HLSpower: Hybrid Statistical Modeling of the Superscalar Power-Performance Design Space

Abstract

As power densities increase and mobile applications become pervasive, power-aware microprocessor design has become a critical issue. We present HLSpower, a unique tool for power-aware design space exploration of superscalar processors. HLSpower is based upon HLS [OCF00], a tool which used a novel blend of statistical modeling and symbolic execution to accelerate performance modeling more than 100-1000X over conventional cycle-based simulators.

In this paper, we extend the HLSmetho dology to model energy efficiency of superscalars. We validate our results against the Wattch [BTM00] cycle-based power simulator. While minor second order power effects continue to require detailed cycle-by-cycle simulation, HLSpower is useful for large-scale exploration of the significant power-performance design space. For example, we can show that the instruction cache hit rate and pipeline depth interact with power efficiency in a non-trivial way as they are varied over significant ranges. In particular, we note that, while the IPC of a superscalar increases monotonically with both optimizations, the energy efficiency does not. We highlight the design capabilities by focusing on these non-monotonic contour graphs to demonstrate how HLSpower can help build intuition in power-aware design.