Thread (computing)

Source: Wikipedia, the free encyclopedia.
This article is about the concurrency concept. For multithreading in hardware, see
subroutine calls, see Threaded code. For other uses, see Thread (disambiguation)
.
A process with two threads of execution, running on one processor
Program vs. Process vs. Thread
Scheduling, Preemption, Context Switching

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] In many cases, a thread is a component of a process.

The multiple threads of a given process may be executed

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.

The implementation of threads and processes differs between operating systems. In Modern Operating Systems, Tanenbaum shows that many distinct models of process organization are possible.[2][page needed]

History

Threads made an early appearance under the name of "tasks" in OS/360 Multiprogramming with a Variable Number of Tasks (MVT) in 1967. Saltzer (1966) credits Victor A. Vyssotsky with the term "thread".[3]

The use of threads in software applications became more common in the early 2000s as CPUs began to utilize multiple cores. Applications wishing to take advantage of multiple cores for performance advantages were required to employ concurrency to utilize the multiple cores.[4]

Related concepts

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.

Processes

Main article: Process (computing)

At the kernel level, a process contains one or more kernel threads, which share the process's resources, such as memory and file handles – a process is a unit of resources, while a thread is a unit of scheduling and execution. 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,[5] 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

context switching, due to issues such as cache flushing (in particular, process switching changes virtual memory addressing, causing invalidation and thus flushing of an untagged translation lookaside buffer
(TLB), notably on x86).

Kernel threads

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). The kernel can assign one or more software threads to each core in a CPU (it being able to assign itself multiple software threads depending on its support for multithreading), and can swap out threads that get blocked. However, kernel threads take much longer than user threads to be swapped.

User threads

Threads are sometimes implemented in

green threads
.

As user thread implementations are typically entirely in

userspace
, context switching between user threads 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) 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 (used, in particular, by many green threads implementations) is providing an I/O API that implements an interface that blocks the calling thread, rather than the entire process, 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 (in particular, using non-blocking I/O, including lambda continuations and/or async/

await primitives[6]
).

Fibers

Main article: Fiber (computer science)

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 sometimes implement their tasks through fibers.[7][8] 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 vs processes

Threads differ from traditional multitasking operating-system processes in several ways:

  • processes are typically independent, while threads exist as subsets of a process
  • processes carry considerably more
    resources
  • processes have separate address spaces, whereas threads share their address space
  • processes interact only through system-provided inter-process communication mechanisms
  • context switching between threads in the same process typically occurs faster than context switching between processes

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 in the cost of an address-space switch, which on some architectures (notably x86) results in a translation lookaside buffer (TLB) flush.

Advantages and disadvantages of threads vs processes include:

  • Lower resource consumption of threads: using threads, an application can operate using fewer resources than it would need when using multiple processes.
  • Simplified sharing and communication of threads: 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.
  • Thread crashes a process: due to threads sharing the same address space, an illegal operation performed by a thread can crash the entire process; therefore, one misbehaving thread can disrupt the processing of all the other threads in the application.

Scheduling

Preemptive vs cooperative scheduling

Operating systems schedule threads either

resource or if it starves
other threads by not yielding control of execution during intensive computation.

Single- vs multi-processor systems

Until the early 2000s, 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. In 2002, Intel added support for simultaneous multithreading to the Pentium 4 processor, under the name hyper-threading; in 2005, they introduced the dual-core Pentium D processor and AMD introduced the dual-core Athlon 64 X2
processor.

Systems with a single processor generally implement multithreading by

hardware threads
, separate software threads can also be executed concurrently by separate hardware threads.

Threading models

1:1 (kernel-level threading)

Threads created by the user in a 1:1 correspondence with schedulable entities in the kernel

Solaris, NetBSD, FreeBSD, macOS, and iOS
.

