Using an Artificial Neural Network to Predict Loop Transformation Time

Automatic software parallelization is a key issue for high performance computing. There are many algorithms to transform program loop nests to multithreaded code. However, the time of a transformation process is usually unknown, especially for transitive closure based algorithms. The computational complexity of transitive closure calculation algorithms is relatively high and may prevent applying corresponding transformations. The paper presents the prediction of loop transformation time by means of an artificial neural network for the source-to-source TRACO compiler. The analysis of a loop nest structure and dependences is used to estimate the time of TRACO transformations. The training of a Feed-Forward Neural Network is used to make a decision about transformation time. Experiments with various NAS Parallel Benchmarks show promise for the use of neural networks in automatic code parallelization and optimization.