Reinforcement Learning Job Scheduling for Frame Rendering
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Solution Overview
Problem
Scheduling of processing jobs, particularly graphical shader jobs, faces a combinatorial explosion of potential solutions, leading to high time and computational resource usage, and existing heuristic approaches fail to optimize scheduling effectively.
Innovation Solution
Reinforcement learning is employed to efficiently schedule jobs by selecting reward metrics such as rendering time, state transitions, and compute barriers, and reordering execution jobs to generate a modified sequence that improves frame rendering performance without exhaustive searches or suboptimal heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used to schedule execution jobs, then frame rendering performance is improved and computational resources are reduced, but the complexity of the scheduling system increases
Solution Approach 1:
A reinforcement learning agent is introduced as an intermediary between the job scheduler and the execution system. The agent learns optimal scheduling policies through iterative interactions, selecting reward metrics and generating modified job sequences without requiring exhaustive search or complex heuristic algorithms, thus improving rendering performance while managing system complexity
Solution Approach 2:
The system dynamically changes scheduling parameters by selecting different reward metrics (e.g., rendering time, state transitions, compute barriers) based on application policies and previous frame performance. This allows the scheduler to adapt to different rendering scenarios and optimize job sequences without requiring a completely different scheduling approach for each case
2Reliability
If exhaustive search is used to optimize job scheduling, then optimal scheduling solutions can be found, but significant time and computational resources are consumed
Solution Approach 1:
The reinforcement learning agent uses feedback from evaluating modified job sequences against selected reward metrics to learn optimal scheduling policies. Instead of exhaustively searching all possible schedules, the agent iteratively improves its scheduling decisions based on performance feedback, achieving near-optimal results with significantly reduced computation time
Solution Approach 2:
The system performs preliminary actions by pre-selecting relevant reward metrics based on application policies and previous frame performance before generating modified job sequences. This preliminary selection guides the reinforcement learning process to focus on the most important optimization criteria, avoiding the need to evaluate all possible scheduling variations
Data Source
AI summary
Systems, methods, and techniques utilize reinforcement learning to efficiently schedule a sequence of jobs for execution by one or more processing threads. A first sequence of execution jobs associated with rendering a target frame of a sequence of frames is received. One or more reward metrics related to rendering the target frame are selected. A modified sequence of execution jobs for rendering the target frame is generated, such as by reordering the first sequence of execution jobs. The modified sequence is evaluated with respect to the selected reward metric(s); and rendering the target frame is initiated based at least in part on the evaluating of the modified sequence with respect to the one or more selected reward metric(s).