Distributed Out-of-Core Approach for In-Situ Volume Rendering of Massive Dataset

Sarton, Jonathan; Rémion, Yannick; Lucas, Laurent

doi:10.1007/978-3-030-34356-9_47

Cited by 2 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a complete review, we refer the reader to the state‐of‐the‐art report by Beyer et al [BHP15]. Recent papers presenting out‐of‐core paging and streaming approaches for structured volume data include the ones by Hadwiger et al [HAAB * 18], by Beyer et al [BMA * 19], by Wang et al [WWJ19], and by Sarton et al [SRL19, SCRL20]. We concentrate specifically on the data management aspects of such systems.…”

Section: Rendering Of Structured Volume Datamentioning

confidence: 99%

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

Sarton,

Zellmann,

Demirci

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

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.

show abstract