High-Performance 3D Graph Visualisation Framework

In this paper we aim to develop a next-generation browser-based 3D graph visualisation framework capable of efficiently rendering and manipulating graphs containing millions of nodes and edges. The primary innovation lies in extending zero-copy memory architectures from server to client, utilising WebAssembly (Wasm) compiled from Rust to achieve near-native performance while maintaining memory efficiency.

1. The Problem

Current JavaScript-based 3D visualisation frameworks face fundamental limitations when dealing with massive data structures:

• Memory Overhead: JavaScript’s object model and garbage collection introduce significant memory overhead and unpredictable performance characteristics
• Serialisation Bottlenecks: Traditional approaches require expensive serialisation/deserialisation steps between network, application, and GPU memory
• Limited Concurrency: JavaScript’s single-threaded nature constrains parallel processing of graph algorithms
• GPU Memory Management: High-level abstractions prevent efficient GPU memory utilisation

These limitations become critical when visualising big data structures with millions of datapoints in collaborative, real-time environments.

2. Graph Structures as a Universal Data Representation

Whilst this research focuses on graph visualisation, the choice is deliberate and strategic. Graph structures provide a universal framework for representing semi-structured data and the inherent relationships within virtually any domain. From social networks to molecular structures, from knowledge bases to infrastructure systems, graphs offer the flexibility to model complex interconnected data whilst maintaining computational tractability.

The techniques developed for efficient graph visualisation directly translate to other data representations:

• Hierarchical data can be represented as trees (a subset of graphs)
• Tabular data becomes a bipartite graph linking records to attributes
• Spatial data maps naturally to geometric graphs with position attributes
• Time-series data forms directed graphs with temporal edges

By solving the performance challenges for graph visualisation, we establish patterns and architectures applicable to the broader challenge of big data visualisation in web environments.

2.1 Use Cases:

• Digital Twins: Real-time visualisation of sensor networks overlaid on 3D models.
• Gaming: Massively multiplayer game world propogations.
• Collaborative Analytics: Multi-user data vis environments where teams manipulate the same visualisations.
• Intelligence Networks: Flow of information and derived intelligence through a network.
• Finance: Real-time market and trading visualisations.
• Telecommunications: Live network topology displaying data flows and hotspots across global infrastructure.
• Pharmaceuticals: Interactive protein-protein interaction networks where researchers can explore in 3D space.
• Energy: Power grid visualisation showing electricity flow and fault propagation.
• Social Media: User interaction networks displaying viral content spread patterns.
• Transportation: City-wide traffic flow visualisation.
• Cybersecurity: Enterprise network security visualisation displaying attack paths.
• Manufacturing: Supply chain visualisation showing resource flows from suppliers to production to distribution.

3. Research Objectives

3.1 Secondary Objectives

The core objective of this research is to develop a zero-copy architecture that seamlessly extends from server-side delta propagation through to the browser’s rendering pipeline. This architecture must achieve interactive frame rates of 60+ FPS whilst handling graphs containing between one and ten million nodes. To accomplish this, we will minimise memory footprint through carefully designed data structures and efficient GPU memory management. Furthermore, the system must support real-time collaborative editing with sub-100ms latency for remote changes, ensuring that multiple users can work with the same massive dataset simultaneously without perceptible delays.

1. Zero-copy from Packet to GPU
2. 60+ FPS with 1-10M nodes
3. Minimal memory footprint
4. Sub-100ms collaborative updates

3.2 Secondary Objectives

Beyond the core performance goals, the research aims to create a modular architecture that supports different rendering strategies and graph algorithms, allowing researchers to experiment with various approaches to visualisation and analysis. We will establish comprehensive performance benchmarks and evaluation methodologies to quantify improvements and identify bottlenecks. Additionally, the project will develop specialised tooling for profiling and optimising large-scale graph visualisations, enabling developers to understand and improve performance characteristics of their specific use cases.

