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  2. Parallel task scheduling - Wikipedia

    en.wikipedia.org/wiki/Parallel_task_scheduling

    To schedule a job , an algorithm has to choose a machine count and assign j to a starting time and to machines during the time interval [, +,). A usual assumption for this kind of problem is that the total workload of a job, which is defined as d ⋅ p j , d {\displaystyle d\cdot p_{j,d}} , is non-increasing for an increasing number of machines.

  3. Gang scheduling - Wikipedia

    en.wikipedia.org/wiki/Gang_scheduling

    In computer science, gang scheduling is a scheduling algorithm for parallel systems that schedules related threads or processes to run simultaneously on different processors. Usually these will be threads all belonging to the same process, but they may also be from different processes, where the processes could have a producer-consumer ...

  4. Optimal job scheduling - Wikipedia

    en.wikipedia.org/wiki/Optimal_job_scheduling

    Optimal job scheduling is a class of optimization problems related to scheduling. The inputs to such problems are a list of jobs (also called processes or tasks) and a list of machines (also called processors or workers). The required output is a schedule – an assignment of jobs to machines. The schedule should optimize a certain objective ...

  5. Unrelated-machines scheduling - Wikipedia

    en.wikipedia.org/wiki/Unrelated-machines_scheduling

    Unrelated-machines scheduling is an optimization problem in computer science and operations research.It is a variant of optimal job scheduling.We need to schedule n jobs J 1, J 2, ..., J n on m different machines, such that a certain objective function is optimized (usually, the makespan should be minimized).

  6. Uniform-machines scheduling - Wikipedia

    en.wikipedia.org/wiki/Uniform-machines_scheduling

    Uniform machine scheduling (also called uniformly-related machine scheduling or related machine scheduling) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. We are given n jobs J 1, J 2, ..., J n of varying processing times, which need to be scheduled on m different machines.

  7. Granularity (parallel computing) - Wikipedia

    en.wikipedia.org/wiki/Granularity_(parallel...

    These tasks are assigned individually to many processors. The amount of work associated with a parallel task is low and the work is evenly distributed among the processors. Hence, fine-grained parallelism facilitates load balancing. [3] As each task processes less data, the number of processors required to perform the complete processing is high.

  8. Fork–join model - Wikipedia

    en.wikipedia.org/wiki/Fork–join_model

    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 ]

  9. Makespan - Wikipedia

    en.wikipedia.org/wiki/Makespan

    In operations research, the makespan of a project is the length of time that elapses from the start of work to the end. This type of multi-mode resource constrained project scheduling problem (MRCPSP) seeks to create the shortest logical project schedule, by efficiently using project resources, adding the lowest number of additional resources as possible to achieve the minimum makespan. [1]