Volume rendering techniques for scientific visualization have recently shifted toward Monte Carlo (MC) methods for their flexibility and robustness, but their use in multi-channel visualization remains underexplored. Traditional multi-channel volume rendering often relies on arbitrary, non-physically based color blending functions that hinder interpretation. We introduce multi-density Woodcock tracking, a simple extension of Woodcock tracking that leverages an MC method to produce high-fidelity, physically grounded multi-channel renderings without arbitrary blending. By generalizing Woodcock's distance tracking, we provide a unified blending modality that also integrates blending functions from prior works. We further implement effects that enhance boundary and feature recognition. By accumulating frames in real-time, our approach delivers high-quality visualizations with perceptual benefits, demonstrated on diverse datasets.

Interactively visualizing large finite element simulation data on High-Performance Computing (HPC) systems poses several difficulties. Some of these relate to unstructured data, which, even on a single node, is much more expensive to render compared to structured volume data. Worse yet, in the data parallel rendering context, such data with highly non-convex spatial domain boundaries will cause rays along its silhouette to enter and leave a given rank's domains at different distances. This straddling, in turn, poses challenges for both ray marching, which usually assumes successive elements to share a face, and compositing, which usually assumes a single fragment per pixel per rank. We holistically address these issues using a combination of three inter-operating techniques: first, we use a highly optimized GPU ray marching technique that, given an entry point, can march a ray to its exit point with high-performance by exploiting an exclusive-or (XOR) based compaction scheme. Second, we use hardware-accelerated ray tracing to efficiently find the proper entry points for these marching operations. Third, we use a "deep" compositing scheme to properly handle cases where different ranks' ray segments interleave in depth. We use GPU-to-GPU remote direct memory access (RDMA) to achieve interactive frame rates of 10-15 frames per second and higher for our motivating use case, the Fun3D NASA Mars Lander.

Abstract Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU‐accelerated AMR rendering has made much progress towards real‐time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high‐quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high‐quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal.

Smoothed-particle hydrodynamics (SPH) is a mesh-free method used to simulate volumetric media in fluids, astrophysics, and solid mechanics. Visualizing these simulations is problematic because these datasets often contain millions, if not billions of particles carrying physical attributes and moving over time. Radial basis functions (RBFs) are used to model particles, and overlapping particles are interpolated to reconstruct a high-quality volumetric field; however, this interpolation process is expensive and makes interactive visualization difficult. Existing RBF interpolation schemes do not account for color-mapped attributes and are instead constrained to visualizing just the density field. To address these challenges, we exploit ray tracing cores in modern GPU architectures to accelerate scalar field reconstruction. We use a novel RBF interpolation scheme to integrate per-particle colors and densities, and leverage GPU-parallel tree construction and refitting to quickly update the tree as the simulation animates over time or when the user manipulates particle radii. We also propose a Hilbert reordering scheme to cluster particles together at the leaves of the tree to reduce tree memory consumption. Finally, we reduce the noise of volumetric shadows by adopting a spatially temporal blue noise sampling scheme. Our method can provide a more detailed and interactive view of these large, volumetric, time-series particle datasets than traditional methods, leading to new insights into these physics simulations.

Abstract 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.

Abstract 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.

Abstract A common way to render cell‐centric adaptive mesh refinement (AMR) data is to compute the dual mesh and visualize that with a standard unstructured element renderer. While the dual mesh provides a high‐quality interpolator, the memory requirements of the dual meshdata structureare significantly higher than those of the original grid, which prevents rendering very large data sets. We introduce a GPU‐friendly data structure and a clustering algorithm that allow for efficient AMR dual mesh rendering with a competitive memory footprint. Fundamentally, any off‐the‐shelf unstructured element renderer running on GPUs could be extended to support our data structure just by adding agridletelement type in addition to the standard tetrahedra, pyramids, wedges, and hexahedra supported by default. We integrated the data structure into a volumetric path tracer to compare it to various state‐of‐the‐art unstructured element sampling methods. We show that our data structure easily competes with these methods in terms of rendering performance, but is much more memory‐efficient.

In theory, efficient and high-quality rendering of unstructured data should greatly benefit from modern GPUs, but in practice, GPUs are often limited by the large amount of memory that large meshes require for element representation and for sample reconstruction acceleration structures. We describe a memory-optimized encoding for large unstructured meshes that efficiently encodes both the unstructured mesh and corresponding sample reconstruction acceleration structure, while still allowing for fast random-access sampling as required for rendering. We demonstrate that for large data our encoding allows for rendering even the 2.9 billion element Mars Lander on a single off-the-shelf GPU-and the largest 6.3 billion version on a pair of such GPUs.

Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling; and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations.

While k-d trees are known to be effective for spatial indexing of sparse 3-d volume data, full reconstruction, e.g. due to changes to the alpha transfer function during rendering, is usually a costly operation with this hierarchical data structure. In a recent publication we showed how to port a clever state of the art k-d tree construction algorithm to a multi-core CPU architecture and by means of thorough optimization we were able to obtain interactive reconstruction rates for moderately sized to large data sets. The construction scheme is based on maintaining partial summed-volume tables that fit in the L1 cache of the multi-core CPU and that allow for fast occupancy queries. In this work we propose a GPU implementation of the parallel k-d tree construction algorithm and compare it with the original multi-core CPU implementation. We conduct a thorough comparative study that outlines performance and scalability of our implementation.

While fast spatial index construction for triangle meshes has gained a lot of attention from the research community in recent years, fast tree construction algorithms for volume data are still rare and usually do not focus on real-time processing. We propose a linear time bounding volume hierarchy construction algorithm based on a popular method for surface ray tracing of triangle meshes that we adapt for direct volume rendering with sparse volumes. We aim at interactive to real-time construction rates and evaluate our algorithm using a GPU implementation.