Algorithm-level Feedback-controlled Adaptive data prefetcher: Accelerating data access for high-performance processors

Yong Chen, Huaiyu Zhu, Hui Jin, Xian He Sun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The rapid advance of processor architectures such as the emerged multicore architectures and the substantially increased computing capability on chip have put more pressure on the sluggish memory systems than ever. In the meantime, many applications become more and more data intensive. Data-access delay, not the processor speed, becomes the leading performance bottleneck of high-performance computing. Data prefetching is an effective solution to accelerating applications' data access and bridging the growing gap between computing speed and data-access speed. Existing works of prefetching, however, are very conservative in general, due to the computing power consumption concern of the past. They suffer low effectiveness especially when applications' access pattern changes. In this study, we propose an Algorithm-level Feedback-controlled Adaptive (AFA) data prefetcher to address these issues. The AFA prefetcher is based on the Data-Access History Cache, a hardware structure that is specifically designed for data access acceleration. It provides an algorithm-level adaptation and is capable of dynamically adapting to appropriate prefetching algorithms at runtime. We have conducted extensive simulation testing with the SimpleScalar simulator to validate the design and to analyze the performance gain. The simulation results show that the AFA prefetcher is effective and achieves considerable IPC (Instructions Per Cycle) improvement for 21 representative SPEC-CPU benchmarks.

Original languageEnglish
Pages (from-to)533-551
Number of pages19
JournalParallel Computing
Issue number10-11
StatePublished - Oct 2012


  • Adaptive prefetching
  • Application accelerator
  • Data access acceleration
  • Data prefetching
  • High-performance processors
  • Memory hierarchy
  • Memory wall


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