Parallel Algorithm Models: Data, Task, Work Pool and Master Slave Model
Parallel Algorithm Models: Data, Task, Work Pool and Master Slave Model
Data Parallel Model:
In the data parallel model, the input data is divided into multiple segments and each segment is processed independently by a different processor. The results are then combined to produce the final output. This model is suitable for problems that can be broken down into independent data segments, such as image processing and data mining.
Task Parallel Model:
In the task parallel model, the algorithm is divided into multiple tasks or subroutines, each of which is executed by a different processor. The tasks are coordinated by a master processor, which assigns tasks to processors and collects results. This model is suitable for problems that can be broken down into independent tasks, such as simulation and optimization.
Work Pool Model:
In the work pool model, a pool of tasks is created and each processor retrieves tasks from the pool as they become available. This model is suitable for problems where tasks have varying execution times or where tasks may depend on the results of other tasks. Examples include search algorithms and genetic algorithms.
Master Slave Model:
In the master slave model, one processor acts as the master and coordinates the work of multiple slave processors. The master processor assigns tasks to slaves and collects results. This model is suitable for problems where some tasks may depend on the results of others or where the workload is unevenly distributed among processors. Examples include numerical simulations and parallel processing of large databases.
Overall, understanding the different parallel algorithm models can help in choosing the most appropriate model for a given problem and designing efficient parallel algorithms for parallel computing systems.
