Memory Dependence Predictors

Abstract

This paper details the implementation of a novel Memory Dependence Predictor based on the relative distance between loads and their dependent stores in the dynamic program order. In presence of unknown store addresses blind speculation never stalls loads which are ready. This results in load violations since the loads may depend on those stores with unknown addresses. To prevent the violation penalty we employ the use of memory dependence predictors that train a predictor table to detect load-store dependency. In this project we will be implementing two kinds of memory dependence predictors namely, Sticky Bit and Store Vectors.

Country : USA

1 Simran Satish Kulkarni2 Soumil Krishnanand Heble3 Dr. Eric Rotenberg

  1. Department of Electrical & Computer Engineering North Carolina State University, Raleigh, USA
  2. Department of Electrical & Computer Engineering North Carolina State University, Raleigh, USA
  3. Department of Electrical & Computer Engineering North Carolina State University, Raleigh, USA

IRJIET, Volume 7, Special Issue of ICRTET- 2023 pp. 248-252

IRJIET.ICRTET52

References

  1. Speculation Techniques for Improving Load Related Instruction Scheduling, Adi Yoaz, Mattan Erez, Ronny Ronen, and Stephan Jourdan.
  2. Memory Dependence Prediction using Store Sets, George Z. Chrysos and Joel S. Emer.
  3. Memory Dependence Prediction, by Andreas Ioannis Moshovos.
  4. Store Vectors for Scalable Memory Dependence Prediction and Scheduling, Samantika Subramaniam Gabriel H. Loh, Georgia Institute of Technology.
  5. R. E. Kessler. The Alpha 21264 Microprocessor. IEEE Micro Magazine, 19(2):24.36, March.April 1999.