Memory Optimizations For Fast Power-Aware Sparse Computations

We consider memory subsystem optimizations for improving the performance of sparse scientific computation while reducing the power consumed by the CPU and memory. We first consider a sparse matrix vector multiplication kernel that is at the core of most sparse scientific codes, to evaluate the impact of prefetchers and power-saving modes of the CPU and caches. We show that performance can be improved at significantly lower power levels, leading to over a factor of five improvement in the operations/Joule metric of energy efficiency. We then indicate that these results extend to more complex codes such as a multigrid solver. We also determine a functional representation of the impacts of such optimizations and we indicate how it can be used toward further tuning. Our results thus indicate the potential for cross-layer tuning for multiobjective optimizations by considering both features of the application and the architecture.