Automated, retargetable back-annotation for host compiled performance and power modeling

Abstract

With traditional cycle-accurate or instruction-set simulations of processors often being too slow, host-compiled or source-level software execution approaches have recently become popular. Such high-level simulations can achieve order of magnitude speedups, but approaches that can achieve highly accurate characterization of both power and performance metrics are lacking. In this paper, we propose a novel host-compiled simulation approach that provides close to cycle-accurate estimation of energy and timing metrics in a retargetable manner, using flexible, architecture description language (ADL) based reference models. Our automated flow considers typical front- and back-end optimizations by working at the compiler-generated intermediate representation (IR). Path-dependent execution effects are accurately captured through pairwise characterization and backannotation of basic code blocks with all possible predecessors. Results from applying our approach to PowerPC targets running various benchmark suites show that close to native average speeds of 2000 MIPS at more than 98% timing and energy accuracy can be achieved.