Idempotent processor architecture

  • Authors:

  • Marc de Kruijf;Karthikeyan Sankaralingam

  • Affiliations:

  • University of Wisconsin, Madison;University of Wisconsin, Madison

  • Venue:
  • Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture
  • Year:
  • 2011

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Abstract

Improving architectural energy efficiency is important to address diminishing energy efficiency gains from technology scaling. At the same time, limiting hardware complexity is also important. This paper presents a new processor architecture, the idempotent processor architecture, that advances both of these directions by presenting a new execution paradigm that allows speculative execution without the need for hardware checkpoints to recover from mis-speculation, instead using only re-execution to recover. Idempotent processors execute programs as a sequence of compiler-constructed idempotent (re-executable) regions. The nature of these regions allows precise state to be reproduced by re-execution, obviating the need for hardware recovery support. We build upon the insight that programs naturally decompose into a series of idempotent regions and that these regions can be large. The paradigm of executing idempotent regions, which we call idempotent processing, can be used to support various types of speculation, including branch prediction, dependence prediction, or execution in the presence of hardware faults or exceptions. In this paper, we demonstrate how idempotent processing simplifies the design of in-order processors. Conventional in-order processors suffer from significant complexities to achieve high performance while supporting the execution of variable latency instructions and enforcing precise exceptions. Idempotent processing eliminates much of these complexities and the resulting inefficiencies by allowing instructions to retire out of order with support for re-execution when necessary to recover precise state. Across a diverse set of benchmark suites, our quantitative results show that we obtain a geometric mean performance increase of 4.4% (up to 25% and beyond) while maintaining an overall reduction in power and hardware complexity.