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Optimizing CMP Design: Statistical Analysis and Analytical Modeling Approaches

This paper presents two methods for narrowing the design space in Chip Multiprocessor (CMP) systems: Plackett-Burman statistical analysis and analytical modeling. As integration technology advances, the ability to place multiple processors on a single chip becomes increasingly vital. However, simulating the complexities of these designs is time-consuming. Our results indicate that CMPs significantly influence the interaction between processor cores and memory subsystems, with the core count being the most critical design factor. We detail our findings and propose solutions for efficient design evaluation.

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Optimizing CMP Design: Statistical Analysis and Analytical Modeling Approaches

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Presentation Transcript

  1. CMP Design Choices

  2. To appear in the grading of CS838

  3. Sam Koblenski Peter McClone

  4. Abstract

  5. In recent years, as feature sizes decrease, the possibility of placing multiple processors on a chip has become a more viable option. Obtaining simulation for this increasingly complex design space is extremely time consuming.We propose the use of two methods for narrowingthe CMP design space, Plackett and Burman statistical analysisand analytical modeling. Our results show that CMPs complicate the relationship between processor cores and the memory subsystem and that the number of cores on chip is the most critical design parameter.

  6. Section I- Introduction

  7. Section II - Method 1: Placket and BurmanDesign

  8. ReferenceParameterBenchmarkProcessorsTransactionsOLTP2200OLTP16100JBB220000JBB1610000Section III - Method 2: Analytical Modeling • Exact MVA Model

  9. Approximate MVA

  10. Section IV - Results

  11. Section V - Conclusion

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