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.
Introduction.
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 need not happen at the same instant. The goal here is to 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.
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.
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. In general, however, the languages, tools, and techniques for parallel programming might not be suitable for concurrent programming, and vice versa.
The exact timing of when tasks in a concurrent system are executed depend on the scheduling, and tasks need not always be executed concurrently. For example, given two tasks, T1 and T2:
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.
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 codice_1:
bool withdraw(int withdrawal)
 if (balance >= withdrawal)
 balance -= withdrawal;
 return true;
 return false;
Suppose codice_2, and two concurrent "threads" make the calls codice_3 and codice_4. If line 3 in both operations executes before line 5 both operations will find that codice_5 evaluates to codice_6, 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.
The advantages of concurrent computing include:
Models.
Models for understanding and analyzing concurrent computing systems include:
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 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 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 is probably the most widely used in industry at present.
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. Languages in which concurrency plays an important role include:
Many other languages provide support for concurrency in the form of libraries, at levels roughly comparable with the above list.