Concurrency (computer science)

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The "Dining Philosophers", a classic problem involving concurrency and shared resources An illustration of the dining philosophers problem.png
The "Dining Philosophers", a classic problem involving concurrency and shared resources

In computer science, concurrency is the ability of different parts or units of a program, algorithm, or problem to be executed out-of-order or in partial order, without affecting the final outcome. This allows for parallel execution of the concurrent units, which can significantly improve overall speed of the execution in multi-processor and multi-core systems. In more technical terms, concurrency refers to the decomposability property of a program, algorithm, or problem into order-independent or partially-ordered components or units. [1]

Computer science Study of the theoretical foundations of information and computation

Computer science is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate, store, and communicate digital information. A computer scientist studies the theory of computation and the practice of designing software systems.

Contents

A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi, the parallel random-access machine model, the actor model and the Reo Coordination Language.

Petri net Modelling language for distributed systems

A Petri net, also known as a place/transition (PT) net, is one of several mathematical modeling languages for the description of distributed systems. It is a class of discrete event dynamic system. A Petri net is a directed bipartite graph, in which the nodes represent transitions and places. The directed arcs describe which places are pre- and/or postconditions for which transitions. Some sources state that Petri nets were invented in August 1939 by Carl Adam Petri—at the age of 13—for the purpose of describing chemical processes.

In computer science, a parallel random-access machine (PRAM) is a shared-memory abstract machine. As its name indicates, the PRAM was intended as the parallel-computing analogy to the random-access machine (RAM). In the same way that the RAM is used by sequential-algorithm designers to model algorithmic performance, the PRAM is used by parallel-algorithm designers to model parallel algorithmic performance. Similar to the way in which the RAM model neglects practical issues, such as access time to cache memory versus main memory, the PRAM model neglects such issues as synchronization and communication, but provides any (problem-size-dependent) number of processors. Algorithm cost, for instance, is estimated using two parameters O(time) and O(time × processor_number).

The actor model in computer science is a mathematical model of concurrent computation that treats "actors" as the universal primitives of concurrent computation. In response to a message that it receives, an actor can: make local decisions, create more actors, send more messages, and determine how to respond to the next message received. Actors may modify their own private state, but can only affect each other indirectly through messaging.

History

As Leslie Lamport (2015) notes, "While concurrent program execution had been considered for years, the computer science of concurrency began with Edsger Dijkstra's seminal 1965 paper that introduced the mutual exclusion problem. ... The ensuing decades have seen a huge growth of interest in concurrency—particularly in distributed systems. Looking back at the origins of the field, what stands out is the fundamental role played by Edsger Dijkstra". [2]

Leslie Lamport American computer scientist

Leslie B. Lamport is an American computer scientist. Lamport is best known for his seminal work in distributed systems, and as the initial developer of the document preparation system LaTeX and the author of its first manual. Leslie Lamport was the winner of the 2013 Turing Award for imposing clear, well-defined coherence on the seemingly chaotic behavior of distributed computing systems, in which several autonomous computers communicate with each other by passing messages. He devised important algorithms and developed formal modeling and verification protocols that improve the quality of real distributed systems. These contributions have resulted in improved correctness, performance, and reliability of computer systems.

Mutual exclusion property of concurrency control, which is instituted for the purpose of preventing race conditions

In computer science, mutual exclusion is a property of concurrency control, which is instituted for the purpose of preventing race conditions; it is the requirement that one thread of execution never enters its critical section at the same time that another concurrent thread of execution enters its own critical section.

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. [3]

Indeterminacy in concurrent computation is concerned with the effects of indeterminacy in concurrent computation. Computation is an area in which indeterminacy is becoming increasingly important because of the massive increase in concurrency due to networking and the advent of many-core computer architectures. These computer systems make use of arbiters which give rise to indeterminacy.

