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g with a variable number of tasks mvt in 1967 saltzer 1966 credits victor a vyssotsky with the term thread 2 popularity of threading has increased around 2003 as the growth of the cpu frequency was replaced with the growth of number of cores in turn requiring concurrency to utilize multiple cores 3 processes kernel threads user threads and fibers edit 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 edit 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 4 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 in particular process switching changes virtual memory addressing causing invalidation and thus flushing of an untagged translation lookaside buffer notably on x86 kernel threads edit 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 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 user threads edit 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 user threads as implemented by virtual machines are also called 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 of 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 5 fibers edit 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 6 7 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 edit 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 state information than threads whereas multiple threads within a process share process state as well as memory and other 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 edit preemptive vs cooperative scheduling edit operating systems schedule threads either preemptively or cooperatively multi user operating systems generally favor preemptive multithreading for its finer grained control over execution time via context switching however preemptive scheduling may context switch threads at moments unanticipated by programmers thus 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 cause 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 single vs multi processor systems edit 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 time slicing the central processing unit cpu switches between different software threads this context switching usually occurs frequently enough that users perceive the threads or tasks as running in parallel for popular server desktop operating systems maximum time slice of a thread when other threads are waiting is often limited to 100 200ms 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 threading models edit 1 1 kernel level threading edit threads created by the user in a 1 1 correspondence with schedulable entities in the kernel 8 are the simplest possible threading implementation os 2 and win32 used this approach from the start while on linux the gnu c library implements this approach via the nptl or older linuxthreads this approach is also used by solaris netbsd freebsd macos and ios n 1 user level threading edit an n 1 model implies that all application level threads map to one kernel level scheduled entity 8 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 8 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 edit m n maps some m number of application threads onto some n number of kernel entities 8 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 edit 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 light weight processes used by older versions of the solaris operating system marcel from the pm2 project the os for the tera cray mta 2 the glasgow haskell compiler ghc for the language haskell uses lightweight threads which are scheduled on operating system threads history of threading models in unix systems edit 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 9 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 edit in computer programming single threading is the processing of one command at a time 10 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 11 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 edit 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 under...