Frame Interceptor for Automatic Rendering Optimization
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Solution Overview
Problem
Current methods for optimizing resource usage and reducing rendering time in image or video content generation are manual, time-consuming, and prone to errors, especially when dealing with large codebases and autogenerated code.
Innovation Solution
The implementation of a Frame Interceptor (FI) system that generates a resource dependency graph by intercepting API calls during the rendering process, allowing for automatic analysis and optimization of task sequences while maintaining dependencies between tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual optimization methods are used to analyze and modify code, then optimization expertise can be applied, but the process becomes extremely time-consuming and error-prone
Solution Approach 1:
The system enables self-service optimization by automatically analyzing application traces, generating optimized code modifications, and testing changes without requiring manual engineer intervention. The automated optimization engine processes code and generates improvements autonomously, eliminating the time-consuming manual analysis and modification process while maintaining reliability through systematic validation.
Solution Approach 2:
The patent replaces the manual mechanical process of code analysis and modification with an automated computational system. Instead of engineers manually examining API calls and modifying code, an automated optimization engine analyzes traces, generates optimized code, and performs testing, substituting human mechanical work with automated computational processes.
2Productivity
If manual code modification is performed on autogenerated code, then optimization opportunities can be identified, but the complexity and difficulty of understanding the code increases significantly
Solution Approach 1:
The patent introduces an intermediary optimization engine that sits between the autogenerated code and the manual modification process. This intermediary automatically analyzes the complex autogenerated code, identifies optimization opportunities, and generates modified code, shielding engineers from directly dealing with the complexity of autogenerated code while maintaining productivity through automated processing.
Solution Approach 2:
The system creates copies of application traces and code segments for automated analysis without modifying the original codebase. By working with copies and generated optimization proposals rather than directly editing complex autogenerated code, the system enables productivity gains while avoiding the pitfalls of manually navigating and modifying difficult-to-understand autogenerated code.
3Reliability
If extensive manual testing is performed to verify optimizations, then quality assurance can be achieved, but the cost and time investment becomes prohibitive for lower-priority operations
Solution Approach 1:
The patent applies partial testing by focusing validation efforts on the most critical optimization opportunities and using automated testing to perform sufficient validation without exhaustive manual testing. The system identifies and tests only the most significant optimizations, achieving adequate quality assurance while reducing resource consumption compared to extensive manual testing of all potential optimizations.
Solution Approach 2:
The automated optimization engine performs self-validation by automatically testing proposed optimizations against test cases and verifying that performance improvements are achieved without introducing bugs. This self-service validation reduces the need for expensive manual testing while maintaining reliability through systematic automated verification.
Data Source
AI summary
Approaches presented herein provide for the optimization of tasks performed for an operation such as the rendering of an image. A Frame Interceptor (FI) can generate a resource dependency graph (RDG) by intercepting API calls during the rendering process and determining dependencies. FI can analyze the RDG to identify potential optimizations, such as may correspond to reordering or parallel execution of certain tasks. FI can automatically test optimizations to determine whether sufficient improvement is obtained. This testing can be performed in real time by replacing the originally intercepted API calls with the newly ordered API calls generated by FI. FI can then issue a report that indicates information such as the changes made, the time taken to render the image, and potentially the fact that the images were determined to be identical.