Cache Prediction Using Statistical Distribution Matching
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Solution Overview
Problem
Existing methods for predicting data to be preloaded into cache memory are limited by the requirement for perfect temporal correlation between the observed and model access sequences, leading to inefficiencies in cache usage and increased execution times due to cache faults.
Innovation Solution
The method employs a statistical distribution approach to identify the most likely predicted delta, minimizing deviations between the model and observed distributions, allowing for accurate predictions even without perfect temporal correlation, and dynamically adapts to changes in access patterns using similarity metrics and prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If perfect temporal correlation between observed and model access sequences is required for prediction, then prediction reliability is improved, but prediction accuracy deteriorates when access patterns change or deviate from the model
Solution Approach 1:
The patent changes the prediction parameter from requiring perfect temporal correlation to allowing statistical distribution matching. Instead of demanding that the observed access sequence exactly matches the model sequence in temporal order, the invention compares statistical distributions (e.g., frequency of access patterns, inter-access intervals) and predicts when distributions match within a threshold, thereby improving accuracy for varying access patterns while maintaining reliability through statistical validation
Solution Approach 2:
The patent introduces dynamic adaptation by continuously updating the model access sequence based on recently executed instructions. The model is not static but evolves with program execution, allowing the prediction system to adapt to changing access patterns. This dynamic modeling maintains reliability through continuous validation while improving accuracy by reflecting actual runtime behavior
2Measurement precision
If the model access sequence is updated frequently to adapt to changing patterns, then prediction accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial updating by modifying only the necessary portions of the model access sequence based on recent executions. Instead of completely regenerating the model frequently, the system performs partial updates to relevant segments, reducing computational overhead while maintaining prediction accuracy for the most recent and relevant access patterns
Solution Approach 2:
The patent segments the access sequence analysis into manageable portions, comparing and updating specific segments of the model based on recent instruction executions. This segmentation allows efficient partial updates of only the relevant portions of the model, reducing overall computational complexity while maintaining accuracy where it matters most
3Measurement precision
If statistical distribution comparison is used instead of temporal correlation, then prediction accuracy is improved for varying patterns, but computational complexity increases
Solution Approach 1:
The patent extracts the essential statistical characteristics from access sequences (such as frequency distributions, inter-access interval statistics) and compares only these extracted features rather than performing complete sequence analysis. This extraction approach reduces computational complexity by focusing on the most discriminative statistical properties while maintaining prediction accuracy
Solution Approach 2:
The patent uses lightweight statistical counters and simple distribution comparisons instead of complex analytical models. These simple statistical representations are computationally inexpensive to calculate and update, providing accurate predictions with minimal computational overhead compared to sophisticated temporal analysis methods
Data Source
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AI summary
This method of predicting data to be preloaded includes: - the acquisition (110) of a statistical distribution, called "model", of the deltas of a model access sequence, - the construction (112) of a statistical distribution, called "observed", of the deltas of an observed access sequence, - the identification (114) in the observed statistical distribution, by comparing it with the model statistical distribution, of the most deficient class, that is to say the class for which the difference NoDSM - NoDSO is maximal, where NoDSM and NoDSO are the numbers of occurrences of this class deduced, respectively, from the model statistical distribution and the observed statistical distribution, - the provision (116) as a prediction of the data to be preloaded into the cache memory, of at least one predicted address where the data to be preloaded is contained, this predicted address being constructed from the most deficient class identified during step e).