Hardware-Specific Selection the Most Fast-Running Software Components
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- 关键词：Software development ; Software component selection ; Runtime prediction ; Computational system characteristics ; Machine learning
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：10049
- 期：1
- 页码：354-364
- 丛书名：Algorithms and Architectures for Parallel Processing
- ISBN：978-3-319-49956-7
- 卷排序：10049
文摘
Software development problems include, in particular, selection of the most fast-running software components among the available ones. In the paper it is proposed to develop a prediction model that can estimate software component runtime to solve this problem. Such a model is built as a function of algorithm parameters and computational system characteristics. It also has been studied which of those features are the most representative ones. As a result of these studies a two-stage scheme of prediction model development based on linear and non-linear machine learning algorithms has been formulated. The paper presents a comparative analysis of runtime prediction results for solving several linear algebra problems on 84 personal computers and servers. The use of the proposed approach shows an error of less than 22% for computational systems represented in the training data set.