Inferred Models for Dynamic and Sparse Hardware-Software Spaces

Diverse software and heterogeneous hardware pose new challenges in systems and architecture management. Managers benefit from improving introspective capabilities, which provide data across a spectrum of platforms, from software and data center profilers to performance counters and canary circuits. Despite this wealth of data, management has become more difficult as sophisticated decisions are demanded. To address these challenges, we present modeling strategies for integrated hardware-software analysis. These strategies include (i) identifying shared software behavior; (ii) quantifying that behavior in a portable, microarchitecture-independent manner; (iii) inferring generalized trends with statistical regression models; and (iv) automatically constructing/updating these models as new software profiles are obtained. Models produced by these strategies are accurate for general SPEC2006 applications with median errors of 8-10%. Predicted and actual performance are strongly correlated with coefficients of ρ > 0.9. Moreover, when we exploit application semantics and domain-specific software parameters, model accuracy improves and model complexity falls. In a case study for sparse linear algebra, we present models with 5% median error and new capabilities in coordinated hardware-software tuning.