  GPU compilers are complex software programs with many optimizations specific
to target hardware. These optimizations are often controlled by heuristics
hand-designed by compiler experts using time- and resource-intensive processes.
In this paper, we developed a GPU compiler autotuning framework that uses
off-policy deep reinforcement learning to generate heuristics that improve the
frame rates of graphics applications. Furthermore, we demonstrate the
resilience of these learned heuristics to frequent compiler updates by
analyzing their stability across a year of code check-ins without retraining.
We show that our machine learning-based compiler autotuning framework matches
or surpasses the frame rates for 98% of graphics benchmarks with an average
uplift of 1.6% up to 15.8%.