Deadlock state in which members are blocking each other

In concurrent computing, a deadlock is a state in which each member of a group is waiting for another member, including itself, to take action, such as sending a message or more commonly releasing a lock. Deadlock is a common problem in multiprocessing systems, parallel computing, and distributed systems, where software and hardware locks are used to arbitrate shared resources and implement process synchronization.

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 maximise throughput. [4]

Latency is a time interval between the stimulation and response, or, from a more general point of view, a time delay between the cause and the effect of some physical change in the system being observed. Latency is physically a consequence of the limited velocity with which any physical interaction can propagate. The magnitude of this velocity is always less than or equal to the speed of light. Therefore, every physical system with any physical separation (distance) between cause and effect will experience some sort of latency, regardless of the nature of stimulation that it has been exposed to.

In general terms, throughput is the rate of production or the rate at which something is processed.

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.

Theoretical computer science subfield of computer science and of mathematics

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation.

Carl Adam Petri German mathematician

Carl Adam Petri was a German mathematician and computer scientist. He was born in Leipzig.

Models

A number of formalisms for modeling and understanding concurrent systems have been developed, including: [5]

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., process calculi can be modeled in the actor model using a two-phase commit protocol. [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 progressionS to construct a denotation (meaning ) for S as follows: [10]

DenoteS ≡ ⊔i∈ωprogressionSi(⊥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

Concurrent programming encompasses programming languages and algorithms used to implement concurrent systems. Concurrent programming is usually considered to be more general than parallel programming because it can involve arbitrary and dynamic patterns of communication and interaction, whereas parallel systems generally have a predefined and well-structured communications pattern. The base goals of concurrent programming include correctness, performance and robustness. Concurrent systems such as Operating systems and Database management systems are generally designed to operate indefinitely, including automatic recovery from failure, and not terminate unexpectedly (see Concurrency control). Some concurrent systems implement a form of transparent concurrency, in which concurrent computational entities may compete for and share a single resource, but the complexities of this competition and sharing are shielded from the programmer.

Because they use shared resources, concurrent systems in general require the inclusion of some 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 because it causes explosion in the state space and can even cause models to have an infinite number of states.

Some concurrent programming models include coprocesses and deterministic concurrency. In these models, threads of control explicitly yield their timeslices, either to the system or to another process.

See also

Related Research Articles

Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another. The components interact with one another in order to achieve a common goal. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, and independent failure of components. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications.

In computer science, denotational semantics is an approach of formalizing the meanings of programming languages by constructing mathematical objects that describe the meanings of expressions from the languages. Other approaches provide formal semantics of programming languages including axiomatic semantics and operational semantics.

Parallel computing programming paradigm in which many calculations or the execution of processes are carried out simultaneously

Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but it's gaining broader interest due to the physical constraints preventing frequency scaling. As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

In computer science, model checking, or property checking, is, for a given finite-state model of a system, exhaustively and automatically checking whether this model meets a given specification. Typically, one has hardware or software systems in mind, whereas the specification contains safety requirements such as the absence of deadlocks and similar critical states that can cause the system to crash, as well as liveness requirements.

In programming language theory, semantics is the field concerned with the rigorous mathematical study of the meaning of programming languages. It does so by evaluating the meaning of syntactically valid strings defined by a specific programming language, showing the computation involved. In such a case that the evaluation would be of syntactically invalid strings, the result would be non-computation. Semantics describes the processes a computer follows when executing a program in that specific language. This can be shown by describing the relationship between the input and output of a program, or an explanation of how the program will be executed on a certain platform, hence creating a model of computation.

In computer science, the process calculi are a diverse family of related approaches for formally modelling concurrent systems. Process calculi provide a tool for the high-level description of interactions, communications, and synchronizations between a collection of independent agents or processes. They also provide algebraic laws that allow process descriptions to be manipulated and analyzed, and permit formal reasoning about equivalences between processes. Leading examples of process calculi include CSP, CCS, ACP, and LOTOS. More recent additions to the family include the π-calculus, the ambient calculus, PEPA, the fusion calculus and the join-calculus.

