Search results
Results from the WOW.Com Content Network
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.
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 ...
The scheduler will generate a list of all the tasks and the details of the cores on which they will execute along with the time that they will execute for. The code Generator will insert special constructs in the code that will be read during execution by the scheduler.
Granularity affects the performance of parallel computers. Using fine grains or small tasks results in more parallelism and hence increases the speedup. However, synchronization overhead, scheduling strategies etc. can negatively impact the performance of fine-grained tasks. Increasing parallelism alone cannot give the best performance.
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 ...
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.
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]
It is a C++ template library with six data-parallel and one task-parallel skeletons, two container types, and support for execution on multi-GPU systems both with CUDA and OpenCL. Recently, support for hybrid execution, performance-aware dynamic scheduling and load balancing is developed in SkePU by implementing a backend for the StarPU runtime ...