Machine learning for microarchitectural prediction
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/108840
Tipus de documentText en actes de congrés
Data publicació2017-09-10
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
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.
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Machine_learning_for.pdf | 168,9Kb | Visualitza/Obre |