Diligent TLBs

Modern workloads such as graph analytics, sparse matrix multiplication, and in-memory key-value stores use very large datasets and typically have non-uniform memory access patterns which defy traditional concepts of locality. Moreover, many of these algorithms simultaneously use multiple data structures that have very distinct access patterns to the corresponding pages, leading to heterogeneity in TLB behavior. Our intuition suggests that these two factors make it important to architect a heterogeneity-aware TLB hierarchy.