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Efficiently Exploring Architectural Design Spaces via Predictive Modeling

Author
Ipek, Engin
Abstract
Computer architects rely on cycle-by-cycle simulation to evaluate the impact of design
choices and to understand tradeoffs and interactions among design parameters. Although
several techniques reduce time per individual simulation, efficiently exploring
exponential-size design spaces spanned by several interacting parameters remains an
open problem: the sheer number of experiments renders detailed simulation intractable.
We attack this via an automated approach for building highly accurate and confident
predictive models of design spaces. We collect simulation data incrementally, giving
reliable estimates of model error on the full parameter space at each step of the building
process. As validation, we perform sensitivity studies on memory system and microprocessor
design spaces (conducting over 300K detailed simulations). Our models generally
predict IPC with less than 1-2% error, even when trained on as little as 2% of the full
design space. Further, our mechanism is orthogonal to techniques that reduce simulation
runtimes. SimPoint [23] reduces the number of simulated instructions per experiment
by 8-62?. We reduce the total number of simulated instructions by 50-200?. Combining
our approach with SimPoint reduces the number of simulated instructions required
to complete thorough design-space explorations by 1000-13,000?. Our approach has
potential to quantitatively and qualitatively transform computer architecture research,
enabling studies heretofore beyond our computational abilities.
Date Issued
2007-07-14Subject
Computing Methodologies; Simulation and Modeling; Hardware; Performance Analysis
Type
dissertation or thesis