A Neural Network Based GCC Cost Model for Faster Compiler Tuning
Hafsah Shahzad, Ahmed Sanaullah, Sanjay Arora, Ulrich Drepper, Martin Herbordt · 2024
Machine learning models have been found to be effective in predicting compiler heuristics, but are limited by their very long training times. This is because computing the impact of transformations on a code, e.g., through the performance values, involves invoking the downstream compiler. One way to circumvent the cost-computation bottleneck is to devise accurate cost models that can be trained to predict the target metric. In this paper, we develop a neural net based cost function that can accurately predict binary code size for GCC-based compilation. The input to the model is a comprehensive list of features that have been extracted offline from GCC's intermediate tree representation and the compiler flags that need to be evaluated by a compiler tuning workload. To extract the code features, we have built a GIMPLE analysis framework that can generate feature sets from intermediate representations at different stages of the compilation process. Our results show that the cost model has a mean absolute percentage error of just 8%, and a Spearman correlation of 0.98 between predicted and measured binary size of the test applications. We also demonstrate that compiler pass selection for feature extraction has a significant benefit on the accuracy of the model. Finally, we show that the cost model can reduce metric evaluation time by multiple orders of magnitude.