Kerbl, BernhardKenzel, MichaelSchmalstieg, DieterSteinberger, MarkusVlastimil Havran and Karthik Vaiyanathan2017-12-062017-12-062017978-1-4503-5101-02079-8679https://doi.org/10.1145/3105762.3105777https://diglib.eg.org:443/handle/10.1145/3105762-3105777To e ectively utilize an ever increasing number of processors during parallel rendering, hardware and so ware designers rely on sophisticated load balancing strategies. While dynamic load balancing is a powerful solution, it requires complex work distribution and synchronization mechanisms. Graphics hardware manufacturers have opted to employ static load balancing strategies instead. Speci cally, triangle data is distributed to processors based on its overlap with screenspace tiles arranged in a xed pa ern. While the current strategy of using simple pa erns for a small number of fast rasterizers achieves formidable performance, it is questionable how this approach will scale as the number of processors increases further. To address this issue, we analyze real-world rendering workloads, derive requirements for e ective pa erns, and present ten di erent pa ern design strategies based on these requirements. In addition to a theoretical evaluation of these design strategies, we compare the performance of select pa erns in a parallel sort-middle so ware rendering pipeline on an extensive set of triangle data captured from eight recent video games. As a result, we are able to identify a set of pa erns that scale well and exhibit signi cantly improved performance over na¨ıve approaches.Computing methodologies RasterizationMassively parallel algorithmsStatic load balancingPa ernParallel RenderingSortmiddleGPU10.1145/3105762.3105777