Task Graph Distribution for Cloud Gaming Latency
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Solution Overview
Problem
Current technologies face challenges in optimizing the distribution and execution of digital content across networks of conventional processors and emerging heterogeneous processing units, particularly in cloud gaming, where efficient resource sharing and parallel processing are needed.
Innovation Solution
The technique involves generating and executing a task graph, which is a collection of execution nodes that define data transformations, allowing for distributed computing and rendering of digital content without requiring the producer computer to have the necessary hardware, enabling efficient distribution and local realization on consumer computers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital content is distributed across multiple computers for parallel processing, then processing capacity and resource sharing are improved, but system complexity and communication overhead increase
Solution Approach 1:
The patent segments the digital content processing into discrete task graphs that can be independently distributed and executed across multiple computers. Each task graph represents a self-contained unit of work that can be processed in parallel, enabling the system to scale processing capacity by adding more computing nodes without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces task graphs as an intermediary data structure that mediates between the distributed computing resources and the digital content processing requirements. This intermediary enables efficient coordination and communication between producer and consumer computers, reducing the overhead associated with direct peer-to-peer communication in distributed systems.
2Speed
If task graphs are sent as binary streams in non-serial fashion, then transmission efficiency is improved, but data integrity and reconstruction complexity increase
Solution Approach 1:
The patent applies preliminary action by organizing and structuring the task graph data into a standardized format before transmission. This pre-processing ensures that the binary stream can be efficiently transmitted in non-serial fashion while maintaining data integrity, as the receiver can reliably reconstruct the original task graph structure from the pre-organized binary data.
3Adaptability or versatility
If producer computer does not require specific hardware, then platform compatibility is improved, but execution precision and hardware optimization are reduced
Solution Approach 1:
The patent uses copying by creating a task graph representation that captures the essential processing logic and data transformations without being tied to specific hardware implementations. This abstract representation can be copied and executed on different hardware platforms, maintaining platform compatibility while preserving execution precision through the detailed task graph specification.
Data Source
AI summary
Techniques are disclosed that can obtain a description of intended digital content data in a producer computer, create a task graph of the intended digital content data, send the task graph to a consumer computer over a bus, execute the task graph locally in the consumer computer to realize an approximation of the intended digital content data, and render the approximation of the intended digital content data on a local display or other appropriate output device.