Exceeding the dataflow limit via value prediction

  • Authors:

  • Mikko H. Lipasti;John Paul Shen

  • Affiliations:

  • Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh PA;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh PA

  • Venue:
  • Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
  • Year:
  • 1996

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Abstract

For decades, the serialization constraints induced by true data dependences have been regarded as an absolute limit--the dataflow limit--on the parallel execution of serial programs. This paper proposes a new technique--value prediction--for exceeding that limit that allows data dependent instructions to issue and execute in parallel without violating program semantics. This technique is built on the concept of value locality, which describes the likelihood of the recurrence of a previously-seen value within a storage location inside a computer system. Value prediction consists of predicting entire 32- and 64-bit register values based on previously-seen values. We find that such register values being written by machine instructions are frequently predictable. Furthermore, we show that simple micro- architectural enhancements to a modern microprocessor implementation based on the PowerPC 620 that enable value prediction can effectively exploit value locality to collapse true dependences, reduce average result latency, and provide performance gains of 4.5%-23% (depending on machine model) by exceeding the dataflow limit.