Integrated predicated and speculative execution in the IMPACT EPIC architecture

  • Authors:
  • David I. August;Daniel A. Connors;Scott A. Mahlke;John W. Sias;Kevin M. Crozier;Ben-Chung Cheng;Patrick R. Eaton;Qudus B. Olaniran;Wen-mei W. Hwu

  • Affiliations:
  • Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Hewlett-Packard Laboratories, Hewlett-Packard, Palo Alto, CA;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL;Center for Reliable and High-Performance Computing, University of Illinois Urbana-Champaign, IL

  • Venue:
  • Proceedings of the 25th annual international symposium on Computer architecture
  • Year:
  • 1998

Quantified Score

Hi-index 0.00

Visualization

Abstract

Explicitly Parallel Instruction Computing (EPIC) architectures require the compiler to express program instruction level parallelism directly to the hardware. EPIC techniques which enable the compiler to represent control speculation, data dependence speculation, and predication have individually been shown to be very effective. However, these techniques have not been studied in combination with each other. This paper presents the IMPACT EPIC Architecture to address the issues involved in designing processors based on these EPIC concepts. In particular, we focus on new execution and recovery models in which microarchitectural support for predicated execution is also used to enable efficient recovery from exceptions caused by speculatively executed instructions. This paper demonstrates that a coherent framework to integrate the three techniques can be elegantly designed to achieve much better performance than each individual technique could alone provide.