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In grammar, parallelism, also known as parallel structure or parallel construction, is a balance within one or more sentences of similar phrases or clauses that have the same grammatical structure. [1] The application of parallelism affects readability and may make texts easier to process. [2]
Parallel process is a phenomenon noted in clinical supervision by therapist and supervisor, whereby the therapist recreates, or parallels, the client's problems by way of relating to the supervisor. The client's transference and the therapist's countertransference thus re-appear in the mirror of the therapist/supervisor relationship.
In rhetoric, parallel syntax (also known as parallel construction, parallel structure, and parallelism) is a rhetorical device that consists of repetition among adjacent sentences or clauses. The repeated sentences or clauses provide emphasis to a central theme or idea the author is trying to convey. [ 1 ]
"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy". [4] The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods."
For simple loops, where each iteration is independent of the others, loop-level parallelism can be embarrassingly parallel, as parallelizing only requires assigning a process to handle each iteration. However, many algorithms are designed to run sequentially, and fail when parallel processes race due to dependence within the code. Sequential ...
Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks —concurrently performed by processes or threads —across different processors.
Concurrent computations may be executed in parallel, [3] [6] for example, by assigning each process to a separate processor or processor core, or distributing a computation across a network. The exact timing of when tasks in a concurrent system are executed depends on the scheduling , and tasks need not always be executed concurrently.
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.