Loading...

Bingo spatial data prefetcher

Bakhshalipour, M ; Sharif University of Technology | 2019

465 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/HPCA.2019.00053
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. 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 exploit this phenomenon to prefetch future memory references and hide the long latency of DRAM accesses. While state-of-the-art spatial data prefetchers are effective at reducing the number of data misses, we observe that there is still significant room for improvement. To select an access pattern for prefetching, existing spatial prefetchers associate observed access patterns to either a short event with a high probability of recurrence or a long event with a low probability of recurrence. Consequently, the prefetchers either offer low accuracy or lose significant prediction opportunities. We identify that associating the observed spatial patterns to just a single event significantly limits the effectiveness of spatial data prefetchers. In this paper, we make a case for associating the observed spatial patterns to both short and long events to achieve high accuracy while not losing prediction opportunities. We propose Bingo spatial data prefetcher in which short and long events are used to select the best access pattern for prefetching. We propose a storage-efficient design for Bingo in such a way that just one history table is needed to maintain the association between the access patterns and the long and short events. Through a detailed evaluation of a set of big-data applications, we show that Bingo improves system performance by 60% over a baseline with no data prefetcher and 11% over the best-performing prior spatial data prefetcher
  6. Keywords:
  7. Big-Data Applications ; Data Prefetching ; Memory System ; Spatial Correlation ; Big data ; Dynamic random access storage ; Supercomputers ; Big data applications ; Data pre-fetching ; Efficient designs ; High probability ; Memory systems ; Spatial correlations ; Spatial patterns ; State of the art ; Memory architecture
  8. Source: 25th IEEE International Symposium on High Performance Computer Architecture, HPCA 2019, 16 February 2019 through 20 February 2019 ; 2019 , Pages 399-411 ; 9781728114446 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8675188