Concurrent computing

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Concurrent computing is a sườn of computing in which several computations are executed concurrently—during overlapping time periods—instead of sequentially—with one completing before the next starts.

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This is a property of a system—whether a program, computer, or a network—where there is a separate execution point or "thread of control" for each process. A concurrent system is one where a computation can advance without waiting for all other computations đồ sộ complete.^[1]

Concurrent computing is a sườn of modular programming. In its paradigm an overall computation is factored into subcomputations that may be executed concurrently. Pioneers in the field of concurrent computing include Edsger Dijkstra, Per Brinch Hansen, and C.A.R. Hoare.^[2]

Introduction[edit]

The concept of concurrent computing is frequently confused with the related but distinct concept of parallel computing,^[3]^[4] although both can be described as "multiple processes executing during the same period of time". In parallel computing, execution occurs at the same physical instant: for example, on separate processors of a multi-processor machine, with the goal of speeding up computations—parallel computing is impossible on a (one-core) single processor, as only one computation can occur at any instant (during any single clock cycle).^[a] By contrast, concurrent computing consists of process lifetimes overlapping, but execution need not happen at the same instant. The goal here is đồ sộ model processes in the outside world that happen concurrently, such as multiple clients accessing a server at the same time. Structuring software systems as composed of multiple concurrent, communicating parts can be useful for tackling complexity, regardless of whether the parts can be executed in parallel.^[5]^: 1

For example, concurrent processes can be executed on one core by interleaving the execution steps of each process via time-sharing slices: only one process runs at a time, and if it does not complete during its time slice, it is paused, another process begins or resumes, and then later the original process is resumed. In this way, multiple processes are part-way through execution at a single instant, but only one process is being executed at that instant.^{[citation needed]}

Concurrent computations may be executed in parallel,^[3]^[6] for example, by assigning each process đồ sộ a separate processor or processor core, or distributing a computation across a network. In general, however, the languages, tools, and techniques for parallel programming might not be suitable for concurrent programming, and vice versa.^{[citation needed]}

The exact timing of when tasks in a concurrent system are executed depends on the scheduling, and tasks need not always be executed concurrently. For example, given two tasks, T1 and T2:^{[citation needed]}

T1 may be executed and finished before T2 or vice versa (serial and sequential)
T1 and T2 may be executed alternately (serial and concurrent)
T1 and T2 may be executed simultaneously at the same instant of time (parallel and concurrent)

The word "sequential" is used as an antonym for both "concurrent" and "parallel"; when these are explicitly distinguished, concurrent/sequential and parallel/serial are used as opposing pairs.^[7] A schedule in which tasks execute one at a time (serially, no parallelism), without interleaving (sequentially, no concurrency: no task begins until the prior task ends) is called a serial schedule. A mix of tasks that can be scheduled serially is serializable, which simplifies concurrency control.^{[citation needed]}

Coordinating access đồ sộ shared resources[edit]

The main challenge in designing concurrent programs is concurrency control: ensuring the correct sequencing of the interactions or communications between different computational executions, and coordinating access đồ sộ resources that are shared among executions.^[6] Potential problems include race conditions, deadlocks, and resource starvation. For example, consider the following algorithm đồ sộ make withdrawals from a checking tài khoản represented by the shared resource balance:

bool withdraw(int withdrawal)
{
    if (balance >= withdrawal)
    {
        balance -= withdrawal;
        return true;
    } 
    return false;
}

Suppose balance = 500, and two concurrent threads make the calls withdraw(300) and withdraw(350). If line 3 in both operations executes before line 5 both operations will find that balance >= withdrawal evaluates đồ sộ true, and execution will proceed đồ sộ subtracting the withdrawal amount. However, since both processes perform their withdrawals, the total amount withdrawn will kết thúc up being more than vãn the original balance. These sorts of problems with shared resources benefit from the use of concurrency control, or non-blocking algorithms.

