Section: New Results

Automatically Adaptating Task Grain for Hybrid Architectures

Participants : Sylvain Henry, Alexandre Denis, Denis Barthou.

• Given a parallel task graph, a runtime such as StarPU can place each task on different hardware. However, there is still the need to adapt the number of tasks, the granularity of these tasks, according to the target hardware. Due to architectures with CPUs and GPUs, it is potentially interesting to have tasks of different granularities. We explore transformations that enable to either automatically split tasks into small ones, or given some user knowledge on the tasks, decide how and when to split a large task into small ones.

• This work starts from a high-level representation of the code, using an explicit data-flow graph.