The SimpleScalar tool set, version 2.0
ACM SIGARCH Computer Architecture News
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
An experimental comparison of model-based clustering methods
Machine Learning
Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Workload characterization of emerging computer applications
Machine Learning
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Computer
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Reducing State Loss For Effective Trace Sampling of Superscalar Processors
ICCD '96 Proceedings of the 1996 International Conference on Computer Design, VLSI in Computers and Processors
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Experiments with Random Projection
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Model-Based Hierarchical Clustering
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling
Proceedings of the 30th annual international symposium on Computer architecture
Proceedings of the 30th annual international symposium on Computer architecture
Comparing Program Phase Detection Techniques
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
| Hi-index | 0.00 |
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.