Advantages[edit]

The advantages of concurrent computing include:

Increased program throughput—parallel execution of a concurrent program allows the number of tasks completed in a given time đồ sộ increase proportionally đồ sộ the number of processors according đồ sộ Gustafson's law
High responsiveness for input/output—input/output-intensive programs mostly wait for input or output operations đồ sộ complete. Concurrent programming allows the time that would be spent waiting đồ sộ be used for another task.^{[citation needed]}
More appropriate program structure—some problems and problem domains are well-suited đồ sộ representation as concurrent tasks or processes.^{[citation needed]}

Models[edit]

Introduced in 1962, Petri nets were an early attempt đồ sộ codify the rules of concurrent execution. Dataflow theory later built upon these, and Dataflow architectures were created đồ sộ physically implement the ideas of dataflow theory. Beginning in the late 1970s, process calculi such as Calculus of Communicating Systems (CCS) and Communicating Sequential Processes (CSP) were developed đồ sộ permit algebraic reasoning about systems composed of interacting components. The π-calculus added the capability for reasoning about dynamic topologies.

Input/output automata were introduced in 1987.

Logics such as Lamport's TLA+, and mathematical models such as traces and Actor sự kiện diagrams, have also been developed đồ sộ describe the behavior of concurrent systems.

Software transactional memory borrows from database theory the concept of atomic transactions and applies them đồ sộ memory accesses.

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Consistency models[edit]

Concurrent programming languages and multiprocessor programs must have a consistency model (also known as a memory model). The consistency model defines rules for how operations on computer memory occur and how results are produced.

One of the first consistency models was Leslie Lamport's sequential consistency model. Sequential consistency is the property of a program that its execution produces the same results as a sequential program. Specifically, a program is sequentially consistent if "the results of any execution is the same as if the operations of all the processors were executed in some sequential order, and the operations of each individual processor appear in this sequence in the order specified by its program".^[8]

Implementation[edit]

This section needs expansion. You can help by adding đồ sộ it. (February 2014)

A number of different methods can be used đồ sộ implement concurrent programs, such as implementing each computational execution as an operating system process, or implementing the computational processes as a mix of threads within a single operating system process.

Interaction and communication[edit]

In some concurrent computing systems, communication between the concurrent components is hidden from the programmer (e.g., by using futures), while in others it must be handled explicitly. Explicit communication can be divided into two classes:

Shared memory communication: Concurrent components communicate by altering the contents of shared memory locations (exemplified by Java and C#). This style of concurrent programming usually needs the use of some sườn of locking (e.g., mutexes, semaphores, or monitors) đồ sộ coordinate between threads. A program that properly implements any of these is said đồ sộ be thread-safe.

Message passing communication: Concurrent components communicate by exchanging messages (exemplified by MPI, Go, Scala, Erlang and occam). The exchange of messages may be carried out asynchronously, or may use a synchronous "rendezvous" style in which the sender blocks until the message is received. Asynchronous message passing may be reliable or unreliable (sometimes referred đồ sộ as "send and pray"). Message-passing concurrency tends đồ sộ be far easier đồ sộ reason about than vãn shared-memory concurrency, and is typically considered a more robust sườn of concurrent programming.^{[citation needed]} A wide variety of mathematical theories đồ sộ understand and analyze message-passing systems are available, including the actor model, and various process calculi. Message passing can be efficiently implemented via symmetric multiprocessing, with or without shared memory cache coherence.

Shared memory and message passing concurrency have different performance characteristics. Typically (although not always), the per-process memory overhead and task switching overhead is lower in a message passing system, but the overhead of message passing is greater than vãn for a procedure đường dây nóng. These differences are often overwhelmed by other performance factors.

History[edit]

Concurrent computing developed out of earlier work on railroads and telegraphy, from the 19th and early 20th century, and some terms date đồ sộ this period, such as semaphores. These arose đồ sộ address the question of how đồ sộ handle multiple trains on the same railroad system (avoiding collisions and maximizing efficiency) and how đồ sộ handle multiple transmissions over a given mix of wires (improving efficiency), such as via time-division multiplexing (1870s).

The academic study of concurrent algorithms started in the 1960s, with Dijkstra (1965) credited with being the first paper in this field, identifying and solving mutual exclusion.^[9]

Prevalence[edit]

Concurrency is pervasive in computing, occurring from low-level hardware on a single chip đồ sộ worldwide networks. Examples follow.

At the programming language level:

Channel
Coroutine
Futures and promises

At the operating system level:

Computer multitasking, including both cooperative multitasking and preemptive multitasking
- Time-sharing, which replaced sequential batch processing of jobs with concurrent use of a system
Process
Thread

At the network level, networked systems are generally concurrent by their nature, as they consist of separate devices.

