Speaker
Description
3. Impact
The effects of this new distributed algorithm will be proved in the MARATRA (MAssive RAy TRAcing in Fusion Plasmas) system. MARATRA aids those community members who are working on the optimization of plasma heating by electron Bernstein waves (EBW). This new algorithm allows the execution of tasks in the MARATRA system using loop parallelization methods. This approach presents important advantages over the traditional task schedulers, for example, a better workload balancing between all Grid resources or a decrease of the scheduling overhead. Furthermore, the estimated execution time of each Grid node during the tasks distribution process allows the dynamically adaptation of the whole application. Hence, the workload of each task will be dynamically distributed depending on the behaviour of each node. The goal of this distribution scheme is to adapt the MARATRA system to the Grid environment.
Provide a set of generic keywords that define your contribution (e.g. Data Management, Workflows, High Energy Physics)
SelfSchedulers, Grid, Dynamic, Distributed, Fusion, Algorithm
1. Short overview
A new scheduling algorithm to distribute tasks on Grid environments will be described. The algorithm is an enhancement of the distributed dynamic self-scheduler algorithm used in loop parallelization. The algorithm will be applied to the efficient distribution of tasks in physics fusion simulation codes.
4. Conclusions / Future plans
The high degree of heterogeneity and high fault rate of existing grid infrastructures require the implementation of new self-scheduling algorithms to calculate the task chunk size of parameter sweep and high throughput computing applications. The presentation will describe a new algorithm inspired in the distributed self-schedulers schemes used for loop distribution on parallel architectures. Its advantages are demonstrated for the execution of a Physics Fusion application.