M:1 (user-level threading)

An M:1 model implies that all application-level threads map to one kernel-level scheduled entity;

multithreaded processors or multi-processor computers: there is never more than one thread being scheduled at the same time.[9] 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)

M:N maps some M number of application threads onto some N number of kernel entities,[9] 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

  • Scheduler activations used by older versions of the NetBSD native POSIX threads library implementation (an M:N model as opposed to a 1:1 kernel or userspace implementation model)
  • Solaris
    operating system
  • Marcel from the PM2 project.
  • The OS for the Tera-Cray MTA-2
  • The
    Haskell
    uses lightweight threads which are scheduled on operating system threads.

History of threading models in Unix systems

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.[10] 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.

Single-threaded vs multithreaded programs

In computer programming, single-threading is the processing of one command at a time.[11] In the formal analysis of the variables' semantics and process state, the term single threading can be used differently to mean "backtracking within a single thread", which is common in the functional programming community.[12]

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 one 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 one process to enable parallel execution on a multiprocessing system.

Multithreading libraries tend to 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 data synchronization functions.

Threads and data synchronization

Main article: Thread safety

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

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 too fine.

Other synchronization APIs include

condition variables, critical sections, semaphores, and monitors
.

Thread pools

Main article:
Thread pool pattern

A popular programming pattern involving threads 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.

Multithreaded programs vs single-threaded programs pros and cons

Multithreaded applications have the following advantages vs single-threaded ones:

Multithreaded applications have the following drawbacks:

  • Synchronization complexity and related bugs: when using shared resources typical for threaded programs, 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. As Edward A. Lee has written: "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."[14]
  • Being untestable. In general, multithreaded programs are non-deterministic, and as a result, are untestable. In other words, a multithreaded program can easily have bugs which never manifest on a test system, manifesting only in production.[15][14] This can be alleviated by restricting inter-thread communications to certain well-defined patterns (such as message-passing).
  • Synchronization costs. As thread context switch on modern CPUs can cost up to 1 million CPU cycles,[16] it makes writing efficient multithreading programs difficult. In particular, special attention has to be paid to avoid inter-thread synchronization from being too frequent.

Programming language support

Many programming languages support threading in some capacity.

See also

References

  1. S2CID 5679366
    .
  2. ISBN 0-13-595752-4
    .
  3. ^ Saltzer, Jerome Howard (July 1966). Traffic Control in a Multiplexed Computer System (PDF) (Doctor of Science thesis). p. 20.
  4. ^ Sutter, Herb (March 2005). "The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software". Dr. Dobb's Journal. 30 (3).
  5. ^ "Erlang: 3.1 Processes".
  6. ^ Ignatchenko, Sergey. Eight Ways to Handle Non-blocking Returns in Message-passing Programs: from C++98 via C++11 to C++20. CPPCON. Archived from the original on 2020-11-25. Retrieved 2020-11-24.{{cite AV media}}: CS1 maint: bot: original URL status unknown (link)
  7. S2CID 251692327
    .
  8. ^ Iwasaki, Shintaro; Amer, Abdelhalim; Taura, Kenjiro; Seo, Sangmin; Balaji, Pavan. BOLT: Optimizing OpenMP Parallel Regions with User-Level Threads (PDF). The 28th International Conference on Parallel Architectures and Compilation Techniques.
  9. ^
    ISBN 9781118063330
    .
  10. ^ "Multithreading in the Solaris Operating Environment" (PDF). 2002. Archived from the original (PDF) on February 26, 2009.
  11. ISBN 978-1-890774-09-7
    .
  12. ISBN 978-0-8176-3937-2
    .
  13. ^ Ignatchenko, Sergey (August 2010). "Single-Threading: Back to the Future?". Overload (97). ACCU: 16–19.
  14. ^ a b Lee, Edward (January 10, 2006). "The Problem with Threads". UC Berkeley.
  15. ^ Ignatchenko, Sergey (August 2015). "Multi-threading at Business-logic Level is Considered Harmful". Overload (128). ACCU: 4–7.
  16. ^ 'No Bugs' Hare (12 September 2016). "Operation Costs in CPU Clock Cycles".

Further reading

Wikiversity has learning resources about Processes and Threads at
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