RnR: A Software-Assisted Record-and-Replay Hardware Prefetcher

Abstract

Applications with irregular memory access patterns do not benefit well from the memory hierarchy as applications that have good locality do. Relatively high miss ratio and long memory access latency can cause the processor to stall and degrade system performance. Prefetching can help to hide the miss penalty by predicting which memory addresses will be accessed in the near future and issuing memory requests ahead of the time. However, software prefetchers add instruction overhead, whereas hardware prefetchers cannot efficiently predict irregular memory access sequences with high accuracy. Fortunately, in many important irregular applications (e.g., iterative solvers, graph algorithms, and sparse matrix-vector multiplication), memory access sequences repeat over multiple iterations or program phases. When the patterns are long, a conventional spatial-temporal prefetcher can not achieve high prefetching accuracy, but these repeating patterns can be identified by programmers.In this work, we propose a software-assisted hardware prefetcher that focuses on repeating irregular memory access patterns for data structures that cannot benefit from conventional hardware prefetchers. The key idea is to provide a programming interface to record cache miss sequence on the first appearance of a memory access pattern and prefetch through replaying the pattern on the following repeats. The proposed Record-and-Replay (RnR) prefetcher provides a lightweight software interface so that the programmers can specify in the application code: 1) which data structures have irregular memory accesses, 2) when to start the recording, and 3) when to start the replay (prefetching). This work evaluated three irregular workloads with different inputs. For the evaluated workloads and inputs, the proposed RnR prefetcher can achieve on average 2.16× speedup for graph applications and 2.91× speedup for an iterative solver with a sparse matrix-vector multiplication kernel. By leveraging the knowledge from the programmers, the proposed RnR prefetcher can achieve over 95% prefetching accuracy and miss coverage.