel thread that share the same state and information. Furthermore, programs can have user-space threads when threading with timers, signals, or other methods to interrupt their own execution, performing a sort of ad hoc time-slicing.
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This context switching generally happens very often and rapidly enough that users perceive the threads or tasks as running in parallel. On a multiprocessor or multi-core system, multiple threads can execute in parallel , with every processor or core executing a separate thread simultaneously; on a processor or core with hardware threads , separate software threads can also be executed concurrently by separate hardware threads.
Saltzer credits Victor A. Vyssotsky with the term "thread". Some threading implementations are called kernel threads, whereas light-weight processes LWP are a specific type of kernel thread that share the same state and information.
Furthermore, programs can have user-space threads when threading with timers, signals, or other methods to interrupt their own execution, performing a sort of ad hoc time-slicing.
Threads vs. Single threading[ edit ] In computer programming , single-threading is the processing of one command at a time. Multithreading is a widespread programming and execution model that allows multiple threads to exist within the context of one process.
The threaded programming model provides developers with a useful abstraction of concurrent execution. Multithreading can also be applied to one process to enable parallel execution on a multiprocessing system.
Multithreaded applications have the following advantages: Responsiveness: multithreading can allow an application to remain responsive to input.
In a one-thread program, if the main execution thread blocks on a long-running task, the entire application can appear to freeze. By moving such long-running tasks to a worker thread that runs concurrently with the main execution thread, it is possible for the application to remain responsive to user input while executing tasks in the background. Lower resource consumption: using threads, an application can serve multiple clients concurrently using fewer resources than it would need when using multiple process copies of itself.
For example, the Apache HTTP server uses thread pools : a pool of listener threads for listening to incoming requests, and a pool of server threads for processing those requests.
Better system utilization: as an example, a file system using multiple threads can achieve higher throughput and lower latency since data in a faster medium such as cache memory can be retrieved by one thread while another thread retrieves data from a slower medium such as external storage with neither thread waiting for the other to finish. Simplified sharing and communication: unlike processes, which require a message passing or shared memory mechanism to perform inter-process communication IPC , threads can communicate through data, code and files they already share.
Parallelization: applications looking to use multicore or multi-CPU systems can use multithreading to split data and tasks into parallel subtasks and let the underlying architecture manage how the threads run, either concurrently on one core or in parallel on multiple cores.
GPU computing environments like CUDA and OpenCL use the multithreading model where dozens to hundreds of threads run in parallel across data on a large number of cores. Multithreading has the following drawbacks: Synchronization : since threads share the same address space, the programmer must 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 mutually exclusive operations often implemented using mutexes to prevent common data from being read or overwritten in one thread while being modified by another. Careless use of such primitives can lead to deadlocks , livelocks or races over resources. Thread crashes a process: an illegal operation performed by a thread crashes the entire process; therefore, one misbehaving thread can disrupt the processing of all the other threads in the application. Scheduling[ edit ] Operating systems schedule threads either preemptively or cooperatively.
On multi-user operating systems , preemptive multithreading is the more widely used approach for its finer grained control over execution time via context switching. However, preemptive scheduling may context switch threads at moments unanticipated by programmers therefore causing lock convoy , priority inversion , or other side-effects. In contrast, cooperative multithreading relies on threads to relinquish control of execution thus ensuring that threads run to completion.
This can create problems if a cooperatively multitasked thread blocks by waiting on a resource or if it starves other threads by not yielding control of execution during intensive computation.
Until the early s, most desktop computers had only one single-core CPU, with no support for hardware threads , although threads were still used on such computers because switching between threads was generally still quicker than full-process context switches. Processes, kernel threads, user threads, and fibers[ edit ] Main articles: Process computing and Fiber computer science Scheduling can be done at the kernel level or user level, and multitasking can be done preemptively or cooperatively.
This yields a variety of related concepts. Kernel scheduling is typically uniformly done preemptively or, less commonly, cooperatively. At the user level a process such as a runtime system can itself schedule multiple threads of execution. If these do not share data, as in Erlang, they are usually analogously called processes,  while if they share data they are usually called user threads, particularly if preemptively scheduled.
Cooperatively scheduled user threads are known as fibers; different processes may schedule user threads differently. User threads may be executed by kernel threads in various ways one-to-one, many-to-one, many-to-many.
The term " light-weight process " variously refers to user threads or to kernel mechanisms for scheduling user threads onto kernel threads. A process is a "heavyweight" unit of kernel scheduling, as creating, destroying, and switching processes is relatively expensive. Processes own resources allocated by the operating system. Resources include memory for both code and data , file handles , sockets, device handles, windows, and a process control block.
Processes are isolated by process isolation , and 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 — see interprocess communication. Creating or destroying a process is relatively expensive, as resources must be acquired or released. Processes are typically preemptively multitasked, and process switching is relatively expensive, beyond basic cost of context switching , due to issues such as cache flushing.
At least one kernel thread exists within each process. If multiple kernel threads exist within a process, then they share the same memory and file resources. 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 , and are thus relatively cheap to create and destroy.
Thread switching is also relatively cheap: it requires a context switch saving and restoring registers and stack pointer , but does not change virtual memory and is thus cache-friendly leaving TLB valid.
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 will 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.
Thread and fiber issues[ edit ] Concurrency and data structures[ edit ] 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 application programming interfaces 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 symmetric multiprocessing SMP systems to contend for the memory bus, especially if the granularity of the locking is fine.
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. 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.
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. 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.
Similar solutions can be provided for other blocking system calls. SunOS 4. NetBSD 2. SunOS 5. Starting with FreeBSD 7, the became the default. 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. Models[ edit ] kernel-level threading [ edit ] Threads created by the user in a correspondence with schedulable entities in the kernel  are the simplest possible threading implementation.
N:1 user-level threading [ edit ] An N:1 model implies that all application-level threads map to one 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 multithreaded processors or multi-processor computers: there is never more than one thread being scheduled at the same time.
M:N hybrid threading [ edit ] M:N maps some M number of application threads onto some N number of kernel entities,  or "virtual processors. 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.
Hybrid implementation examples[ edit ] Scheduler activations used by the NetBSD native POSIX threads library implementation an M:N model as opposed to a kernel or userspace implementation model Light-weight processes used by older versions of the Solaris operating system Marcel from the PM2 project.
Fiber implementation examples[ edit ] Fibers can be implemented without operating system support, although some operating systems or libraries provide explicit support for them.
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