Abstract

With modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such large data, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for both out-of-core and in situ rendering setups. Results show that compared to a well-known scheduling method, our approach on ambient occlusion and path tracing achieves up to 2.3× speedup for the scenes comprising up to billions of triangles extracted from terascale scientific data.