Machine learning for microarchitectural prediction
Tipus de documentText en actes de congrés
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
Cache replacement and branch prediction are two important microarchitectural prediction techniques for improving performance. We propose a data-driven approach to designing microarchitectural predictors. Through simulation, we collect traces giving detailed control-flow and memory behavior. Then use stochastic search techniques, such as genetic algorithms, to find points in a large design space of predictors that yield good accuracy on the traces. We then evaluate the predictors on held-out data. This talk will present two techniques resulting from this methodology. In Multiperspective Branch Prediction, many features and their parameters are tuned using a genetic algorithm to yield a very accurate perceptron-based branch predictor. Multiperspective Reuse Prediction uses the same idea for cache management. Many features of memory accesses to predict the reuse of a given memory access. The features and their parameters are chosen by a stochastic search yielding a very accurate predictor. This predictor is applied to a placement, replacement, and bypass optimization that out-performs the state of the art.
CitacióJiménez, D. A. Machine learning for microarchitectural prediction. A: 3rd Severo Ochoa Research Seminar Lectures at BSC, Barcelona, 2016-2017. "Book of abstracts". Barcelona: Barcelona Supercomputing Center, 2017, p. 49-50.