CSR-SMA: Toward Model-Driven Multilevel Analysis and Optimization of Multicomponent Computer Systems

This project seeks to enable model-driven optimizations spanning

multiple levels of a computing system including the architecture,

compiler, algorithm and application layers, for multiple objectives

such as performance, power and productivity. A primary goal is to

develop a comprehensive framework for model-driven multilevel,

multiobjective optimizations with a focus on chip multiprocessors

(CMPs) and large-scale, sparse engineering and scientific applications.

Key activities concern developing (i) parameterized models to

compose models of the application, architecture and compiler

transformations, (ii) an optimization framework to determine

multiobjective, optimal or pareto-optimal designs while modeling

uncertainties, and (iii) undergraduate and graduate courses on the

methodology for multilevel optimizations of computing systems,

The proposed techniques yield metrics at coarse- and medium-scales

that can be used with stochastic optimization techniques to determine

optimal design choices. The medium-scale metrics are obtained by

simulating a concatenated discrete time Markov Chain model (C-DT-MCM)

which incorporates both the deterministic and stochastic aspects

of multilevel optimizations and their impacts. Such C-DT-MCMs can

be simulated very efficiently to obtain traces which can then be

compared using statistical techniques with those from detailed

hardware simulation. Using this approach, only promising design

options need be studied in detail, using current modalities, such

as detailed hardware simulators, which can be prohibitively slow

for larger CMP architectures.