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As the complexity of many-core processors grow, meeting performance, energy, temperature, reliability, and noise requirements under dynamically changing operating conditions requires run-time optimization of all parts of the computing stack - architecture, system software, and applications. Unfortunately, the combination of design parameters for the entire computing stack results in an operating space...
Exploring the vast microarchitectural design space of chip multiprocessors (CMPs) through the traditional approach of exhaustive simulations is impractical due to the long simulation times and its super-linear increase with core scaling. Kernel based statistical machine learning algorithms can potentially help predict multiple performance metrics with non-linear dependence on the CMP design parameters...
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