Octree Padding Sphere Engine for Scalable Simulation Workloads
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Solution Overview
Problem
Current simulation engines face challenges in scalability, workload distribution, and realistic representation of object visibility, particularly when dealing with large numbers of objects of varying sizes, leading to inefficiencies in computational resources and increased latency.
Innovation Solution
A highly scalable cluster computing engine that organizes simulated space using a unique octree structure with padding spheres, distributing objects among nodes to ensure equal workload and efficiently identifying interacting objects by determining overlaps with padding spheres rather than cubes, allowing for easy scaling and improved visibility representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single high-end dedicated server is used to provide larger and faster simulations, then simulation speed and capacity are improved, but cost and difficulty of scaling increase prohibitively
Solution Approach 1:
The simulation engine is segmented into multiple computing nodes that can be distributed across a cluster. Each node handles a portion of the simulation workload, allowing the system to scale by adding more nodes rather than requiring a single powerful server. This segmentation enables cost-effective scaling while maintaining simulation performance.
2Device complexity
If computation is distributed among personal computers used by gamers, then hardware cost is reduced, but data traffic increases rapidly and computing speeds vary causing latency
Solution Approach 1:
The system performs beforehand cushioning by pre-calculating spatial relationships and organizing objects into an octree structure with padding spheres. This preprocessing allows for efficient query operations during simulation, reducing the computational burden and latency during actual gameplay while still utilizing distributed personal computers.
3Ease of operation
If objects are assigned to nodes in equal numbers, then workload distribution is improved, but data traffic increases due to frequent interactions between nodes
Solution Approach 1:
The system applies local quality by using spatial partitioning with octree and padding spheres to determine which objects are likely to interact. This allows the system to focus communication and computation on local regions where interactions are probable, rather than uniformly distributing all objects across nodes. Objects that are spatially close are more likely to be assigned to the same or nearby nodes, reducing inter-node data traffic while maintaining balanced workloads.
Data Source
AI summary
A system for simulating objects in a three dimensional virtual space, comprising a host computing node coupled to a network, padding spheres defined in the virtual space, one padding sphere fully encasing each cube in an octree; a plurality of objects, each object tracked for position in the virtual space. A visibility sphere associated with each object is provided, and at least one display, displaying objects in the virtual space. One of the objects becoming a target object, a search determines objects to be rendered in the display, identifying all padding spheres overlapped at least partially by the visibility sphere of the target object as candidate padding spheres, and identifying objects within or overlapping the candidate padding spheres, determining a visibility ratio for each object of maximum dimension of the object divided by distance from the target object, any object having a visibility to be rendered in the display.