Languages supporting concurrent programming[edit]

Concurrent programming languages are programming languages that use language constructs for concurrency. These constructs may involve multi-threading, tư vấn for distributed computing, message passing, shared resources (including shared memory) or futures and promises. Such languages are sometimes described as concurrency-oriented languages or concurrency-oriented programming languages (COPL).^[10]

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Today, the most commonly used programming languages that have specific constructs for concurrency are Java and C#. Both of these languages fundamentally use a shared-memory concurrency model, with locking provided by monitors (although message-passing models can and have been implemented on top of the underlying shared-memory model). Of the languages that use a message-passing concurrency model, Erlang is probably the most widely used in industry at present.^{[citation needed]}

Many concurrent programming languages have been developed more as research languages (e.g. Pict) rather than vãn as languages for production use. However, languages such as Erlang, Limbo, and occam have seen industrial use at various times in the last đôi mươi years. A non-exhaustive list of languages which use or provide concurrent programming facilities:

Ada—general purpose, with native tư vấn for message passing and monitor based concurrency
Alef—concurrent, with threads and message passing, for system programming in early versions of Plan 9 from Bell Labs
Alice—extension đồ sộ Standard ML, adds tư vấn for concurrency via futures
Ateji PX—extension đồ sộ Java with parallel primitives inspired from π-calculus
Axum—domain specific, concurrent, based on actor model and .NET Common Language Runtime using a C-like syntax
BMDFM—Binary Modular DataFlow Machine
C++—std::thread
Cω (C omega)—for research, extends C#, uses asynchronous communication
C#—supports concurrent computing using lock, yield, also since version 5.0 async and await keywords introduced
Clojure—modern, functional dialect of Lisp on the Java platform
Concurrent Clean—functional programming, similar đồ sộ Haskell
Concurrent Collections (CnC)—Achieves implicit parallelism independent of memory model by explicitly defining flow of data and control
Concurrent Haskell—lazy, pure functional language operating concurrent processes on shared memory
Concurrent ML—concurrent extension of Standard ML
Concurrent Pascal—by Per Brinch Hansen
Curry
D—multi-paradigm system programming language with explicit tư vấn for concurrent programming (actor model)
E—uses promises đồ sộ preclude deadlocks
ECMAScript—uses promises for asynchronous operations
Eiffel—through its SCOOP mechanism based on the concepts of Design by Contract
Elixir—dynamic and functional meta-programming aware language running on the Erlang VM.
Erlang—uses asynchronous message passing with nothing shared
FAUST—real-time functional, for signal processing, compiler provides automatic parallelization via OpenMP or a specific work-stealing scheduler
Fortran—coarrays and do concurrent are part of Fortran 2008 standard
Go—for system programming, with a concurrent programming model based on CSP
Haskell—concurrent, and parallel functional programming language^[11]
Hume—functional, concurrent, for bounded space and time environments where automata processes are described by synchronous channels patterns and message passing
Io—actor-based concurrency
Janus—features distinct askers and tellers đồ sộ logical variables, bag channels; is purely declarative
Java—thread class or Runnable interface
Julia—"concurrent programming primitives: Tasks, async-wait, Channels."^[12]
JavaScript—via trang web workers, in a browser environment, promises, and callbacks.
JoCaml—concurrent and distributed channel based, extension of OCaml, implements the join-calculus of processes
Join Java—concurrent, based on Java language
Joule—dataflow-based, communicates by message passing
Joyce—concurrent, teaching, built on Concurrent Pascal with features from CSP by Per Brinch Hansen
LabVIEW—graphical, dataflow, functions are nodes in a graph, data is wires between the nodes; includes object-oriented language
Limbo—relative of Alef, for system programming in Inferno (operating system)
Locomotive BASIC—Amstrad variant of BASIC contains EVERY and AFTER commands for concurrent subroutines
MultiLisp—Scheme variant extended đồ sộ tư vấn parallelism
Modula-2—for system programming, by N. Wirth as a successor đồ sộ Pascal with native tư vấn for coroutines
Modula-3—modern thành viên of Algol family with extensive tư vấn for threads, mutexes, condition variables
Newsqueak—for research, with channels as first-class values; predecessor of Alef
occam—influenced heavily by communicating sequential processes (CSP)
- occam-π—a modern variant of occam, which incorporates ideas from Milner's π-calculus
Orc—heavily concurrent, nondeterministic, based on Kleene algebra
Oz-Mozart—multiparadigm, supports shared-state and message-passing concurrency, and futures
ParaSail—object-oriented, parallel, không tính phí of pointers, race conditions
Pict—essentially an executable implementation of Milner's π-calculus
Raku includes classes for threads, promises and channels by default^[13]
Python — uses thread-based parallelism and process-based parallelism ^[14]
Reia—uses asynchronous message passing between shared-nothing objects
Red/System—for system programming, based on Rebol
Rust—for system programming, using message-passing with move semantics, shared immutable memory, and shared mutable memory.^[15]
Scala—general purpose, designed đồ sộ express common programming patterns in a concise, elegant, and type-safe way
SequenceL—general purpose functional, main design objectives are ease of programming, code clarity-readability, and automatic parallelization for performance on multicore hardware, and provably không tính phí of race conditions
SR—for research
SuperPascal—concurrent, for teaching, built on Concurrent Pascal and Joyce by Per Brinch Hansen
Swift—built-in tư vấn for writing asynchronous and parallel code in a structured way^[16]
Unicon—for research
TNSDL—for developing telecommunication exchanges, uses asynchronous message passing
VHSIC Hardware Description Language (VHDL)—IEEE STD-1076
XC—concurrency-extended subset of C language developed by XMOS, based on communicating sequential processes, built-in constructs for programmable I/O

