Past research has proposed numerous hardware prefetching techniques, most of which rely
on exploiting one specific type of program context information (eg, program counter …

This paper presents Voyager, a novel neural network for data prefetching. Unlike previous
neural models for prefetching, which are limited to learning delta correlations, our model can …

Applications extensively use data objects with a regular and fixed layout, which leads to the
recurrence of access patterns over memory regions. Spatial data prefetching techniques …

Recent years have seen a dramatic increase in the microarchitectural complexity of
processors. This increase in complexity presents a twofold challenge for the field of …

Irregular workloads are typically bottlenecked by the memory system. These workloads often
use sparse data representations, eg, compressed sparse row/column (CSR/CSC), to …

Recently, the machine learning research trend expands to the system performance
optimization field, where it has still been proposed by researchers based on their intuitions …

Data prefetching, ie, the act of predicting an application's future memory accesses and
fetching those that are not in the on-chip caches, is a well-known and widely used approach …

Hardware prefetching is an effective technique for hiding cache miss latencies in modern
processor designs. Prefetcher performance can be characterized by two main metrics that …

Big-data server applications frequently encounter data misses, and hence, lose significant
performance potential. One way to reduce the number of data misses or their effect is data …

Graph processing is an important analysis technique for a wide range of big data
applications. The ability to explicitly represent relationships between entities gives graph …