1. Modular, extensible architecture
2. Comprehensive benchmarking suite
3. Performance profiling tools

4. Technical Approach

4.1 Core Architecture

┌─────────────────────────────────────────────────────────┐
│       Backend Server│
│  ┌─────────────┐  ┌──────────────┐  ┌───────────────┐  │
│  │ Graph State │  │ Delta Engine │  │ Zero-Copy Pub │  │
│  │   (Rust)    │  │    (Rust)    │  │     (Rust)    │  │
│  └─────────────┘  └──────────────┘  └───────────────┘  │
└─────────────────────────────────────────────────────────┘
							│
					Binary Delta Stream
							│
┌─────────────────────────────────────────────────────────┐
│       Browser Client│
│  ┌─────────────┐  ┌──────────────┐  ┌───────────────┐  │
│  │ Wasm Module │  │ Shared Memory│  │  WebGPU       │  │
│  │   (Rust)    │  │    Buffer    │  │  Renderer     │  │
│  └─────────────┘  └──────────────┘  └───────────────┘  │
└─────────────────────────────────────────────────────────┘

4.2 Key Technologies

WebAssembly (Wasm) + Rust The framework will compile Rust code to WebAssembly to achieve near-native performance in the browser. Rust’s zero-copy abstractions and memory safety guarantees eliminate the overhead of garbage collection whilst providing fine-grained control over memory layout. This approach enables direct memory management patterns that mirror the server-side architecture, maintaining consistency across the entire system.

WebGPU WebGPU provides the low-level GPU control necessary for efficient rendering of millions of primitives. The API supports advanced features such as compute shaders for GPU-based graph algorithms, instanced rendering for node drawing, and direct buffer management that aligns with our zero-copy philosophy. Unlike WebGL, WebGPU’s modern architecture allows for explicit synchronisation and memory barriers, crucial for maintaining performance with dynamic data.

Binary Delta Protocol The communication protocol will employ a custom binary format optimised specifically for graph delta propagation. This format will map directly to in-memory graph structures, enabling true zero-copy operation from network buffer to application memory. The delta-based approach ensures that only changes are transmitted, reducing bandwidth requirements and enabling real-time collaboration at scale.

SharedArrayBuffer SharedArrayBuffer enables true shared memory between WebAssembly modules and JavaScript contexts, facilitating zero-copy data transfer between different parts of the application. Combined with atomic operations for synchronisation, this technology allows the rendering thread to directly access graph data being modified by the WebAssembly module without expensive memory copies or serialisation steps.

4.3 Memory Architecture

// Proposed zero-copy graph structure
#[repr(C)]
struct GraphNode {
	position: [f32; 3],
	colour: u32,
	metadata_offset: u32,
	edge_list_offset: u32,
}

#[repr(C)]
struct GraphEdge {
	source: u32,
	target: u32,
	weight: f32,
	attributes: u32,
}

// Direct GPU buffer mapping
struct GraphBuffer {
	nodes: Vec<GraphNode>,      // Maps to GPU vertex buffer
	edges: Vec<GraphEdge>,      // Maps to GPU index buffer
	spatial_index: Octree,      // For efficient culling
}

5. Research Plan

Phase 1: Proof of Concept (Month 1)

• Implement basic Wasm module for graph
• Establish WebGPU rendering plane
• Demonstrate zero-copy delta ingestion
• Develop basic performance profiling tools
• Target: 10M nodes at 60 FPS

Phase 2: Scale Testing (Month 2)

• Implement spatial indexing and LOD systems
• Optimise GPU matrix operations to memory layout
• Develop interactive framework
• Target: 100M nodes at 60 FPS

Phase 3: Advanced Features (Month 3)

• Develop collaboration framework
• Develop advanced performance profiling tools
• Target: 100M nodes with dynamic updates

Phase 4: Demo MVP

• Global Satellite Tracking Network
• Digital Twin for a Large Mining Outfit
• Multi-user Collaborative Data Visualisation

6. Evaluation Metrics

Performance Metrics

• Frame rate at various graph sizes (1K to 10M nodes)
• Memory usage (CPU and GPU)
• Delta application latency
• Initial load time

Quality Metrics

• Visual quality (anti-aliasing, LOD transitions)
• Interaction responsiveness
• Network bandwidth usage
• Power consumption

