GPU Memory Trapping for Fast Graphics Operation Re-Execution
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Solution Overview
Problem
Existing methods for debugging graphics processing operations are time-consuming and require complex CPU analysis, making them inefficient and proprietary.
Innovation Solution
Implementing a memory trapping function at the graphics processing unit (GPU) to report memory accesses and modifications, allowing for efficient capture and storage of memory accessing commands and associated memory portions, enabling re-execution of graphics processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CPU analysis is used to capture and analyze each GPU graphics processing operation, then debugging capability is improved, but time consumption increases significantly
Solution Approach 1:
The patent extracts the debugging functionality from the CPU and implements it directly in the GPU through a trap mechanism. The GPU independently captures its own operational data and memory access patterns without requiring CPU intervention for analysis, thereby resolving the time consumption issue while maintaining debugging capability.
Solution Approach 2:
The GPU performs self-debugging by implementing trap mechanisms that automatically capture and analyze its own graphics processing operations. The GPU monitors its memory accesses and operational states independently, eliminating the need for external CPU analysis and significantly reducing time consumption.
2Measurement precision
If CPU analysis is used for each GPU operation, then operation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complex analysis functionality from the CPU system and embeds it within the GPU as a trap mechanism. This localizes the complexity within the GPU's existing architecture rather than requiring a complex CPU-GPU interaction system, maintaining analysis accuracy while reducing overall system complexity.
Solution Approach 2:
The GPU's trap mechanism serves multiple functions: it captures operational data, monitors memory accesses, and enables debugging all through a single integrated mechanism. This multi-functionality eliminates the need for separate CPU analysis tools and reduces system complexity while maintaining comprehensive operation analysis capability.
3Reliability
If proprietary CPU analysis methods are used, then debugging functionality is improved, but ease of operation deteriorates due to proprietary dependencies
Solution Approach 1:
The GPU implements self-debugging through trap mechanisms that are intrinsic to its architecture. This eliminates dependency on proprietary CPU analysis tools and methods, making the debugging functionality accessible and operable without proprietary restrictions while maintaining comprehensive debugging capability.
Data Source
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AI summary
Examples described herein generally relate to capturing and executing graphics processing operations. A memory trap function can be activated to cause a graphics processing unit (GPU) to report memory accesses in executing graphics processing operations. Based on activating the memory trap function and for each of a sequence of executed graphics processing operations executed by the GPU, a sequence of memory accessing commands and associated portions of memory modified based on executing the sequence of executed graphics processing operations can be received. Each of the sequence of multiple memory accessing commands and associated portions of memory can be stored and provided to the GPU to emulate re-executing of the sequence of executed graphics processing operations by the GPU.