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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.
The scheduler will list all the tasks and their dependencies on each other in terms of execution and start times. The scheduler will produce the optimal schedule in terms of number of processors to be used or the total execution time for the application.
The basic form of the problem of scheduling jobs with multiple (M) operations, over M machines, such that all of the first operations must be done on the first machine, all of the second operations on the second, etc., and a single job cannot be performed in parallel, is known as the flow-shop scheduling problem.
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 ...
by Michel Auguin (University of Nice Sophia-Antipolis) and François Larbey (Thomson/Sintra), [1] [2] [3] as a “fork-and-join” and data-parallel approach where the parallel tasks (“single program”) are split-up and run simultaneously in lockstep on multiple SIMD processors with different inputs, and
Identical-machines scheduling is an optimization problem in computer science and operations research.We are given n jobs J 1, J 2, ..., J n of varying processing times, which need to be scheduled on m identical machines, such that a certain objective function is optimized, for example, the makespan is minimized.
After a task graph is generated, the task scheduler manages the workflow based on the given task graph by assigning tasks to workers in a manner that improves parallelism and respects the data dependencies. Dask provides two families of schedulers: single-machine scheduler and distributed scheduler.
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 ...