Applied Inference: Case Studies in Microarchitectural Design

We propose and apply a new simulation paradigm for microarchitectural design evaluation and optimization. This paradigm enables more comprehensive design studies by combining spatial sampling and statistical inference. Specifically, this paradigm (i) defines a large, comprehensive design space, (ii) samples points from the space for simulation, and (iii) constructs regression models based on sparse simulations. This approach greatly improves the computational efficiency of microarchitectural simulation and enables new capabilities in design space exploration. We illustrate new capabilities in three case studies for a large design space of approximately 260,000 points: (i) Pareto frontier, (ii) pipeline depth, and (iii) multiprocessor heterogeneity analy-ses. In particular, regression models are exhaustively evaluated to identify Pareto optimal designs that maximize performance for given power budgets. Thesemodels enable pipeline depth studies in which all parameters vary simultaneously with depth, thereby more effectively revealing interac-tions with nondepth parameters. Heterogeneity analysis combines regression-based optimization with clustering heuristics to identify efficient design compromises between similar optimal archi-tectures. These compromises are potential core designs in a heterogeneous multicore architecture. Increasing heterogeneity can improve bips3/w efficiency by as much as 2.4×, a theoretical up