Concurrent computing

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

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 to complete.

Concurrent computing is a form 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.

Concurrency is a broader concept that encompasses several related ideas, including:

Introduction

Concurrency refers to the ability of a system to execute multiple tasks through simultaneous execution or time-sharing (context switching), sharing resources and managing interactions. Concurrency improves responsiveness, throughput, and scalability in modern computing, including:

The concept of concurrent computing is frequently confused with the related but distinct concept of parallel computing, 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). By contrast, concurrent computing consists of process lifetimes overlapping, but execution does not happen at the same instant. The goal here is to model processes that happen concurrently, like 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.: 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, for example, by assigning each process to a separate processor or processor core, or distributing a computation across a network.

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]

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. 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 set of tasks that can be scheduled serially is serializable, which simplifies concurrency control.[citation needed]

Coordinating access to shared resources

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 to resources that are shared among executions. Potential problems include race conditions, deadlocks, and resource starvation. For example, consider the following algorithm to make withdrawals from a checking account 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 to true, and execution will proceed to subtracting the withdrawal amount. However, since both processes perform their withdrawals, the total amount withdrawn will end up being more than the original balance. These sorts of problems with shared resources benefit from the use of concurrency control, or non-blocking algorithms.

Advantages

There are advantages of concurrent computing:

Issues

Because computations in a concurrent system can interact with each other while being executed, the number of possible execution paths in the system can be extremely large, and the resulting outcome can be indeterminate. Concurrent use of shared resources can be a source of indeterminacy, leading to issues such as deadlocks, and resource starvation.

Design of concurrent systems often entails finding reliable techniques for coordinating their execution, data exchange, memory allocation, and execution scheduling to minimize response time and maximize throughput.

Theory

Concurrency theory has been an active field of research in theoretical computer science. One of the first proposals was Carl Adam Petri's seminal work on Petri nets in the early 1960s. In the years since, a wide variety of formalisms have been developed for modeling and reasoning about concurrency.

Models

A number of formalisms for modeling and understanding concurrent systems have been developed, including:

Some of these models of concurrency are primarily intended to support reasoning and specification, while others can be used through the entire development cycle, including design, implementation, proof, testing, and simulation of concurrent systems. Some of these are based on message passing, while others have different mechanisms for concurrency.

The proliferation of different models of concurrency has motivated some researchers to develop ways to unify these different theoretical models. For example, Lee and Sangiovanni-Vincentelli have demonstrated that a so-called "tagged-signal" model can be used to provide a common framework for defining the denotational semantics of a variety of different models of concurrency, while Nielsen, Sassone, and Winskel have demonstrated that category theory can be used to provide a similar unified understanding of different models.

The Concurrency Representation Theorem in the actor model provides a fairly general way to represent concurrent systems that are closed in the sense that they do not receive communications from outside. (Other concurrency systems, e.g., process calculi can be modeled in the actor model using a two-phase commit protocol.) The mathematical denotation denoted by a closed system S is constructed increasingly better approximations from an initial behavior called ⊥S using a behavior approximating function progressionS to construct a denotation (meaning) for S as follows:

DenoteS ≡ ⊔i∈ω progressionSi(⊥S)

In this way, S can be mathematically characterized in terms of all its possible behaviors.

Logics

Various types of temporal logic can be used to help reason about concurrent systems. Some of these logics, such as linear temporal logic and computation tree logic, allow assertions to be made about the sequences of states that a concurrent system can pass through. Others, such as action computational tree logic, Hennessy–Milner logic, and Lamport's temporal logic of actions, build their assertions from sequences of actions (changes in state). The principal application of these logics is in writing specifications for concurrent systems.

Models

Introduced in 1962, Petri nets were an early attempt to codify the rules of concurrent execution. Dataflow theory later built upon these, and Dataflow architectures were created to 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 to 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 event diagrams, have also been developed to describe the behavior of concurrent systems.

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

Consistency models

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".

Implementation

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

Interaction and communication

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 form of locking (e.g., mutexes, semaphores, or monitors) to coordinate between threads. A program that properly implements any of these is said to be thread-safe.
Message passing communication
Concurrent components communicate by message passing (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 to as "send and pray"). Message-passing concurrency tends to be far easier to reason about than shared-memory concurrency, and is typically considered a more robust form of concurrent programming. A wide variety of mathematical theories to 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 for a procedure call. These differences are often overwhelmed by other performance factors.

History

Concurrent computing developed out of earlier work on railroads and telegraphy, from the 19th and early 20th century, and some terms date to this period, such as semaphores. These arose to address the question of how to handle multiple trains on the same railroad system (avoiding collisions and maximizing efficiency) and how to handle multiple transmissions over a given set 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.

Prevalence

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

At the programming language level:

At the operating system level:

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

Languages supporting concurrent programming

Concurrent programming languages are programming languages that use language constructs for concurrency. These constructs may involve multi-threading, support 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).

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 was probably the most widely used in industry as of 2010.[citation needed]

Many concurrent programming languages have been developed more as research languages (e.g., Pict) rather than as languages for production use. However, languages such as Erlang, Limbo, and occam have seen industrial use at various times in the last 20 years. A non-exhaustive list of languages that use or provide concurrent programming facilities:

Many other languages provide support for concurrency in the form of libraries, at levels roughly comparable with the above list.

