Energy-aware data prefetching for general-purpose programs

  • Authors:

  • Yao Guo;Saurabh Chheda;Israel Koren;C. Mani Krishna;Csaba Andras Moritz

  • Affiliations:

  • ECE Dept., University of Massachusetts, Amherst, MA;BlueRISC Inc., Hadley, MA;ECE Dept., University of Massachusetts, Amherst, MA;ECE Dept., University of Massachusetts, Amherst, MA;ECE Dept., University of Massachusetts, Amherst, MA

  • Venue:
  • PACS'04 Proceedings of the 4th international conference on Power-Aware Computer Systems
  • Year:
  • 2004

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Abstract

There has been intensive research on data prefetching focusing on performance improvement, however, the energy aspect of prefetching is relatively unknown. Our experiments show that although software prefetching tends to be more energy efficient, hardware prefetching outperforms software prefetching on most of the applications in terms of performance. This paper proposes several techniques to make hardware-based data prefetching power-aware. Our proposed techniques include three compiler-based approaches which make the prefetch predictor more power efficient. The compiler identifies the pattern of memory accesses in order to selectively apply different prefetching schemes depending on predicted access patterns and to filter out unnecessary prefetches. We also propose a hardware-based filtering technique to further reduce the energy overhead due to prefetching in the L1 cache. Our experiments show that the proposed techniques reduce the prefetching-related energy overhead by close to 40% without reducing its performance benefits.