Practical aggregation of semantical program properties for machine learning based optimization
Mircea Namolaru, Albert Cohen, Grigori Fursin, Ayal Zaks, Ari Freund · 2010
Iterative search combined with machine learning is a promising approach to design optimizing compilers harnessing the complexity of modern computing systems. While traversing a program optimization space, we collect characteristic feature vectors of the program, and use them to discover correlations across programs, target architectures, data sets, and performance. Predictive models can be derived from such correlations, effectively hiding the time-consuming feedback-directed optimization process from the application programmer.