Prefetch Pattern Back-Off Scheduling for Balanced Training
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Solution Overview
Problem
Existing prefetch training systems often prioritize frequently observed prefetch patterns, neglecting less frequent yet potentially useful patterns, leading to inefficient distribution of training resources.
Innovation Solution
Implementing prefetch patterns with back-off information to manage training frequency, ensuring infrequently observed patterns receive attention by adjusting the back-off period based on training frequency, thereby distributing training resources more evenly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frequently observed prefetch patterns are prioritized for training, then training efficiency for common patterns is improved, but less frequent yet potentially useful patterns are neglected
Solution Approach 1:
The patent implements a periodic training mechanism where prefetch patterns are selected for training based on a back-off counter that increments over time. This periodic selection ensures that frequently observed patterns are trained less often while less frequent patterns receive training opportunities, balancing training distribution across all pattern types
Solution Approach 2:
The system uses feedback from pattern observation frequency to dynamically adjust the back-off counter values. Patterns that are observed more frequently receive higher back-off values, reducing their training frequency, while less frequent patterns receive lower back-off values, increasing their chances of being selected for training
2Adaptability or versatility
If all prefetch patterns are trained equally, then comprehensive pattern coverage is achieved, but training resources are wasted on frequently observed patterns
Solution Approach 1:
The patent changes the parameter of training frequency by introducing a back-off counter that varies for each prefetch pattern based on its observation frequency. This parameter change allows the system to adapt training resource allocation dynamically, reducing training frequency for common patterns while maintaining training opportunities for less frequent patterns
3Reliability
If frequently observed patterns are trained repeatedly, then training convergence for common patterns is accelerated, but training resources are monopolized by these patterns
Solution Approach 1:
The periodic training mechanism with back-off counters ensures that no single pattern monopolizes training resources. By implementing time-based periodic selection with varying frequencies, the system maintains reliable training convergence for common patterns while ensuring equitable distribution of training resources across all patterns
Data Source
AI summary
There is provided an apparatus, a system, a chip containing product, a method, and a computer-readable medium. The apparatus comprises pattern storage circuitry to store information indicative of a plurality of prefetch patterns, each prefetch pattern indicating a trigger access request and comprising pattern information associated with the trigger access request. The pattern information is indicative of one or more addresses to be used for generation of prefetch requests. The apparatus also comprises control circuitry responsive to an observation of the trigger access request indicated in a prefetch pattern to determine whether the prefetch pattern is selected for training by prefetch training circuitry. Each of the plurality of prefetch patterns comprises back-off information indicating a back-off period during which the prefetch pattern is to be overlooked for the training. The control circuitry is responsive to the prefetch pattern being selected for the training to update the back-off information.