Concurrent computing

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:

• 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

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.

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:

• Ada – general purpose, with native support 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 to Standard ML, adds support for concurrency via futures
• Ateji PX – extension to 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
• Binary Modular Dataflow Machine (BMDFM)
• C++ – thread and coroutine support libraries
• 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 programming dialect of Lisp on the Java platform
• Concurrent Clean – functional programming, similar to 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 support for concurrent programming (actor model)
• E – uses promises to 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, which uses asynchronous message passing with no shared memory
• Erlang – dynamic and functional language running on the Erlang VM, which uses asynchronous message passing with no shared memory
• 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 communicating sequential processes (CSP)
• Haskell – concurrent, and parallel functional programming language
• 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 to logical variables, bag channels; is purely declarative
• Java – thread class or Runnable interface
• Julia – concurrent programming primitives: tasks, async-wait, channels
• JavaScript – via 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 to support parallelism
• Modula-2 – for system programming, by Niklaus Wirth as a successor to Pascal with native support for coroutines
• Modula-3 – modern member of ALGOL family with extensive support for threads, mutexes, condition variables
• Newsqueak – for research, with channels as first-class values; predecessor of Alef
• occam – influenced highly by communicating sequential processes (CSP)
  • occam-π – modern variant of occam, includes ideas from Milner's π-calculus
• Object REXX (ooRexx) – object-based, message exchange for communicating and synchronizing
• Orc – highly concurrent, nondeterministic, based on Kleene algebra
• Oz-Mozart – multiparadigm, supports shared-state and message-passing concurrency, and futures
• ParaSail – object-oriented, parallel, free of pointers, race conditions
• PHP – multithreading support with parallel extension implementing message passing inspired from Go
• Pict – basically an executable implementation of Milner's π-calculus
• Python – uses thread-based parallelism and process-based parallelism
• Raku – includes classes for threads, promises and channels by default
• 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, and shared memory (immutable and mutable)
• Scala – general purpose, designed to 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 free of race conditions
• SR – for research
• SuperPascal – concurrent, for teaching, built on Concurrent Pascal and Joyce by Per Brinch Hansen
• Swift – built-in support for writing asynchronous and parallel code in a structured way
• Unicon – for research
• TeleNokia Specification and Description Language (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 support for concurrency in the form of libraries, at levels roughly comparable with the above list.