An exact algorithm for energy-efficient acceleration of task trees on CPU/GPU architectures
Mark Silberstein, Naoya Maruyama · 2011
We consider the problem of energy-efficient acceleration of applications comprising multiple interdependent tasks forming a dependency tree, on a hypothetical CPU/GPU system where both a CPU and a GPU can be powered off when idle. Each task in the tree can be invoked on either a GPU or a CPU, but the performance may vary: some run faster on a GPU, while others prefer a CPU, making the choice of the lowest-energy processor input dependent. Furthermore, greedily minimizing the energy consumption for each task is suboptimal because of the additional energy required for the communication between the tasks executed on different processors.