Prefetching Stride Determination Circuitry for Data Processing Efficiency
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Solution Overview
Problem
Current prefetching techniques lack efficiency in determining optimal stride lengths for proactive data retrieval, often resulting in suboptimal performance and increased complexity during the training period, particularly due to the need to consider multiple candidate stride lengths and potential page boundary crossings.
Innovation Solution
A data processing apparatus that includes stride determination circuitry to calculate stride values based on frequency analysis during a training period, prefetch circuitry to proactively retrieve data using the most frequent stride values, and feedback mechanisms to adjust the training process and minimize page boundary crossings, allowing for efficient prefetching operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple candidate stride lengths are monitored during training period, then prefetching accuracy is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameter of stride length monitoring from tracking all possible candidate stride lengths to tracking only the frequency of the most recent stride length. This parameter change reduces the complexity of the training period while maintaining prefetching accuracy by focusing on the most relevant stride information.
Solution Approach 2:
The patent extracts only the essential information needed for prefetching - the frequency of the most recent stride length - and discards unnecessary candidate stride length data. This extraction approach reduces training complexity by eliminating redundant monitoring while preserving the key factor for accurate prefetching.
2Measurement precision
If training period monitors all candidate stride lengths, then optimal stride selection is improved, but loss of time increases
Solution Approach 1:
The patent skips the time-consuming process of monitoring and comparing multiple candidate stride lengths during the training period. Instead, it directly uses the frequency of the most recent stride length, rushing through the training process more quickly while still achieving optimal stride selection for prefetching operations.
Solution Approach 2:
The patent performs preliminary action by immediately using the most recent stride length frequency without waiting for extensive training data collection. This allows the system to start making informed prefetching decisions sooner, reducing the time loss associated with lengthy training periods.
3Adaptability or versatility
If prefetching operates across page boundaries, then data access flexibility is improved, but harmful factors increase
Solution Approach 1:
The patent implements feedback by monitoring whether prefetch operations cross page boundaries and using this information to adjust stride length selection. The system learns from past performance and modifies its prefetching behavior to minimize harmful page boundary crossings while maintaining data access flexibility through adaptive stride selection.
Data Source
AI summary
A variety of data processing apparatuses are provided in which stride determination circuitry determines a stride value as a difference between a current address and a previously received address. Stride storage circuitry stores an association between stride values determined by the stride determination circuitry and a frequency during a training period. Prefetch circuitry causes a further data value to be proactively retrieved from a further address. The further address is the current address modified by a stride value in the stride storage circuitry having a highest frequency during the training period. The variety of data processing apparatuses are directed towards improving efficiency by variously disregarding certain candidate stride values, considering additional further addresses for prefetching by using multiple stride values, using feedback to adjust the training process and compensating for page table boundaries.