Adaptive Data Prefetcher with Probability Learning in LLC
Jusin Kim, Jiwon Lee, Won Woo Ro · 2023
Data prefetching improves the performance of CPUs by predicting which data will be used in the near future. This paper introduces Adaptive Data Prefetcher (ADP), an effective prefetching technique that enhances the coverage of existing prefetchers. First, ADP consists of two prefetcher blocks. One is a path confidence-based lookahead prefetcher from prior work. The other is an LSTM prefetcher that predicts the next data address to be used based on block offset and page pattern history. The LSTM prefetcher is trained with the memory access patterns that the first prefetcher cannot predict, and thus it can further improve the performance of prefetching. Second, ADP adaptively selects a prefetcher which has a higher probability to hit the address of incoming memory accesses. Together, these features improve the coverage and accuracy of prefetching. In our analysis, we find that ADP achieves an IPC improvement of 24.2% over the confidence-based lookahead prefetcher.