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Concurrent and parallel programming languages involve multiple timelines. Such languages provide synchronization constructs whose behavior is defined by a parallel execution model . A concurrent programming language is defined as one which uses the concept of simultaneously executing processes or threads of execution as a means of structuring a ...
A parallel programming language may be based on one or a combination of programming models. For example, High Performance Fortran is based on shared-memory interactions and data-parallel problem decomposition, and Go provides mechanism for shared-memory and message-passing interaction.
One concept used in programming parallel programs is the future concept, where one part of a program promises to deliver a required datum to another part of a program at some future time. Efforts to standardize parallel programming include an open standard called OpenHMPP for hybrid multi-core parallel programming.
A variety of data parallel programming environments are available today, most widely used of which are: Message Passing Interface: It is a cross-platform message passing programming interface for parallel computers. It defines the semantics of library functions to allow users to write portable message passing programs in C, C++ and Fortran.
The goal of the program is to do some net total task ("A+B"). If we write the code as above and launch it on a 2-processor system, then the runtime environment will execute it as follows. In an SPMD (single program, multiple data) system, both CPUs will execute the code. In a parallel environment, both will have access to the same data.
Go—for system programming, with a concurrent programming model based on CSP; Haskell—concurrent, and parallel functional programming language [14] 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
Due to the inherent difficulties in full automatic parallelization, several easier approaches exist to get a parallel program in higher quality. One of these is to allow programmers to add "hints" to their programs to guide compiler parallelization, such as HPF for distributed memory systems and OpenMP or OpenHMPP for shared memory systems ...
A trivial example involves serving static data. It would take very little effort to have many processing units produce the same set of bits. Indeed, the famous Hello World problem could easily be parallelized with few programming considerations or computational costs. Some examples of embarrassingly parallel problems include: Monte Carlo ...