Independent component analysis and evolutionary algorithms for building representative benchmark subsets

This work addresses the problem of building representative subsets of benchmarks from an original large set of benchmarks, using statistical analysis techniques. The subsets should be developed in this way to include only the necessary information for evaluating the performance of a computer system or application. The development of representative workloads is not a trivial procedure, since incorrectly selecting benchmarks the representative subset can produce erroneous results. A number of statistical analysis techniques have been developed for identifying representative workloads. The goal of these approaches is to reduce the dimensionality of the original set of benchmarks prior to identifying similar benchmarks. In this work we propose a combination of Independent Component Analysis (ICA) and Evolutionary Algorithm (EA) as a more efficient way for reducing the computational complexity of the problem and the redundant information of the original set of benchmarks. Experimental results validate that the proposed technique generates more representative workloads than prior techniques.