A framework for statistical modeling of superscalar processor performance

Presents a statistical approach to modeling superscalar processor performance. Standard trace-driven techniques are very accurate, but require extremely long simulation times, especially as traces reach lengths in the billions of instructions. A framework for statistical models is described which facilitates fast, accurate performance evaluation. A machine model is built up from components: buffers, pipelines, etc. Each program trace is scanned once, generating a set of program parallelism parameters which can be used across an entire family of machine models. The machine model and program parallelism parameters are combined to form a Markov chain. The Markov chain is partitioned in order to reduce the size of the state space, and the resulting linked models are solved using an iterative technique. The use of this framework is demonstrated with two simple processor microarchitectures. The IPC estimates are very close to the IPCs generated by trace-driven simulation of the same microarchitectures. Resource utilization and other performance data can also be obtained from the statistical model.