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PETSc includes a large suite of parallel linear and nonlinear equation solvers that are easily used in application codes written in C, C++, Fortran and now Python. PETSc provides many of the mechanisms needed within parallel application code, such as simple parallel matrix and vector assembly routines that allow the overlap of communication and ...
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 ...
Some parallel programming systems, such as OpenMP and Cilk, have language support for the map pattern in the form of a parallel for loop; [2] languages such as OpenCL and CUDA support elemental functions (as "kernels") at the language level. The map pattern is typically combined with other parallel design patterns.
In computer science, Linda is a coordination model that aids communication in parallel computing environments. Developed by David Gelernter , it is meant to be used alongside a full-fledged computation language like Fortran or C where Linda's role is to " create computational activities and to support communication among them".
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
In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine.
Analysis of parallel algorithms is usually carried out under the assumption that an unbounded number of processors is available. This is unrealistic, but not a problem, since any computation that can run in parallel on N processors can be executed on p < N processors by letting each processor execute multiple units of work.
Fork–join is the main model of parallel execution in the OpenMP framework, although OpenMP implementations may or may not support nesting of parallel sections. [6] It is also supported by the Java concurrency framework, [ 7 ] the Task Parallel Library for .NET, [ 8 ] and Intel's Threading Building Blocks (TBB). [ 1 ]