Machine learning techniques for improved data prefetching

Diana Guttman, Mahmut Taylan Kandemir, Meena Arunachalam, Rahul Khanna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

With the advent of teraflop-scale computing on both a single coprocessor and many-core designs, there is tremendous need for techniques to fully utilize the compute power by keeping cores fed with data. Data prefetching has been used as a popular method to hide memory latencies by fetching data proactively before the processor needs the data. Fetching data ahead of time from the memory subsystem into faster caches reduces observable latencies or wait times on the processor end and this improves overall program execution times. We study two types of prefetching techniques that are available on a 61-core Intel Xeon Phi co-processor, namely software (compiler-guided) prefetching and hardware prefetching on a variety of workloads. Using machine learning techniques, we synthesize workload phases and the sequence of phase patterns using raw performance data from hardware counters such as memory bandwidth, miss ratios, prefetches issued, etc. Furthermore, we use performance data from workloads with different impacts and behaviors under various prefetcher settings. Our contribution can help in future prefetching design in the following ways: (1) to identify phases within workloads that have different characteristics and behaviors and help dynamically modify prefetch types and intensities to suit the phase; (2) to manage auto setting of prefetcher knobs without great effort from the user; (3) to influence software and hardware prefetching interaction designs in future processors; and (4) to use valuable insights and performance data in many areas such as power provisioning for the nodes in a large cluster to maximize both energy and performance efficiencies.

Original languageEnglish (US)
Title of host publication5th International Conference on Energy Aware Computing Systems and Applications, ICEAC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479917716
DOIs
StatePublished - Dec 9 2015
Event5th International Conference on Energy Aware Computing Systems and Applications, ICEAC 2015 - Cairo, Egypt
Duration: Mar 24 2015Mar 26 2015

Publication series

Name5th International Conference on Energy Aware Computing Systems and Applications, ICEAC 2015

Other

Other5th International Conference on Energy Aware Computing Systems and Applications, ICEAC 2015
CountryEgypt
CityCairo
Period3/24/153/26/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Energy Engineering and Power Technology

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