In computer science, the Actor model and process calculi are two closely related approaches to the modelling of concurrent digital computation. See Actor model and process calculi history.

Concurrent computing is a form of computing in which several computations are executed during overlapping time periods—concurrently—instead of sequentially. This is a property of a system—this may be an individual program, a computer, or a network—and there is a separate execution point or "thread of control" for each computation ("process"). A concurrent system is one where a computation can advance without waiting for all other computations to complete.

In computer science, unbounded nondeterminism or unbounded indeterminacy is a property of concurrency by which the amount of delay in servicing a request can become unbounded as a result of arbitration of contention for shared resources while still guaranteeing that the request will eventually be serviced. Unbounded nondeterminism became an important issue in the development of the denotational semantics of concurrency, and later became part of research into the theoretical concept of hypercomputation.

The Actor model and process calculi share an interesting history and co-evolution.

Programming language theory branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features

Programming language theory (PLT) is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering, linguistics and even cognitive science. It is a well-recognized branch of computer science, and an active research area, with results published in numerous journals dedicated to PLT, as well as in general computer science and engineering publications.

Explicit Multi-Threading (XMT) is a computer science paradigm for building and programming parallel computers designed around the parallel random-access machine (PRAM) parallel computational model. A more direct explanation of XMT starts with the rudimentary abstraction that made serial computing simple: that any single instruction available for execution in a serial program executes immediately. A consequence of this abstraction is a step-by-step (inductive) explication of the instruction available next for execution. The rudimentary parallel abstraction behind XMT, dubbed Immediate Concurrent Execution (ICE) in Vishkin (2011), is that indefinitely many instructions available for concurrent execution execute immediately. A consequence of ICE is a step-by-step (inductive) explication of the instructions available next for concurrent execution. Moving beyond the serial von Neumann computer, the aspiration of XMT is that computer science will again be able to augment mathematical induction with a simple one-line computing abstraction.

References

  1. Lamport, Leslie (July 1978). "Time, Clocks, and the Ordering of Events in a Distributed System" (PDF). Communications of the ACM. 21 (7): 558–565. doi:10.1145/359545.359563 . Retrieved 4 February 2016.
  2. Lamport, Leslie. "Turing Lecture: The Computer Science of Concurrency: The Early Years (Communications of the ACM, Vol. 58 No. 6, June 2015)". ACM . Retrieved 22 Mar 2017.
  3. 1 2 Cleaveland, Rance; Scott Smolka (December 1996). "Strategic Directions in Concurrency Research". ACM Computing Surveys. 28 (4): 607. doi:10.1145/242223.242252.
  4. Campbell, Colin; Johnson, Ralph; Miller, Ade; Toub, Stephen (August 2010). Parallel Programming with Microsoft .NET. Microsoft Press. ISBN   978-0-7356-5159-3.
  5. Filman, Robert; Daniel Friedman (1984). Coordinated Computing - Tools and Techniques for Distributed Software. McGraw-Hill. ISBN   978-0-07-022439-1. Archived from the original on 2007-05-16.
  6. Keller, Jörg; Christoph Keßler; Jesper Träff (2001). Practical PRAM Programming. John Wiley and Sons.
  7. Lee, Edward; Alberto Sangiovanni-Vincentelli (December 1998). "A Framework for Comparing Models of Computation". IEEE Transactions on CAD . 17 (12): 1217–1229. doi:10.1109/43.736561.
  8. Mogens Nielsen; Vladimiro Sassone; Glynn Winskel (1993). "Relationships Between Models of Concurrency". REX School/Symposium.
  9. Frederick Knabe. A Distributed Protocol for Channel-Based Communication with Choice PARLE 1992.
  10. William Clinger (June 1981). "Foundations of Actor Semantics". Mathematics Doctoral Dissertation. MIT. hdl:1721.1/6935.Cite journal requires |journal= (help)
  11. Roscoe, Colin (2001). Modal and Temporal Properties of Processes. Springer. ISBN   978-0-387-98717-0.

Further reading