Dead Page and Dead Block Predictors: Cleaning TLBs and Caches Together

Abstract

The last level TLB (LLT) and the last level cache (LLC) play a critical role in the overall performance of memory-intensive applications. While management of LLC content has received significant attention, the same may not be true for LLT. In this work, we first explore the well-known concept of dead blocks in caches for TLBs. We find that <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dead</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pages</i> are fairly common in the LLT. Different from dead blocks in LLCs, dead pages in LLTs are most often dead-on-arrival, i.e., they produce zero hits in the TLB. We design a storage-efficient dead page predictor that works with a fraction of storage compared to typical dead block predictors. This is important since an LLT itself requires only a few KBs of storage compared to MBs in LLC. We then leverage the dead page information to guide a simple dead block predictor in LLC. This is driven by the observation that dead blocks are often concentrated within dead pages. In effect, we designed a dead page predictor and a correlating dead block predictor with a total storage overhead of only 11KB to bypass predicted dead pages and dead blocks in LLTs and LLCs, respectively. Together, these predictors help improve the IPC of a set of 14 memory-intensive workloads by 8.3%, on average.