Dynamic Prefetch Offset Selection for Data Processing Systems
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Solution Overview
Problem
Existing data processing systems face challenges in efficiently selecting prefetch offsets, leading to latency issues and thrashing due to unpredictable data access patterns, as they often rely on a default order that is not optimized for changing access patterns.
Innovation Solution
An apparatus and method that includes candidate offset storage circuitry to store a list of candidate offset values, prefetch circuitry to generate prefetch addresses, and prefetch training circuitry to dynamically modify the order of these values based on recent requests, prioritizing subset offset values that have a mathematical or statistical relationship with the current offset, thereby selecting a new offset to improve prefetch timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a default order of candidate offset values is used for prefetching, then the system maintains simplicity in offset selection, but it fails to adapt to changing data access patterns causing increased latency and thrashing
Solution Approach 1:
The patent implements a dynamic offset selection mechanism where the prefetch training circuitry continuously monitors recent demand requests and reorders candidate offset values based on their effectiveness. The system transitions from a static default order to a dynamic order that adapts to changing access patterns, allowing the prefetch mechanism to respond to temporal variations in data access behavior while maintaining operational simplicity through automated reordering.
Solution Approach 2:
The patent employs feedback through the prefetch training circuitry that monitors the effectiveness of prefetch operations by comparing predicted offsets against actual demand requests. This feedback loop identifies which candidate offset values are most effective for recent access patterns and reorders them accordingly, enabling the system to learn from past performance and continuously improve prefetch accuracy without increasing operational complexity.
2Productivity
If prefetch requests are issued with fixed offset values, then the prefetch mechanism is simple to implement, but it results in wasted prefetch requests and increased latency when access patterns change
Solution Approach 1:
The patent implements preliminary action by proactively reordering candidate offset values based on analysis of recent demand requests before new prefetch operations are initiated. The prefetch training circuitry continuously updates the ordering of candidate offsets in anticipation of future access patterns, ensuring that when prefetch requests are issued, they use the most current and effective offset values available, thereby reducing wasted prefetches and latency.
3Speed
If the system monitors all candidate offset values equally, then the selection process is straightforward, but it slows down the identification of optimal offsets due to unnecessary comparisons
Solution Approach 1:
The patent applies segmentation by dividing the candidate offset values into different priority groups based on their effectiveness for recent access patterns. The prefetch training circuitry segments the candidate offsets and reorders them with the most effective offsets appearing first in the prioritized list. This segmentation allows the system to focus comparisons on the most promising candidates first, significantly speeding up optimal offset identification while maintaining manageable complexity through systematic grouping.
Data Source
AI summary
There is provided an apparatus, medium and method. The apparatus comprises candidate offset storage circuitry to store a list comprising a plurality of candidate offset values having a default order, and prefetch circuitry to generate prefetch addresses by modifying a base address using a current offset, and to issue prefetch requests to cause information beginning at a corresponding prefetch address to be prefetched into the storage structure in anticipation of a demand request for that information. The apparatus further comprises prefetch training circuitry to select a new offset from the list of candidate offset values through comparison of the plurality of candidate offset values against data indicative of recent requests. The prefetch training circuitry is configured to identify a subset of the candidate offset values based on the current offset and to dynamically modify the default order to increase priority of the subset.