The Concept of Cosmograph

Large-scale graph visualizations present a fundamental challenge. As networks grow to include hundreds of thousands or millions of nodes and links, computing their layout becomes increasingly difficult. Graph layout determines where each point appears on the canvas — without it, there’s no visualization.

Rendering large numbers of elements is equally problematic. While SVG works for small graphs, it fails beyond a few thousand animated objects. WebGL offers a solution for rendering, but can we also harness GPU power for layout computation? That’s exactly what Cosmograph does.

GPU-Accelerated Force Layout

Force-directed layouts are a cornerstone of network visualization. These physical simulations apply various forces to graph nodes: spring forces pull connected nodes together, many-body repulsion pushes nodes apart, and gravitational forces prevent disconnected components from drifting too far apart.

Most force layout implementations rely on CPU computation and hit performance walls around 100,000 nodes. GPU-based algorithms are rare because they’re significantly more complex to implement. The traditional many-body force approach doesn’t translate well to GPUs due to their poor performance with random memory access — you need many such operations when calculating forces between arbitrary node pairs whose data may be scattered across memory.

When visualizing large networks, you typically must choose between desktop tools that pre-compute layouts with optimized CPU algorithms, web-based tools that calculate layouts server-side and stream results via WebGL, or sophisticated command-line utilities.

Cosmograph takes a different approach. We’ve developed techniques that implement the entire force simulation on the GPU, enabling real-time computation directly in the browser.

Technology Stack

Cosmograph combines several technologies to achieve high-performance graph visualization:

• Apache Arrow provides efficient data transfer to the WebGL renderer. Its low-level, performance-optimized table format enables seamless handling of large datasets on the GPU.

• DuckDB Wasm handles SQL data operations entirely in the browser. Running in WebAssembly, it can filter, aggregate, and transform large datasets efficiently, providing responsive interaction even with massive graphs.

• Mosaic offers a declarative framework for interactive visualizations. Its reactive dataflow architecture updates visualizations efficiently based on user input, providing fast update experience due to the pre-aggregated data tables and caching.

• cosmos.gl is our GPU-accelerated force graph engine. It leverages GPU parallel processing to calculate force-directed layouts for graphs with hundreds of thousands or millions of nodes in seconds, freeing the CPU for other tasks.

This integrated stack enables Cosmograph to handle complex graph visualization tasks that were previously limited to specialized desktop software or required server-side processing pushing the boundaries of what’s possible with in-browser graph visualization. Whether you’re exploring social networks, biological pathways, or any other complex connected data, Cosmograph provides a fast, fluid, and insightful interface to make sense of it all.