Fatahalian, KayvonLuong, EdwardBoulos, SolomonAkeley, KurtMark, William R.Hanrahan, PatDavid Luebke and Philipp Slusallek2013-10-292013-10-292009978-1-60558-603-82079-8687https://doi.org/10.1145/1572769.1572780Current GPUs rasterize micropolygons (polygons approximately one pixel in size) inefficiently. We design and analyze the costs of three alternative data-parallel algorithms for rasterizing micropolygon workloads for the real-time domain. First, we demonstrate that efficient micropolygon rasterization requires parallelism across many polygons, not just within a single polygon. Second, we produce a data-parallel implementation of an existing stochastic rasterization algorithm by Pixar, which is able to produce motion blur and depth-of-field effects. Third, we provide an algorithm that leverages interleaved sampling for motion blur and camera defocus. This algorithm outperforms Pixar s algorithm when rendering objects undergoing moderate defocus or high motion and has the added benefit of predictable performance.