Hybrid Image-/Data-Parallel Rendering Using Island Parallelism

Self Cite

Davis 3 NVIDIA Figure 1: Overview of our method. Given a block-structured or octree-AMR data set (left) we first create the dual mesh (middle) and split that into the truly unstructured elements used to stitch the level boundaries (red) and those that are regular voxels (blue/white checkered). We then cluster voxels to become "gridlets". Right: gridlets colorized by their ID. We build a bounding volume hierarchy over the gridlets and the remaining unstructured elements. The result is a sampleable representation that generates the exact same result as sampling on the dual mesh directly, but with significantly lower memory overhead and higher sampling speed. On our largest data sets, we see memory savings of up to 3× compared to highly compressed state-of-the-art unstructured mesh representations.

Section: Results and Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 94%

See 1 more Smart Citation

Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh

Zellmann

Liu

et al. 2023

Self Cite

“…With these methods established, follow‐up papers identified the problem that AMR sampling is expensive. Then they proposed to use more intricate traversal schemes to allow for space skipping and adaptive sampling [WZU * 21,ZWS * 22a,ZWS * 22b].…”

Section: Discussion and Comparison Of The Surveyed Approachesmentioning

confidence: 99%

“…Representations optimized for volume rendering are sometimes also used for arbitrary sampling, e.g., to compute flow visualizations [ZSM * 22], which involves more divergent memory access patterns. Finally, we observe a recent trend that path tracing is adopted by the sci‐vis community [MSG * 22,ZWS * 22b], which is most likely driven by the recent availability of high‐quality denoising techniques [IGMM22] and requires sample placement at arbitrary positions. Taking individual samples becomes even more costly with this approach, so efficient volume traversal data structures become even more critical.…”

Section: Discussion and Comparison Of The Surveyed Approachesmentioning

confidence: 99%

State‐of‐the‐art in Large‐Scale Volume Visualization Beyond Structured Data

Sarton,

Zellmann,

Demirci

et al. 2023

Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.

Data Parallel Multi‐GPU Path Tracing using Ray Queue Cycling

Wald¹,

Jaroš

Zellmann

2023

Self Cite

We propose a novel approach to data‐parallel path tracing on single‐node/multi‐GPU hardware that builds on ray forwarding, but which aims—above all else—at generality and practicability. We do this by avoiding any attempts at reducing the number of traces or forward operations performed, and instead focus on always using all GPUs' aggregate compute and bandwidth to effectively trace each ray on every GPU. We show that—counter‐intuitively—this is both feasible and desirable; and that when run on typical data‐center/cloud hardware, the resulting framework not only achieves good performance and scalability, but also comes with significantly fewer limitations, assumptions, or preprocessing requirements than existing techniques.