# Concurrency (computer science)

In

^{[1]}

Note that in computer science, parallelism and concurrency are two different things: a parallel program uses multiple CPU cores, each core performing a task independently. On the other hand, concurrency enables a program to deal with multiple tasks even on a single CPU core; the core switches between tasks (i.e. threads) without necessarily completing each one. A program can have both, neither, or a combination of parallelism and concurrency characteristics.^{[2]}

A number of mathematical models have been developed for general concurrent computation including

## 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

^{[3]}

Design of concurrent systems often entails finding reliable techniques for coordinating their execution, data exchange,

^{[4]}

## 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:^{[5]}

- The parallel random-access machine
^{[6]} - The actor model
- Computational bridging models such as the bulk synchronous parallel (BSP) model
- Petri nets
- Process calculi
- Calculus of communicating systems (CCS)
- Communicating sequential processes (CSP) model
- π-calculus

- Tuple spaces, e.g., Linda
- Simple Concurrent Object-Oriented Programming (SCOOP)
- Reo Coordination Language
- Trace monoids

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,^{[7]} while Nielsen, Sassone, and Winskel have demonstrated that category theory can be used to provide a similar unified understanding of different models.^{[8]}

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.,

^{[9]}) 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

**progression**

_{S}to construct a denotation (meaning ) for S as follows:

^{[10]}

**Denote**_{S}≡ ⊔_{i∈ω}**progression**_{S}^{i}(⊥_{S})

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

### Logics

Various types of temporal logic^{[11]} 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.^{[3]}

## Practice

Because they use shared resources, concurrent systems in general^{[}

*example needed*] kind of arbiter somewhere in their implementation (often in the underlying hardware), to control access to those resources. The use of arbiters introduces the possibility of indeterminacy in concurrent computation which has major implications for practice including correctness and performance. For example, arbitration introduces unbounded nondeterminism which raises issues with model checking

Some concurrent programming models include

## See also

- Chu space
- Client–servernetwork nodes
- Clojure
- Clusternodes
- Concurrency control
- Concurrent computing
- Concurrent object-oriented programming
- Concurrency pattern
- Construction and Analysis of Distributed Processes (CADP)
- D (programming language)
- Distributed system
- Elixir (programming language)
- Erlang (programming language)
- Go (programming language)
- Gordon Pask
- International Conference on Concurrency Theory (CONCUR)
- OpenMP
- Parallel computing
- Partitioned global address space
- Processes
- Ptolemy Project
- Rust (programming language)
- Sheaf (mathematics)
- Threads
- X10 (programming language)
- Structured concurrency

## References

- S2CID 215822405. Retrieved 4 February 2016.
**.**- ^ .
**.****.****^**Keller, Jörg; Christoph Keßler; Jesper Träff (2001).*Practical PRAM Programming*. John Wiley and Sons.**.****^**Mogens Nielsen; Vladimiro Sassone; Glynn Winskel (1993). "Relationships Between Models of Concurrency".*REX School/Symposium*.**^**Frederick Knabe. A Distributed Protocol for Channel-Based Communication with Choice PARLE 1992.**)****.**

**
**## Further reading

- Lynch, Nancy A. (1996).
*Distributed Algorithms*. Morgan Kaufmann.ISBN 978-1-55860-348-6. - Tanenbaum, Andrew S.; Van Steen, Maarten (2002).
*Distributed Systems: Principles and Paradigms*. Prentice Hall. . - Kurki-Suonio, Reino (2005).
*A Practical Theory of Reactive Systems*. Springer. . - Garg, Vijay K. (2002).
*Elements of Distributed Computing*. Wiley-IEEE Press. . - Magee, Jeff; Kramer, Jeff (2006).
*Concurrency: State Models and Java Programming*. Wiley. . - Distefano, S., & Bruneo, D. (2015).
*Quantitative assessments of distributed systems: Methodologies and techniques*(1st ed.). Somerset: John Wiley & Sons Inc. - Bhattacharyya, S. S. (2013;2014;).
*Handbook of signal processing systems*(Second;2;2nd 2013; ed.). New York, NY: Springer.10.1007/978-1-4614-6859-2 - Wolter, K. (2012;2014;).
*Resilience assessment and evaluation of computing systems*(1. Aufl.;1; ed.). London;Berlin;: Springer.

## External links