Distributed Node Graph Processing for Interactive Workflows
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Solution Overview
Problem
Current desktop systems, including high-performance desktop computers, face limitations in processing resource-intensive node-based graphical software applications, such as those used for complex animation scenes, due to hardware constraints, and lack the capability for efficient multi-user collaboration.
Innovation Solution
A distributed computing system is implemented, where node graph processing is distributed across multiple servers and devices, using proxy nodes to manage data flow and caching, allowing for efficient processing and collaboration by leveraging the processing power of multiple computing devices and servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If resource-intensive computing tasks are processed on high-performance desktop computers, then processing capability is improved, but hardware cost and accessibility are worsened
Solution Approach 1:
The system segments the computing workload by separating the node graph application interface from the actual processing tasks. The application runs locally on various devices while processing-intensive operations are divided into discrete tasks that can be independently executed on remote servers, enabling both high-performance processing and broad device accessibility
Solution Approach 2:
A distributed computing system acts as an intermediary between local node graph applications and remote processing resources. The system mediates task submission, execution, and result retrieval, allowing users to access powerful computing capabilities through standard desktop interfaces without requiring local high-performance hardware
2Speed
If node graph processing is performed locally on desktop workstations, then processing speed is improved, but multi-user collaboration capability is worsened
Solution Approach 1:
The system merges local processing capabilities with remote distributed computing resources. Node graphs are processed locally when possible for immediate feedback, while collaborative tasks and heavy computations are combined with remote server processing, achieving both speed and collaboration capabilities simultaneously
Solution Approach 2:
The distributed computing system provides universal access to processing resources for multiple users simultaneously. The same infrastructure supports both fast local processing and collaborative multi-user workflows, making the system adaptable to different work modes without requiring separate systems
3Manufacturing precision
If complex node graphs are processed in real-time, then rendering quality is improved, but computational resource requirements are worsened
Solution Approach 1:
The system performs preliminary actions by pre-processing node graphs on remote servers before real-time rendering is needed. Computationally intensive operations such as ray tracing and physics simulations are executed in advance on distributed computing resources, reducing the real-time burden on local devices while maintaining high rendering quality
Data Source
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AI summary
A system and method for leveraging grid computing for node based interactive workflows is disclosed. A server system spawns a server process that receives node graph data and input attributes from a computing device, processes the data, caches the processed data, and transmits the processed data over a network to a computing device. The computing device runs a node graph application instance comprising proxy nodes configured to initiate a request to process node graph data at the server system. The server processed node graph data is displayed on the computing device. A plurality of computing devices may collaborate on a complex node graph where the node graph data processing is distributed over a plurality of servers.