Out-of-Order Processor Schedule Repetition Detection
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Solution Overview
Problem
Out-of-order processors face challenges in efficiently detecting repetition in execution schedules for groups of instructions, which can lead to suboptimal performance and resource utilization, especially when instructions encounter varying latency events or branch mispredictions.
Innovation Solution
A method and apparatus that determine repetition in out-of-order execution schedules by comparing performance metrics across instances, using a counter system to evaluate similarity and detect schedule repetition, allowing for memoization and resource optimization without requiring explicit address comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit address comparisons are used to detect schedule repetition, then detection accuracy is improved, but hardware implementation cost and complexity increase
Solution Approach 1:
The patent extracts only the essential characteristics of execution schedules (performance metrics like execution time, resource usage, instruction completion patterns) rather than comparing complete address sequences. This extraction approach maintains detection accuracy by focusing on distinctive features while significantly reducing hardware complexity and implementation cost.
Solution Approach 2:
Instead of storing and comparing complete execution schedule records which would require substantial hardware resources, the patent creates simplified copies or representations of schedules based on key performance metrics. These metric-based representations enable efficient comparison with minimal hardware overhead while preserving the ability to detect repetitions accurately.
2Reliability
If performance metrics are compared across multiple instances, then schedule repetition detection is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent applies partial action by comparing only a selected subset of performance metrics rather than analyzing all possible execution parameters. By focusing on the most discriminative metrics (such as total execution time, critical path length, or specific resource utilization patterns), the system achieves reliable repetition detection with minimal processing time and computational overhead.
Solution Approach 2:
The system performs preliminary analysis of execution schedules by pre-computing and storing key performance metrics during normal execution. When repetition detection is needed, these pre-computed metrics are readily available for rapid comparison, eliminating the need for time-consuming re-analysis and reducing processing delays.
3Productivity
If out-of-order execution is used to improve instruction throughput, then processing performance is improved, but detection of schedule repetition becomes more difficult
Solution Approach 1:
The patent introduces performance metrics as an intermediary layer between the complex out-of-order execution machinery and the repetition detection logic. Instead of attempting to trace and compare the complex reordering of individual instructions, the system uses aggregate metrics that naturally capture the essence of execution patterns regardless of instruction reordering, thereby simplifying detection complexity while maintaining accuracy.
Data Source
AI summary
A method, apparatus, and CRM that detect repetition of an out-of-order execution schedule for a group of instructions executed by an out-of-order processor. Data indicative of at least one performance metric for an instance of execution of said group of instructions by the out-of-order processor is determined. The determined data are compared with previous data of the at least one performance metric for at least one previous instance of execution of the group of instructions by the out-of-order processor. Repetition of the out-of-order execution schedule is detected dependent on the comparison.