Compiling generalized histograms for GPU

We present and evaluate an implementation technique for histogram-like computations on GPUs that ensures both work-efficient asymptotic cost, support for arbitrary associative and commutative operators, and efficient use of hardware-supported atomic operations when applicable. Based on a systematic empirical examination of the design space, we develop a technique that balances conflict rates and memory footprint.