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Parallel task scheduling (also called parallel job scheduling [1] [2] or parallel processing scheduling [3]) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling .
Loop-level parallelism is a form of parallelism in software programming that is concerned with extracting parallel tasks from loops.The opportunity for loop-level parallelism often arises in computing programs where data is stored in random access data structures.
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.
The idea of work stealing goes back to the implementation of the Multilisp programming language and work on parallel functional programming languages in the 1980s. [2] It is employed in the scheduler for the Cilk programming language, [3] the Java fork/join framework, [4] the .NET Task Parallel Library, [5] and the Rust Tokio runtime. [6] [7]
Implementations of the fork–join model will typically fork tasks, fibers or lightweight threads, not operating-system-level "heavyweight" threads or processes, and use a thread pool to execute these tasks: the fork primitive allows the programmer to specify potential parallelism, which the implementation then maps onto actual parallel execution. [1]
There are many pleasingly parallel problems that have such relatively independent code blocks, in particular systems using pipes and filters. For example, when producing live broadcast television, the following tasks must be performed many times a second: Read a frame of raw pixel data from the image sensor,
oneAPI Threading Building Blocks (oneTBB; formerly Threading Building Blocks or TBB) is a C++ template library developed by Intel for parallel programming on multi-core processors. Using TBB, a computation is broken down into tasks that can run in parallel. The library manages and schedules threads to execute these tasks.
GCD works by allowing specific tasks in a program that can be run in parallel to be queued up for execution and, depending on availability of processing resources, scheduling them to execute on any of the available processor cores [12] [13] (referred to as "routing" by Apple). [14] A task can be expressed either as a function or as a "block."