Modeling and Exploiting Spatial Locality Trade-Offs in Wavelet-Based Applications under Varying Resource Requirements
Future dynamic applications will require new mapping strategies to deliver power-efficient performance. Fully static design-time mappings will not be able to optimally address the unpredictably varying application characteristics and system resource requirements. Instead, the platforms will not only need to be programmable in terms of instruction set processors, but also at least partial reconfigurability will be required, while the applications themselves will need to exploit this increased freedom at runtime to adapt to the dynamism. In this context, it is important for applications to optimally exploit the memory hierarchy under varying memory availability. This article presents an analysis of spatial locality trade-offs in wavelet-based applications, to be used in dynamic execution environments: Depending on the encountered runtime conditions, the execution switches to different memory optimized instantiations or localizations, optimally exploiting temporal and spatial locality under these conditions. This is enabled by systematic mapping guidelines, indicating how the miss-rate behavior of a localization is influenced by a specific execution condition, under which conditions a certain localization is optimal and which miss-rate gains may be obtained by switching to that localization.
dynamism performance algorithms design loop transformations wavelet transform