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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.
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• Would you trust a stylist with 50,000 clients to get your look right?
• Why Choose Thread Bridesmaid?
• Thread | Definition of Thread by Merriam-Webster
• Thread in Operating System
• What Is a Thread?

If these do not share data, as in Erlang, they are usually analogously called processes, [7] 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.

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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. A kernel thread is a "lightweight" unit of kernel scheduling. 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 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 , 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.

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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.

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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.

Thread | Definition of Thread by Merriam-Webster

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.

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Threads, as a model of computation, are wildly non-deterministic, and the job of the programmer becomes one of pruning that nondeterminism. 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. 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.

Thread in Operating System

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 correspondence with schedulable entities in the kernel [10] are the simplest possible threading implementation.

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.

M : N maps some M number of application threads onto some N number of kernel entities, [10] or "virtual processors.

What Is a Thread?

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.

Many programming languages support threading in some capacity. 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.

A few interpreted programming languages have implementations e. 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.

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. 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. There is one small twist: Thread has just eight stylists. Every Thursday, every single one of them gets a selection of new outfit ideas. Actually buying clothes from the company is an alarmingly frictionless experience. Those are the companies that actually provide and store the clothes; retailers such as ASOS and Urban Outfitters, as well as smaller boutiques and fashion houses.

Only recently has Thread begun to warehouse any clothes at all, and it now pre-purchases stocks of its most popular few items. After that, he became known for Playfire, a games-focused social network that he again sold, in May , to games retailer Green Man Gaming. This time, he says, he wants to stick around. Oxford Street on a Saturday was my idea of hell, and online there was an overwhelming number of things you could look at. And so I kind of had this problem. And so I see this as building a new default for how the majority of men buy clothes.

The startup won a position in the prestigious Y Combinator accelerator in San Francisco, a sort of three-month boot-camp for startups, which led to one of its first paying customers being Instagram CEO Kevin Systrom. But after graduating, it moved from San Francisco back to London, and after a stint in trendy Shoreditch is now further east in a decidedly less cool part of Whitechapel.

And from a purely human approach in , the scalability has come in droves. Machine learning is firmly in buzzword territory today.