PD-CO Match Replacement for Producer-Consumer Prefetch Training
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Solution Overview
Problem
Prefetching in data processing systems faces challenges with irregular and non-repeating access patterns, particularly in producer-consumer load relationships, where variability in data values leads to unpredictable address patterns, making it difficult to identify and schedule effective prefetches.
Innovation Solution
An apparatus and method that utilize prefetch generation circuitry to initiate producer and consumer prefetches, with training circuitry updating a training table of candidate producer-consumer relationships, match determining circuitry to assess PD-CO match conditions, and replacement control circuitry to prioritize relationships based on PD-CO-match-based replacement policies, ensuring retention of true dependencies for improved prefetch coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prefetching is used for producer-consumer load relationships, then cache hit rates improve for regular access patterns, but prefetch effectiveness deteriorates for irregular access patterns caused by data value variability
Solution Approach 1:
The system dynamically adapts the prefetching strategy based on the characteristics of access patterns. For regular patterns, traditional prefetching is used; for irregular patterns caused by producer-consumer relationships, the system switches to a data-driven approach that learns and predicts addresses based on observed data values, making the prefetching mechanism flexible and adaptive to different access pattern types
Solution Approach 2:
The system changes the fundamental parameter used for prefetch address generation. Instead of using fixed or stride-based address calculations, it uses actual data values returned from producer loads as the basis for predicting consumer load addresses. This parameter change enables the system to handle irregular access patterns where addresses are directly derived from variable data contents
2Reliability
If the training table stores all candidate producer-consumer relationships, then prefetch coverage improves, but the complexity and resource usage of the training table increases
Solution Approach 1:
The system extracts only the essential and most useful producer-consumer relationships from the full set of possible relationships. By using replacement policies that selectively retain relationships demonstrating consistent predictive performance, the system maintains a compact training table that contains only the most valuable prefetch candidates, removing redundant or less effective relationships
Solution Approach 2:
Different replacement policies are applied to different types of relationships in the training table. The system uses PD-CO-match-based replacement policies that evaluate relationships locally based on their specific characteristics, such as whether the producer data directly matches the consumer operand. This allows the system to prioritize retention of high-quality relationships while allowing lower-quality ones to be replaced
3Measurement precision
If replacement policies prioritize PD-CO matches, then accuracy of prefetch predictions improves, but the complexity of replacement control increases
Solution Approach 1:
The system performs preliminary evaluation of candidate relationships using the PD-CO match criterion before committing them to the training table. By checking whether producer data directly matches consumer operands in advance, the system pre-filters relationships that are likely to yield accurate predictions, reducing the need for complex post-hoc evaluation and simplifying the overall replacement control logic
Data Source
AI summary
Prefetch generation circuitry generates requests to prefetch data to a cache, where the prefetch generation circuitry is configured to initiate a producer prefetch to request return of producer data having a producer address and to initiate at least one consumer prefetch to request prefetching of consumer data to the cache, the consumer data having an address derived from the producer data returned in response to the producer prefetch. Training circuitry updates, based on executed load operations, a training table indicating candidate producer-consumer relationships being trained for use by the prefetch generation circuitry in generating the producer/consumer prefetches. Replacement control circuitry controls replacement of candidate producer-consumer relationships based on a producer-data-consumer-operand (PD-CO) match-based replacement policy criterion, which depends on whether a PD-CO match condition, indicative of the producer data for a producer load matching an address operand of a consumer load, is satisfied for existing/new candidate producer-consumer relationships.