Predictive design space exploration using genetically programmed response surfaces

ABSTRACT

Exponential increases in architectural design complexity threaten to make traditional processor design optimization techniques intractable. Genetically programmed response surfaces (GPRS) address this challenge by transforming the optimization process from a lengthy series of detailed simulations into the tractable formulation and rapid evaluation of a predictive model. We validate GPRS methodology on realistic processor design spaces and compare it to recently proposed techniques for predictive microarchitectural design space exploration.

REFERENCES

• 1: L. Alvarez. Design Optimization based on Genetic Programming. PhD thesis, Univ. of Bradford, 2000.
• 2: P. Audze and V. Eglais. A new approach to the planning out of experiments. In Problems of dynamics and strength, volume 35, 1977. In Russian.
• 3: S. J. Bates , J. Sienz , D. S. Langley, Formulation of the Audze--Eglais uniform Latin hypercube design of experiments, Advances in Engineering Software, v.34 n.8, p.493-506, 19 June 2003 [doi>10.1016/S0965-9978(03)00042-5]
• 4: George E P Box , Norman R Draper, Empirical model-building and response surface, John Wiley & Sons, Inc., New York, NY, 1986
• 5: John D. Davis , James Laudon , Kunle Olukotun, Maximizing CMP Throughput with Mediocre Cores, Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques, p.51-62, September 17-21, 2005 [doi>10.1109/PACT.2005.42]
• 6: L. Eeckhout, S. Nussbaum, J. Smith, and K. DeBosschere. Statistical simulation: Adding efficiency to the computer designer's toolbox. In IEEE Micro, Sept./Oct. 2003.
• 7: L. Eeckhout, J. Sampson, and B. Calder. Exploiting program microarchitecture independent characteristics and phase behavior for reduced benchmark suite simulation. In IISWC, Oct. 2005.
• 8: Joel Emer , Nikolas Gloy, A language for describing predictors and its application to automatic synthesis, Proceedings of the 24th annual international symposium on Computer architecture, p.304-314, June 01-04, 1997, Denver, Colorado, United States
• 9: Stijn Eyerman , Lieven Eeckhout , Koen De Bosschere, Efficient design space exploration of high performance embedded out-of-order processors, Proceedings of the conference on Design, automation and test in Europe: Proceedings, March 06-10, 2006, Munich, Germany
• 10: Stijn Eyerman , Lieven Eeckhout , Tejas Karkhanis , James E. Smith, A performance counter architecture for computing accurate CPI components, Proceedings of the 12th international conference on Architectural support for programming languages and operating systems, October 21-25, 2006, San Jose, California, USA
• 11: Engin Ïpek , Sally A. McKee , Rich Caruana , Bronis R. de Supinski , Martin Schulz, Efficiently exploring architectural design spaces via predictive modeling, Proceedings of the 12th international conference on Architectural support for programming languages and operating systems, October 21-25, 2006, San Jose, California, USA
• 12: P. Joseph, K. Vaswani, and M. J. Thazhuthaveetil. Construction and use of linear regression models or processor performance analysis. In HPCA, Feb. 2006.
• 13: Tejas S. Karkhanis , James E. Smith, A First-Order Superscalar Processor Model, Proceedings of the 31st annual international symposium on Computer architecture, p.338, June 19-23, 2004, München, Germany
• 14: A J KleinOsowski , David J. Lilja, MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research, IEEE Computer Architecture Letters, v.1 n.1, p.7, January 2002 [doi>10.1109/L-CA.2002.8]
• 15: John R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT Press, Cambridge, MA, 1992
• 16: Rakesh Kumar , Dean M. Tullsen , Norman P. Jouppi, Core architecture optimization for heterogeneous chip multiprocessors, Proceedings of the 15th international conference on Parallel architectures and compilation techniques, September 16-20, 2006, Seattle, Washington, USA [doi>10.1145/1152154.1152162]
• 17: 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
• 18: Y. Li, B. C. Lee, D. Brooks, Z. Hu, and K. Skadron. Cmp design space exploration subject to physical constraints. In HPCA, Feb. 2006.
• 19: M. Lourakis. Ievmar: Levenberg-marquardt nonlinear least squares algorithms in C/C++. http://www.ics.forth.gr/lourakis/levmar/, 2004.
• 20: A. Phansalkar , A. Joshi , L. Eeckhout , L. K. John, Measuring Program Similarity: Experiments with SPEC CPU Benchmark Suites, Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2005, p.10-20, March 20-22, 2005 [doi>10.1109/ISPASS.2005.1430555]
• 21: G. Venter, R. Haftka, and J. Starnes. Construction of response surfaces for design optimization applications. In 6th Symp. on Mult. Anal. and Opt., 1996.
• 22: Joshua J. Yi , David J. Lilja , Douglas M. Hawkins, A Statistically Rigorous Approach for Improving Simulation Methodology, Proceedings of the 9th International Symposium on High-Performance Computer Architecture, p.281, February 08-12, 2003

INDEX TERMS

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

General Terms:
Design, Measurement, Performance

Keywords:
design space exploration, genetic programming, predictive modeling