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Modeling in Computer Architecture

Modeling in Computer Architecture. Matthew Jacob. Architecture Evaluation Challenges. Skadron, Martonosi, August, Hill, Lilja and Pai, IEEE Computer, Aug 2003 “Quantitative evaluation is the mainstay, but system complexity makes it troublesome”

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Modeling in Computer Architecture

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  1. Modeling in Computer Architecture Matthew Jacob

  2. Architecture Evaluation Challenges • Skadron, Martonosi, August, Hill, Lilja and Pai, IEEE Computer, Aug 2003 • “Quantitative evaluation is the mainstay, but system complexity makes it troublesome” • There has been a dramatic shift towards simulation

  3. Simulation is the Preferred Tool

  4. Simulation: Are there any real alternatives? • “Knee-jerk negative reactions from program committee members … effectively discourages the research community from exploring other useful and possibly more informative modeling techniques” • “Developing scientific methods for abstracting evaluations to explore large design spaces is imperative”

  5. What about analytical models? • Example: Karkhanis and Smith, A First-order Model of Superscalar Processors, 31st ISCA 2004 • Analytical model for estimating superscalar processor program CPI (Cycles per Instruction)

  6. What about analytical models? • Example: Karkhanis and Smith, A First-order Model of Superscalar Processors, 31st ISCA 2004 • Analytical model for estimating superscalar processor program CPI • 5.8% average error - Uses “expert knowledge”

  7. How reliable is expert knowledge? • “I think there is a world market for maybe five computers.” (1943) • Thomas Watson, Chairman, IBM • “640K ought to be enough for anybody.” (1981) • Bill Gates • “$100 million dollars is way too much to pay for Microsoft.” (1982) • IBM • “There is no reason anyone would want a computer in their home.” (1977) • Ken Olson, President, Chairman and Founder, DEC

  8. What is Empirical Modeling? • Extracting models from measured data • We can use simulators to generate the data Linear models Ease of Interpretation Neural nets Prediction accuracy

  9. Modeling Out-of-order Superscalars (P. J. Joseph, Kapil Vaswani) • Build models to help understand the relative importance of design parameters and also of their interactions The first (and still only) systematic approach available • Build an accurate predictive model The first (and still most efficient) predictive modeling technique available • Demonstrate the use of such models

  10. References • `Construction and Use of Linear Regression Models for Processor Performance Analysis’, with P. J. Joseph, Kapil Vaswani, HPCA-12, 2006 • `A Predictive Perfomance Model for Superscalar Processors’, with P. J. Joseph, Kapil Vaswani, MICRO-39, 2006  • `Microarchitecture Sensitive Empirical Models for Compiler Optimizations’, with Kapil Vaswani, P. J. Joseph,  Y. N. Srikant, CGO-5, 2007

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