Slice-processors: an implementation of operation-based prediction

  • Authors:
  • Andreas Moshovos;Dionisios N. Pnevmatikatos;Amirali Baniasadi

  • Affiliations:
  • Electrical and Computer Engineering, University of Toronto;Electronic and Computer Engineering, Technical University of Crete;Electrical and Computer Engineering, Northwestern University

  • Venue:
  • ICS '01 Proceedings of the 15th international conference on Supercomputing
  • Year:
  • 2001

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Abstract

We describe the Slice Processor micro-architecture that implements a generalized operation-based prefetching mechanism. Operation-based prefetchers predict the series of operations, or the computation slice that can be used to calculate forthcoming memory references. This is in contrast to outcome-based predictors that exploit regularities in the (address) outcome stream. Slice processors are a generalization of existing operation-based prefetching mechanisms such as stream buffers where the operation itself is fixed in the design (e.g., address + stride). A slice processor dynamically identifies frequently missing loads and extracts on-the-fly the relevant address computation slices. Such slices are then executed in-parallel with the main sequential thread prefetching memory data. We describe the various support structures and emphasize the design of the slice detection mechanism. We demonstrate that a relatively simple organization can significantly improve performance over an aggressive, dynamically-scheduled processor and for a set of pointer-intensive programs and for some integer applications from the SPEC'95 suite. In particular, a slice processor that can detect slices of up to 8 instructions extracted over of a region of up to 32 instructions improves performance by 11% on the average (even if slice detection requires up to 32 cycles). Allowing slices of up to 16 instructions results in an average performance improvement of 15%. Finally, we study how our operation-based predictor interacts with an outcome-based one and find them mutually beneficial.