Pruning strategies in adaptive off-line tuning for optimized composition of components on heterogeneous systems
We consolidate our convexity assumption that forms the basis for adaptive pruning of the sampling space. We provide better control of trade-offs between sampling time, runtime overhead and accuracy in adaptive empirical modeling. Reducing training time and improving prediction accuracy can be achieved simultaneously. Our method can converge faster and reaches higher accuracy than random sampling.
heterogeneous systems smart sampling component selection