Parallel computation of stream surfaces on GPUs

Stream surfaces can reveal complex flow behaviors that are not available on streamlines, but their construction algorithm is also more sophisticated. A common strategy is to sample a series of streamlines from a seeding curve and stitch the neighboring streamlines to form surfaces. To maintain a uniform density of sample points, extra streamlines may be inserted when the flow diverges. This leads to high computation cost with sequential insertion operations, hindering the use of stream surfaces in interactive scenarios and large-scale learning. In this paper, we proposed two parallel strategies to construct stream surfaces on GPUs. Both strategies generate stream surfaces in multiple passes, where each pass consists of a streamline tracing phase and a ribbon construction phase. The first strategy aims at increasing throughput, by hiding the long generation time of an individual surface with computation from other surfaces. The second strategy aims at reducing latency, by generating redundant streamlines for a single surface. We evaluate the performance of the two strategies and compare them with a CPU baseline using ten datasets exhibiting various characteristics.