Many other languages provide tư vấn for concurrency in the sườn of libraries, at levels roughly comparable with the above list.

Notes[edit]

^ This is discounting parallelism internal đồ sộ a processor core, such as pipelining or vectorized instructions. A one-core, one-processor machine may be capable of some parallelism, such as with a coprocessor, but the processor alone is not.

References[edit]

^ Operating System Concepts 9th edition, Abraham Silberschatz. "Chapter 4: Threads"
^ Hansen, Per Brinch, ed. (2002). The Origin of Concurrent Programming. doi:10.1007/978-1-4757-3472-0. ISBN 978-1-4419-2986-0. S2CID 44909506.
^ ^a ^b Pike, Rob (2012-01-11). "Concurrency is not Parallelism". Waza conference, 11 January 2012. Retrieved from http://talks.golang.org/2012/waza.slide (slides) and http://vimeo.com/49718712 (video).
^ "Parallelism vs. Concurrency". Haskell Wiki.
^ Schneider, Fred B. (1997-05-06). On Concurrent Programming. Springer. ISBN 9780387949420.
^ ^a ^b Ben-Ari, Mordechai (2006). Principles of Concurrent and Distributed Programming (2nd ed.). Addison-Wesley. ISBN 978-0-321-31283-9.
^ Patterson & Hennessy 2013, p. 503.
^ Lamport, Leslie (1 September 1979). "How đồ sộ Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs". IEEE Transactions on Computers. C-28 (9): 690–691. doi:10.1109/TC.1979.1675439. S2CID 5679366.
^ "PODC Influential Paper Award: 2002", ACM Symposium on Principles of Distributed Computing, retrieved 2009-08-24
^ Armstrong, Joe (2003). "Making reliable distributed systems in the presence of software errors" (PDF).
^ Marlow, Simon (2013) Parallel and Concurrent Programming in Haskell : Techniques for Multicore and Multithreaded Programming ISBN 9781449335946
^ https://juliacon.talkfunnel.com/2015/21-concurrent-and-parallel-programming-in-julia Concurrent and Parallel programming in Julia
^ "Concurrency". docs.perl6.org. Retrieved 2017-12-24.
^ Documentation » The Python Standard Library » Concurrent Execution
^ Blum, Ben (2012). "Typesafe Shared Mutable State". Retrieved 2012-11-14.
^ "Concurrency". 2022. Retrieved 2022-12-15.

Sources[edit]

Patterson, David A.; Hennessy, John L. (2013). Computer Organization and Design: The Hardware/Software Interface. The Morgan Kaufmann Series in Computer Architecture and Design (5 ed.). Morgan Kaufmann. ISBN 978-0-12407886-4.