Name
Title | ||
|---|---|---|
Independent Component Analysis and Evolutionary Algorithms for Building Representative Benchmark Subsets |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISPASS.2008.4510749 | Austin, TX |
Keywords | Field | DocType |
representative subsets,experimental results validate,independent component analysis,building representative benchmark subsets,original large set,prior technique,original set,necessary information,representative subset,evolutionary algorithms,representative workloads,similar benchmarks,statistical analysis technique,principal component analysis,statistical analysis,set theory,evolutionary computation,correlation,microarchitecture,evolutionary algorithm,computational modeling,system performance,maximum likelihood estimation,computational complexity,clustering algorithms,algorithm design and analysis | Set theory,Data mining,Algorithm design,Evolutionary algorithm,Computer science,Evolutionary computation,Independent component analysis,Artificial intelligence,Cluster analysis,Principal component analysis,Machine learning,Computational complexity theory | Conference |
ISBN | Citations | PageRank |
978-1-4244-2233-3 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilios N. Christopoulos | 1 | 35 | 4.51 |
| David J. Lilja | 2 | 1878 | 175.33 |
| Paul R. Schrater | 3 | 141 | 22.71 |
| Apostolos Georgopoulos | 4 | 0 | 0.34 |