Nanomatrix: Scalable Construction of Crowded Biological Environments

TL;DR

Nanomatrix tackles the challenge of visualizing cellular mesoscale environments containing trillions of atoms, exceeding GPU memory by never fully storing the scene. It introduces a view-guided, on-the-fly construction pipeline paired with a two-stage RTX-based renderer, using Geometric Wang tiles for near-field geometry and image-based tiles for distant regions. Key contributions include the Geometric Wang tiles framework, a two-level scene partitioning with per-action cell caches, a memory-management strategy that merges construction and rendering, and a parallel rendering pipeline that achieves interactive frame rates on large RBC and SARS-CoV-2 setups. The work enables interactive visualization of red blood cells and viral particles at atomistic resolution, with practical implications for education and scientific communication.

Abstract

We present a novel method for the interactive construction and rendering of extremely large molecular scenes, capable of representing multiple biological cells in atomistic detail. Our method is tailored for scenes, which are procedurally constructed, based on a given set of building rules. Rendering of large scenes normally requires the entire scene available in-core, or alternatively, it requires out-of-core management to load data into the memory hierarchy as a part of the rendering loop. Instead of out-of-core memory management, we propose to procedurally generate the scene on-demand on the fly. The key idea is a positional- and view-dependent procedural scene-construction strategy, where only a fraction of the atomistic scene around the camera is available in the GPU memory at any given time. The atomistic detail is populated into a uniform-space partitioning using a grid that covers the entire scene. Most of the grid cells are not filled with geometry, only those are populated that are potentially seen by the camera. The atomistic detail is populated in a compute shader and its representation is connected with acceleration data structures for hardware ray-tracing of modern GPUs. Objects which are far away, where atomistic detail is not perceivable from a given viewpoint, are represented by a triangle mesh mapped with a seamless texture, generated from the rendering of geometry from atomistic detail. The algorithm consists of two pipelines, the construction-compute pipeline, and the rendering pipeline, which work together to render molecular scenes at an atomistic resolution far beyond the limit of the GPU memory containing trillions of atoms. We demonstrate our technique on multiple models of SARS-CoV-2 and the red blood cell.

Figures (14)

• Figure 1: Red Blood Cell model of diameter 8 µm containing approx. 250 million of hemoglobin molecules, with lipid-bilayer and membrane-bound proteins (approx. 1.2 trillion atoms in total) constructed and rendered with our view-guided two-level Nanomatrix approach. To the right of the cell there is a model of SARS-CoV-2. The rendering exploits hardware ray tracing, maintaining highly interactive framerates.
• Figure 2: Nanomatrix -- the scalable construction algorithm. Based on the input structure and building rules, in the preprocessing step rectangular geometry-based or image-based aperiodic patches are generated. For representing a volume of molecules, box tiles are generated. In real-time rendering, these tiles populate the scene with the appropriate level of detail depending on the distance from the camera. The scene is then rendered using RTX ray tracing so that the population and rendering fully utilize separate computational units on the graphics hardware.
• Figure 3: Illustration of a rectangle-based patch (l) and box-based patch (r) for SARS-CoV-2 (top row) and RBC (bottom row).
• Figure 4: Illustration of the construction process of a single geometric Wang tile. (a) four randomly chosen initial base patches inside rule-based geometry patch each of them is associated with a color. (b) the four colored base patches are further subdivided into four triangular sub-patches to be used for horizontal and vertical edges. (c) a Wang tile is created by combining four triangular sub-patches, collisions need to be resolved between the stitched triangular sub-patches. (d) the Wang tile has been further processed to resolve the collision.
• Figure 5: Illustration of the tiling algorithm that shows the application and visualization of the geometric Wang tiles of SARS-CoV-2 model. From left: four randomly chosen initial base patches inside rule-based geometry patch, 16 Geometric Wang Tiles, the population of the tiles using a tile recipe rendered with and without Wang Tiles encoding.