Pre-rendering Task Distribution for Real-time Visualization
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Solution Overview
Problem
Mobile devices and other data visualization systems face resource constraints, particularly in CPU/GPU power and battery life, making real-time data visualization demanding and energy-intensive, limiting the types of applications that can be provided effectively.
Innovation Solution
A method is introduced to distribute pre-rendering processing tasks by mapping data processing tasks to a visualization processing model, receiving a processing cost model, and determining an execution plan to offload tasks from the frontend to a backend system, reducing the workload on the device and optimizing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time data visualization is performed continuously at 10-60 frames per second, then the visualization quality and responsiveness are improved, but the energy consumption and device resource usage increase significantly
Solution Approach 1:
The patent segments the data processing and visualization tasks by introducing a backend system that handles complex data processing, geometric primitive generation, and pre-rendering operations, while the frontend device focuses on displaying geometric primitives and final visualization. This segmentation allows the frontend to operate at lower energy consumption while maintaining real-time performance.
Solution Approach 2:
The backend system performs preliminary actions by pre-processing data, generating geometric primitives, and preparing visualization elements before transmitting them to the frontend. This pre-rendering approach reduces the computational burden on the frontend device during real-time operation, thereby reducing energy consumption while maintaining frame rate.
2Productivity
If complex data processing tasks are performed on the frontend device, then the real-time visualization capability is improved, but the device resource limitations are exceeded
Solution Approach 1:
The patent divides the system into backend and frontend components, with the backend handling complex data processing tasks including data reception, processing, geometric primitive generation, and transmission. The frontend device only needs to receive and display geometric primitives, significantly reducing its resource requirements while maintaining overall system productivity.
Solution Approach 2:
The backend system acts as an intermediary between the data source and the frontend device. It performs complex data processing, generates geometric primitives, and transmits them to the frontend, thereby shielding the frontend device from resource-intensive operations and enabling real-time visualization within device resource constraints.
3Use of energy by moving object
If all data processing tasks are performed on the backend system, then the frontend energy consumption is reduced, but the real-time rendering capability may be compromised
Solution Approach 1:
The backend system performs preliminary data processing and generates geometric primitives in advance, but the frontend device maintains real-time rendering capability by efficiently displaying these primitives and handling user interactions. This approach reduces frontend energy consumption while preserving real-time rendering performance.
Solution Approach 2:
The patent applies local quality by having the backend handle complex processing tasks that benefit from server-side resources, while the frontend focuses on local display and interaction tasks that require low latency. This division optimizes both energy consumption and rendering speed for their respective roles.
Data Source
AI summary
A computer-implemented method for distributing pre-rendering processing tasks includes: mapping data processing tasks in a real-time visualization to a visualization processing model, the real-time visualization involving data from a backend system to be rendered by a frontend system; receiving a processing cost model derived from processing-node information; determining, using the mapped data processing tasks and the models, an execution plan for the real-time visualization; and for each of the mapped data processing tasks, and according to the determined execution plan, assigning the backend system or the frontend system to perform the task.