See also

Notes

  1. ↑ This is discounting parallelism internal to 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

  1. ↑ Operating System Concepts 9th edition, Abraham Silberschatz. "Chapter 4: Threads"
  2. ↑ 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.
  3. 1 2 Operating System Concepts. Wiley. 29 July 2008. ISBN 978-0470128725.
  4. 1 2 Computer Organization and Design: The Hardware/Software Interface. The Morgan Kaufmann Series in Computer Architecture and Design. Morgan Kaufmann. 2012. ISBN 978-0123747501.
  5. 1 2 Distributed Systems: Concepts and Design. Pearson. 2012. ISBN 978-0132143011.
  6. 1 2 Quinn, Michael Jay (1994). Parallel Computing: Theory and Practice. McGraw-Hill. ISBN 978-0070512948.
  7. 1 2 Zomaya, Albert Y. (1996). Parallel and Distributed Computing Handbook. McGraw Hill Professional. ISBN 978-0070730205.
  8. ↑ Parallel and Concurrent Programming in Haskell. O'Reilly Media. 2013. ISBN 9781449335922.
  9. 1 2 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).
  10. ↑ "Parallelism vs. Concurrency". Haskell Wiki.
  11. ↑ Schneider, Fred B. (1997-05-06). On Concurrent Programming. Springer. ISBN 9780387949420.
  12. 1 2 Ben-Ari, Mordechai (2006). Principles of Concurrent and Distributed Programming (2nd ed.). Addison-Wesley. ISBN 978-0-321-31283-9.
  13. ↑ Patterson & Hennessy 2013, p. 503.
  14. ↑ Padua, David (2011). Encyclopedia of Parallel Computing. Springer New York, NY (published September 8, 2011). pp. 819–825. ISBN 978-0-387-09765-7.
  15. ↑ "Asynchronous I/O", Wikipedia, 2024-12-20, retrieved 2024-12-27
  16. 1 2 Cleaveland, Rance; Scott Smolka (December 1996). "Strategic Directions in Concurrency Research". ACM Computing Surveys. 28 (4): 607. doi:10.1145/242223.242252. S2CID 13264261.
  17. ↑ Campbell, Colin; Johnson, Ralph; Miller, Ade; Toub, Stephen (August 2010). Parallel Programming with Microsoft .NET. Microsoft Press. ISBN 978-0-7356-5159-3.
  18. ↑ Filman, Robert; Daniel Friedman (1984). Coordinated Computing - Tools and Techniques for Distributed Software. McGraw-Hill. ISBN 978-0-07-022439-1.
  19. ↑ Keller, Jörg; Christoph Keßler; Jesper Träff (2001). Practical PRAM Programming. John Wiley and Sons.
  20. ↑ Lee, Edward; Alberto Sangiovanni-Vincentelli (December 1998). "A Framework for Comparing Models of Computation" (PDF). IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 17 (12): 1217–1229. doi:10.1109/43.736561.
  21. ↑ Mogens Nielsen; Vladimiro Sassone; Glynn Winskel (1993). "Relationships Between Models of Concurrency". REX School/Symposium.{{cite conference}}: CS1 maint: miscellaneous url (link)
  22. ↑ Frederick Knabe. A Distributed Protocol for Channel-Based Communication with Choice PARLE 1992.
  23. ↑ William Clinger (June 1981). Foundations of Actor Semantics (Thesis). Mathematics Doctoral Dissertation. MIT. hdl:1721.1/6935.
  24. ↑ Roscoe, Colin (2001). Modal and Temporal Properties of Processes. Springer. ISBN 978-0-387-98717-0.
  25. ↑ Lamport, Leslie (1 September 1979). "How to Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs". IEEE Transactions on Computers. C-28 (9): 690–691. Bibcode:1979ITCmp.100..690L. doi:10.1109/TC.1979.1675439. S2CID 5679366.
  26. ↑ Lange, Julien; Ng, Nicholas; Toninho, Bernardo; Yoshida, Nobuko (January 2017). "Fencing off go: Liveness and safety for channel-based programming". Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages. pp. 748–761. doi:10.1145/3009837.3009847. ISBN 978-1-4503-4660-3.
  27. ↑ PODC Influential Paper Award: 2002. ACM Symposium on Principles of Distributed Computing (Report). Retrieved 2009-08-24.
  28. ↑ Armstrong, Joe (2003). "Making reliable distributed systems in the presence of software errors" (PDF). Archived from the original (PDF) on 2016-04-15.
  29. ↑ "Standard library header <thread> (C++11)". en.cppreference.com. Retrieved 2024-10-03.
  30. ↑ "Standard library header <coroutine> (C++20)". en.cppreference.com. Retrieved 2024-10-03.
  31. ↑ Marlow, Simon (2013) Parallel and Concurrent Programming in Haskell: Techniques for Multicore and Multithreaded Programming ISBN 9781449335946
  32. ↑ "Concurrent and Parallel programming in Julia – JuliaCon India 2015 – HasGeek Talkfunnel". juliacon.talkfunnel.com. Archived from the original on 2016-10-18.
  33. ↑ "PHP: parallel – Manual". www.php.net. Retrieved 2024-10-03.
  34. ↑ Documentation: The Python Standard Library: Concurrent Execution
  35. ↑ "Concurrency". docs.perl6.org. Retrieved 2017-12-24.
  36. ↑ Blum, Ben (2012). "Typesafe Shared Mutable State". Retrieved 2012-11-14.
  37. ↑ "Concurrency". 2022. Retrieved 2022-12-15.

Sources

Further reading