Iterative Process Data Caching via Feedback Loop
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Solution Overview
Problem
Current data caching systems lack efficient prefetch mechanisms that dynamically adapt to changing data access patterns, leading to suboptimal cache performance and increased processing time due to inefficient prefetch depth settings.
Innovation Solution
A computer-implemented method that retrieves updated metadata on cache misses, modifies the data stream control register settings, and sets hints at the data cache block touch module to optimize prefetch operations, utilizing iterative feedback from cache miss statistical data to dynamically adjust prefetch depth and improve cache hit rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prefetch depth is increased to improve cache hit rates, then more data is loaded into cache, but processing time increases due to excessive data fetching
Solution Approach 1:
The patent implements dynamic adjustment of prefetch depth based on runtime cache miss statistics. The system continuously monitors cache performance and adapts the prefetch depth parameter during execution, transitioning from static to dynamic control. This resolves the contradiction by allowing the system to increase prefetch depth when cache hits are needed while reducing it when excessive fetching occurs, optimizing both cache hit rate and processing time
Solution Approach 2:
The patent employs feedback mechanisms where cache miss statistics from previous executions are retrieved and used to modify prefetch settings for subsequent executions. The system measures actual cache performance, feeds this information back into the control logic, and adjusts prefetch depth accordingly. This closed-loop feedback system enables the resolution of the contradiction by continuously optimizing prefetch parameters based on actual performance data
2Productivity
If prefetch operations are optimized dynamically, then cache performance is improved, but system complexity increases due to additional control mechanisms
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically monitors its own cache performance and adjusts prefetch parameters without external intervention. The metadata storage and retrieval mechanisms enable the system to service its own optimization needs by automatically capturing cache miss statistics and applying them to subsequent prefetch operations. This reduces the perceived complexity by making the system self-regulating
Solution Approach 2:
The patent optimizes cache performance through parameter changes in the prefetch mechanism, specifically adjusting prefetch depth based on cache miss statistics. By modifying operational parameters rather than fundamental system architecture, the patent improves productivity while minimizing the increase in system complexity. The changes are confined to controllable parameters within existing structures
3Adaptability or versatility
If iterative feedback is implemented to adjust prefetch settings, then adaptive prefetch functionality is achieved, but metadata management complexity increases
Solution Approach 1:
The patent implements iterative feedback by retrieving cache miss statistics from metadata after process execution and using this information to modify prefetch settings for subsequent executions. The feedback loop captures performance data, processes it through control logic, and applies adjustments to prefetch parameters. This enables adaptive prefetch functionality while managing metadata complexity through structured storage and retrieval mechanisms
Solution Approach 2:
The patent performs preliminary actions by capturing and storing cache miss statistics in metadata during process execution. This preliminary data collection enables subsequent adjustments to prefetch settings without requiring complex real-time analysis during critical execution phases. The preliminary action of data capture simplifies the overall metadata management complexity
Data Source
AI summary
Systems and methods for improved process caching through iterative feedback are disclosed. In embodiments, a computer implemented method comprises retrieving updated metadata of a process to be executed, wherein the updated metadata includes information regarding cache misses from a prior execution of the process; automatically modifying a setting of a data stream control register based on the updated metadata; automatically setting a hint at a data cache block touch module; performing an initial execution of the process after the steps of retrieving the updated metadata, automatically modifying the setting of the data stream control register, and automatically setting the hint at the data cache block touch module; and modifying the updated metadata of the process after the execution of the process based on cache miss statistical data gathered during the execution of the process, to produce newly updated metadata.