Efficient mart-aided modeling for microarchitecture design space exploration and performance prediction

ABSTRACT

Computer architects usually evaluate new designs by cycle-accurate processor simulation. This approach provides detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied in a larger design space. In this paper, we propose an automated performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. Our method not only produces highly accurate estimations for unsampled points in the design space, but also provides interpretation tools that help investigators to understand performance bottlenecks. According to our experiments, by sampling only 0.02% of the full design space with about 15 millions points, the median percentage errors, based on 5000 independent test points, range from 0.32% to 3.12% in 12 benchmarks. Even for the worst-case performance, the percentage errors are within 7% for 10 out of 12 benchmarks. In addition, the proposed model can also help architects to find important design parameters and performance bottlenecks.

REFERENCES

• 1: J. Friedman, "Greedy function approximation: a gradient boosting machine", The Annals of Statistics, 29: 1189--1232, 2001.
• 2: E. 0pek, S. A. McKee, B. R. Supinski, "M. Schulz and R. Caruana. "Efficiently exploring architectural design spaces via predictive modeling," ASPLOS XII, Oct. 2006.
• 3: Benjamin C. Lee , David M. Brooks, Accurate and efficient regression modeling for microarchitectural performance and power prediction, Proceedings of the 12th international conference on Architectural support for programming languages and operating systems, October 21-25, 2006, San Jose, California, USA
• 4: S. Sair, M. Charney, "Memory Behavior of the SPEC2000 Benchmark Suit," Tech. Report, IBM Corp. Oct. 2000.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Modeling techniques

Additional Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Measurement techniques

General Terms:
Design, Experimentation, Measurement, Performance

Keywords:
MART-aided models, design space exploration, performance prediction