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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 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.
This answer requires a reliable estimation (modeling) of the program workload and the capacity of the parallel system. The first pass of the compiler performs a data dependence analysis of the loop to determine whether each iteration of the loop can be executed independently of the others.
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
The situation T 1 / T p = p is called perfect linear speedup. [9] An algorithm that exhibits linear speedup is said to be scalable. [6] Analytical expressions for the speedup of many important parallel algorithms are presented in this book. [10] Efficiency is the speedup per processor, S p / p. [6] Parallelism is the ratio T 1 / T ∞. It ...
In the kernel partitioning problem, there are some m pre-specified jobs called kernels, and each kernel must be scheduled to a unique machine. An equivalent problem is scheduling when machines are available in different times: each machine i becomes available at some time t i ≥ 0 (the time t i can be thought of as the length of the kernel job).
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]
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]