Vector, emerging (homogenous and heterogeneous) multi-core and a number of accelerator processing devices potentially offer an order of magnitude speedup for scientific application...
Compositional Q-Learning (CQ-L) (Singh 1992) is a modular approach to learning to performcomposite tasks made up of several elemental tasks by reinforcement learning. Skills acqui...
The DAG-based task graph model has been found effective in scheduling for performance prediction and optimization of parallel applications. However the scheduling complexity and s...
In business processes, knowledge-intensive tasks are ones in which the people performing such tasks are involved in a fair degree of uncertainty. These people are required to appl...
Abstract—rSesame is a generic modeling and simulation framework which can explore and evaluate reconfigurable systems at the early design stages. The framework can be used to ex...
Kamana Sigdel, Mark Thompson, Carlo Galuzzi, Andy ...