7. Expected Outcomes

1. Open-source framework for high-performance graph visualisation
2. Research publications on zero-copy architectures for web applications
3. Performance benchmarks establishing new standards for web-based visualisation
4. Design patterns for Wasm/WebGPU applications

8. Potential Impact

The development of a zero-copy, high-performance graph visualisation framework addresses specific technical barriers that currently limit how we interact with complex relational data:

Enabling True Real-time Collaboration on Complex Data Current visualisation tools struggle with simultaneous multi-user interaction on large datasets due to synchronisation overhead and rendering bottlenecks. Our zero-copy architecture enables genuinely concurrent exploration where multiple analysts can manipulate million-node graphs without the lag that makes current tools impractical for collaborative analysis. In intelligence applications, this means distributed teams can explore relationship networks together, with each analyst’s discoveries immediately visible to others. For digital twin applications, operations teams across different locations can interact with the same live facility model, seeing sensor updates and system changes propagate instantly.

Scaling Interactive Analysis Beyond Current Limits Existing tools typically require pre-aggregation or sampling when dealing with graphs beyond 100,000 nodes, losing critical detail in the process. By achieving 60+ FPS with millions of nodes, analysts can work with complete datasets rather than simplified representations. In pharmaceutical research, this means exploring entire protein interaction networks without filtering, potentially revealing drug interactions that would be hidden in aggregated views. For financial networks, compliance teams could trace transaction paths through complete trading networks rather than working with daily summaries.

Bridging Simulation and Visualisation The direct memory architecture enables tight integration between computational models and visual representation. In massively multiplayer games, this allows game state calculations to feed directly into the rendering pipeline, supporting unprecedented numbers of simultaneous players. For supply chain digital twins, simulations can run alongside visualisation with changes immediately reflected, enabling what-if analysis on complete network models rather than simplified abstractions.

Democratising Large-scale Graph Analysis By leveraging client-side GPU capabilities through WebAssembly and WebGPU, the framework makes large-scale graph visualisation accessible without specialised hardware or software installation. Research institutions without supercomputing facilities could analyse genomic networks directly in browsers. Small financial firms could perform network risk analysis previously limited to institutions with dedicated visualisation clusters.

Establishing New Patterns for Web Application Architecture The zero-copy approach from network to GPU demonstrates that web applications can achieve performance previously exclusive to native applications. This architectural pattern - using WebAssembly for memory management, SharedArrayBuffer for zero-copy transfers, and WebGPU for direct GPU control - provides a blueprint for other performance-critical web applications. The techniques developed here will influence how future web applications handle large-scale data, from scientific visualisation to real-time monitoring systems.

Unlocking Graph-based Approaches to Data Analysis Many datasets have inherent relational structures that are currently analysed using traditional statistical methods due to performance limitations of graph visualisation. Fast, interactive exploration of million-node graphs makes graph-based analysis practical for new domains. Urban planners could model entire city infrastructures as interconnected networks. Epidemiologists could trace disease spread through complete population contact networks rather than statistical models.

The framework’s impact extends beyond mere performance improvements. By removing the technical barriers to working with large-scale relational data, it enables new analytical approaches and collaborative workflows that were previously impractical. The zero-copy architecture ensures that insights can be shared instantly across global teams, while the browser-based approach removes infrastructure barriers that limit access to advanced visualisation capabilities.

9. Technical Risks and Mitigation

10. Funding

This project addresses critical limitations in current web technologies that prevent effective visualisation of large-scale data. Success would:

1. Enable new classes of web applications previously impossible
2. Reduce infrastructure costs through client-side processing
3. Advance the state of web platform capabilities
4. Create reusable patterns for high-performance web applications

The research outputs would benefit multiple domains including scientific visualisation, social network analysis, infrastructure monitoring, and collaborative design tools.

Conclusion

This research project represents a significant advance in web-based data visualisation, pushing the boundaries of what’s possible in browsers. By combining cutting-edge web technologies with systems-level programming, we can achieve performance levels previously reserved for native applications whilst maintaining the accessibility and reach of web platforms.

The zero-copy architecture philosophy, extended from server to client, represents a paradigm shift in how we think about web application architecture, with implications beyond visualisation into general high-performance web computing.