Prediction-based superpage-friendly TLB designs

This work demonstrates that a set of commercial and scale-out applications exhibit significant use of superpages and thus suffer from the fixed and small superpage TLB structures of some modern core designs. Other processors better cope with superpages at the expense of using power-hungry and slow fully-associative TLBs. We consider alternate designs that allow all pages to freely share a single, power-efficient and fast set-associative TLB. We propose a prediction-guided multi-grain TLB design that uses a superpage prediction mechanism to avoid multiple lookups in the common case. In addition, we evaluate the previously proposed skewed TLB [1] which builds on principles similar to those used in skewed associative caches [2]. We enhance the original skewed TLB design by using page size prediction to increase its effective associativity. Our prediction-based multi-grain TLB design delivers more hits and is more power efficient than existing alternatives. The predictor uses a 32-byte prediction table indexed by base register values.