Hybrid Heuristics for Mapping Task Problem on Large Scale Heterogeneous Platforms

Task allocation problem is one of most studied topic in the field of parallel computing. With the emergence of the large scale platforms, scheduling applications on these large heterogeneous parallel systems is a challenging task due to the large number of mapping possibilities. Indeed, different methods to compute the weights of nodes and edges when scheduling directed acyclic graphs onto heterogeneous platforms may lead to significant variations in the generated schedule, in other terms the problem is ill posed. In this work, a new task mapping using a hybrid clustering method based on a branch and bound is proposed to regularize the problem. The obtained results show that the proposed formulation allows to provide a robust solution in most cases.