GPU Self-Throttling via Timeline Callbacks
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Solution Overview
Problem
Traditional systems face challenges in predicting GPU frame rendering times, leading to underutilization of the GPU due to reliance on CPU timelines, which are often too late to correct issues in preceding frames, resulting in inefficient resource management.
Innovation Solution
The implementation of GPU self-throttling techniques that utilize timing information from the GPU timeline by inserting callbacks to determine elapsed time for unpredictable work, allowing for dynamic decision-making to throttle optional/non-critical frame processing based on configurable thresholds, thereby optimizing workload distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If CPU timeline is used to time rendering and take corrective action, then timing control is achieved, but the corrective action is too late to fix preceding frames and GPU utilization is reduced
Solution Approach 1:
The GPU timeline self-monitors its own processing time by inserting callbacks that measure elapsed time for unpredictable work. The GPU makes self-correction decisions about throttling optional work based on its own performance data, eliminating the need for CPU intervention and enabling real-time adaptive workload management.
Solution Approach 2:
Callbacks are inserted into the GPU timeline to measure actual processing time of unpredictable work. This feedback loop allows the system to compare expected versus actual processing time and dynamically adjust workload by throttling optional portions when necessary, creating a closed-loop control system.
2Reliability
If worst case scenario is assumed for frame processing, then timing safety is ensured, but GPU is under-utilized much of the time
Solution Approach 1:
The system dynamically adjusts the processing level based on actual measured performance. Instead of static worst-case assumptions, the GPU timeline adaptively determines whether to perform heavy or light processing by measuring elapsed time and comparing it against thresholds, optimizing GPU utilization while maintaining timing safety.
Solution Approach 2:
The system changes the processing parameters (heavy vs. light processing) based on measured elapsed time. When elapsed time exceeds the threshold, the system switches to light processing for optional portions; when under the threshold, heavy processing is performed, creating a parameter-adaptive system.
3Manufacturing precision
If heavy processing is performed for all frames, then processing quality is maintained, but GPU resources are wasted when unpredictable work exceeds timing thresholds
Solution Approach 1:
The system performs full heavy processing only when necessary (when elapsed time is under threshold). When elapsed time exceeds the threshold, it applies partial action by throttling optional portions of the workload, reducing GPU resource consumption while maintaining acceptable processing quality for critical portions.
Data Source
AI summary
Techniques for GPU self throttling are described. In one or more embodiments, timing information for GPU frame processing is obtained using a timeline for the GPU. This may occur by inserting callbacks into the GPU processing timeline. An elapsed time for unpredictable work that is inserted into the GPU workload is determined based on the obtained timing information. A decision is then made regarding whether to “throttle” designated optional/non-critical portions of the work for a frame based on the amount of elapsed time. In one approach the elapsed time is compared to a configurable timing threshold. If the elapsed time exceeds the threshold, work is throttled by performing light or no processing for one or more optional portions of a frame. If the elapsed time is less than the threshold, heavy processing (e.g., “normal” work) is performed for the frame.