CodeSeer: input-dependent code variants selection via machine learning

In high performance computing (HPC), scientific simulation codes are executed repeatedly with different inputs. The peak performance of these programs heavily depends on various compiler optimizations, which are often selected agnostically on program input or may be selected with sensitivity to just a single input. When subsequently executed, often with different inputs, performance may suffer for all or all but the one input tested, and for the latter potentially even compared to the O3 baseline. This work proposes a new auto-tuning framework, CodeSeer, to assess and improve existing input-agnostic or single-input centric rigid application tuning methods. Aided by CodeSeer, it is observed that modern HPC programs expose different types of input sensitivities, which present a significant challenge for prior work. To tackle this problem, CodeSeer proceeds with several machine learning models to predict the best per-input code variant on-the-fly. Our evaluation shows that CodeSeer incurs less than 0.01 second overhead, predicts the best code variant with a geometric mean precision 92% of the time and is capable of improving per-input peak performance to unprecedented levels.