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In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality: how well a range of different problems can be expressed for a variety of different architectures, and its performance: how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language. Consensus around a particular programming model is important because it leads to different parallel computers being built with support for the model, thereby facilitating portability of software. In this sense, programming models are referred to as bridging between hardware and software. Classifications of parallel programming models can be divided broadly into two areas: process interaction and problem decomposition. Process interaction relates to the mechanisms by which parallel processes are able to communicate with each other. The most common forms of interaction are shared memory and message passing, but interaction can also be implicit (invisible to the programmer). Shared memory (interprocess communication) Shared memory is an efficient means of passing data between processes. In a shared-memory model, parallel processes share a global address space that they read and write to asynchronously. Asynchronous concurrent access can lead to race conditions, and mechanisms such as locks, semaphores and monitors can be used to avoid these. Conventional multi-core processors directly support shared memory, which many parallel programming languages and libraries, such as Cilk, OpenMP and Threading Building Blocks, are designed to exploit. Message passing In a message-passing model, parallel processes exchange data through passing messages to one another.

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