Prefetch Engine Stride Pattern Detection and Conflict Resolution
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Solution Overview
Problem
In processor systems, multiple prefetch engines using a shared structure to store stride patterns often experience conflicts, leading to incorrect prefetching of data due to overlapping stride patterns, resulting in inefficient data retrieval from main memory.
Innovation Solution
A technique is implemented to detect stride patterns in address streams by checking if the sum of current and previous strides are equal, using a confidence counter to determine when to prefetch data, and dynamically allocating or reassigning prefetch engines within a memory prefetch table to manage address streams effectively, ensuring accurate prefetching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a shared structure is used to store stride patterns for multiple prefetch engines, then the amount of resources required is reduced, but stride pattern conflicts occur leading to incorrect prefetching
Solution Approach 1:
The patent divides the shared stride pattern storage into separate, dedicated storage structures for each prefetch engine. Each prefetch engine maintains its own stride pattern table, eliminating conflicts between engines while preserving resource efficiency through localized rather than globally-shared storage.
Solution Approach 2:
The patent introduces an intermediary arbitration mechanism that manages access to shared stride pattern storage when conflicts are detected. This mediator coordinates between multiple prefetch engines, resolving conflicts and ensuring correct prefetching while allowing the system to use shared storage structures.
2Productivity
If multiple prefetch engines independently track address streams, then data prefetching capacity is increased, but conflicts in shared stride pattern storage lead to incorrect addresses
Solution Approach 1:
The patent segments the stride pattern storage into engine-specific tables, allowing each prefetch engine to independently track and predict addresses without interference from other engines. This maintains high prefetching capacity while ensuring address prediction accuracy through isolated storage.
Solution Approach 2:
The patent implements feedback mechanisms where each prefetch engine monitors its own stride pattern validity and adjusts its predictions based on detected conflicts or invalid patterns. This feedback loop maintains address prediction accuracy even when multiple engines operate concurrently on shared data structures.
Data Source
AI summary
A processor system (100) includes a central processing unit (102) and a prefetch engine (110). The prefetch engine (110) is coupled to the central processing unit (102). The prefetch engine (110) is configured to detect, when data associated with the central processing unit (102) is read from a memory (114), a stride pattern in an address stream based upon whether sums of a current stride and a previous stride are equal for a number of consecutive reads. The prefetch engine (110) is also configured to prefetch, for the central processing unit (102), data from the memory (114) based on the detected stride pattern.