Efficient dynamic scheduling of heterogeneous applications in hybrid architectures

The emergence of different applications that deal with growing amounts of data at reasonable times, has stimulated the development of new computing architectures consisting of different processing units (PU). Runtime environments have been proposed in order to exploit these resource as much as possible by offering a variety of methods for dynamically scheduling tasks on different PUs. These schedulers determine which PU is better suited for executing each task, based upon a set of task parameters such as the amount of data, computation requirements, etc. Although large number of applications are heterogeneous, composed of tasks with different characteristics, the current techniques focus on these characteristics as isolated features leading to inefficient executions in several situations. In this work we present two new scheduling strategies, combining different existing strategies, that leads to more efficient executions in different scenarios. Our results show that our approach can be up to 20% more efficient than current techniques.