GPU Shader Performance Analysis via Hardware Counter Correlation
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Solution Overview
Problem
Debugging and profiling GPU applications, especially those using TBDR architecture, are challenging due to lack of visibility into shader program execution times, making it difficult to quantify performance costs of individual shader programs.
Innovation Solution
A method to monitor and visualize the runtime performance of shader programs across multiple applications on a GPU by correlating execution with graphics hardware performance metrics, using stochastic sampling and visualization tools to identify performance issues and optimize shader code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional API trace replay methods are used to profile GPU performance, then performance metrics can be obtained, but the process requires multiple replays and cannot provide real-time visibility into shader execution
Solution Approach 1:
The system performs preliminary setup by installing hooks at the API level before execution, preparing the instrumentation infrastructure in advance so that actual shader execution timing can be captured in a single pass without replay requirements
Solution Approach 2:
The patent replaces the mechanical replay-based measurement system with a real-time sampling system that uses stochastic sampling and timeline correlation to capture shader execution timing during actual execution, eliminating the need for multiple replays
2Adaptability or versatility
If system-wide monitoring of multiple applications is implemented, then comprehensive performance data is obtained, but the complexity of correlating data from multiple sources increases
Solution Approach 1:
The system implements a universal timeline structure that can accommodate multiple applications and shader programs simultaneously, allowing the same correlation mechanism to handle diverse data sources without increasing complexity
Solution Approach 2:
The patent introduces an intermediary timeline structure that acts as a mediator between multiple application execution streams and the performance analysis tools, correlating all shader executions to a common timeline reference point and simplifying cross-application analysis
3Measurement precision
If detailed shader execution tracking is implemented, then performance issues can be precisely identified, but the overhead of monitoring and recording execution data increases
Solution Approach 1:
The system uses stochastic sampling to capture a representative subset of shader executions rather than tracking every single execution, providing sufficient precision for identifying performance issues while minimizing monitoring overhead
Solution Approach 2:
The patent implements periodic sampling of shader execution events at strategically chosen intervals, allowing detailed tracking when needed while reducing overhead during normal operation, with the ability to adjust sampling frequency based on performance conditions
Data Source
AI summary
Systems, methods, and computer readable media to analyze and improve the performance of applications utilizing graphics hardware are described. In general, techniques are disclosed to monitor the run-time performance of various shader programs from multiple applications executing concurrently on a graphics processing unit (GPU) and present a visualization of such performance to a user. More particularly, the GPU performance profiling comprises sampling data from multiple hardware performance counters and shader programs during the execution of the shader programs on the GPU. The hardware counters may be indicative of the status of various performance and/or architectural limitations of the GPU at a given moment in time. By time-correlating the execution of the various shader programs and the responses of the multiple hardware counters, a more instructive visualization may be presented to the user, which may be used, e.g., as an aid in debugging and/or profiling the applications executing on the GPU.