Data prefetching by dependence graph precomputation

  • Authors:

  • Murali Annavaram;Jignesh M. Patel;Edward S. Davidson

  • Affiliations:

  • Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor;Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor;Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor

  • Venue:
  • ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
  • Year:
  • 2001

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Abstract

Data cache misses reduce the performance of wide-issue processors by stalling the data supply to the processor. Prefetching data by predicting the miss address is one way to tolerate the cache miss latencies. But current applications with irregular access patterns make it difficult to accurately predict the address sufficiently early to mask large cache miss latencies. This paper explores an alternative to predicting prefetch addresses, namely precomputing them. The Dependence Graph Precomputation scheme (DGP) introduced in this paper is a novel approach for dynamically identifying and precomputing the instructions that determine the addresses accessed by those load/store instructions marked as being responsible for most data cache misses. DGP's dependence graph generator efficiently generates the required dependence graphs at run time. A separate precomputation engine executes these graphs to generate the data addresses of the marked load/store instructions early enough for accurate prefetching. Our results show that 94% of the prefetches issued by DGP are useful, reducing the D-cache miss stall time by 47%. Thus DGP takes us about half way from an already highly tuned baseline system toward perfect D-cache performance. DGP improves the overall performance of a wide range of applications by 7% over tagged next line prefetching, by 13% over a baseline processor with no prefetching, and is within 15% of the perfect D-cache performance.