A Tutorial in Spatial Sampling and Regression Strategies for Microarchitectural Analysis

We present a new simulation paradigm for microarchitectural design evaluation and optimization. This paradigm counters increasing simulation costs attributed to the exponentially increasing size of design spaces and the need for more thorough, comprehensive studies when evaluating increasingly diverse design options. We present a tutorial for (1) obtaining a more comprehensive understanding of the design space by (2) selectively simulating a modest number of designs from that space and then (3) more effectively leveraging that simulation data using techniques in statistical inference. We survey techniques in spatial sampling to obtain designs for simulation. We also detail the statistical techniques required to derive efficient and robust models, interleaving code segments from scripts performing these analyses. The predictive ability and computational efficiency of these regression models enable new capabilities in microarchitectural design space studies. Collectively, our experiences with this paradigm suggest significant potential for accurate, efficient statistical inference in the microarchitectural domain.