document: A multinomial clustering model for fast simulation of computer architecture designs Computer architects utilize simulation tools to evaluate the merits of a new design feature . The time needed to adequately evaluate the tradeoffs associated with adding any new feature has become a critical issue . Recent work has found that by identifying execution phases present in common workloads used in simulation studies , we can apply clustering algorithms to significantly reduce the amount of time needed to complete the simulation . Our goal in this paper is to demonstrate the value of this approach when applied to the set of industry-standard benchmarks most commonly used in computer architecture studies . We also look to improve upon prior work by applying more appropriate clustering algorithms to identify phases , and to further reduce simulation time . We find that the phase clustering in computer architecture simulation has many similarities to text clustering . In prior work on clustering techniques to reduce simulation time , K-means clustering was used to identify representative program phases . In this paper we apply a mixture of multinomials to the clustering problem and show its advantages over using K-means on simulation data . We have implemented these two clustering algorithms and evaluate how well they can characterize program behavior . By adopting a mixture of multinomials model , we find that we can maintain simulation result fidelity , while greatly reducing overall simulation time . We report results for a range of applications taken from the SPEC2000 benchmark suite .
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