Load-Store Dependency Predictor for Out-of-Order Execution
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Solution Overview
Problem
Superscalar processors face performance degradation due to incorrect execution of load memory operations before dependent store operations, leading to unnecessary flushing and redirection, which reduces memory level parallelism and processor efficiency.
Innovation Solution
A load-store dependency predictor is implemented, which includes a dispatch unit, reservation station, and a predictor table to detect and manage dependencies between younger loads and older stores, ensuring that loads are not scheduled out of order until corresponding stores have been scheduled, using a counter field to represent the strength of data dependencies and manage table entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the processor schedules load memory operations prior to older store memory operations to maximize out-of-order execution, then instruction throughput is improved, but ordering violations occur when loads execute before dependent stores
Solution Approach 1:
The predictor table proactively establishes load-store dependencies before execution occurs. When a store operation is decoded, the predictor table is searched for potential dependent loads, and dependencies are preemptively established based on address matching, preventing out-of-order execution violations before they happen.
Solution Approach 2:
The system uses feedback from actual execution outcomes to refine dependency predictions. When ordering violations are detected or when loads correctly wait for stores, the predictor table updates its dependency information, improving future prediction accuracy and reducing false dependencies.
2Reliability
If the processor uses static mechanisms to prevent load-store ordering violations, then reliability is improved, but processor performance degrades due to overcompensation and unnecessary in-order enforcement
Solution Approach 1:
The dependency prediction mechanism is dynamic rather than static. The predictor table entries are created, updated, and removed based on actual execution patterns and detected violations. Dependencies are established dynamically per instruction pair rather than using fixed static rules, allowing the system to adapt to actual program behavior.
Solution Approach 2:
The system changes the parameter of dependency enforcement from a static binary state to a dynamic probability-based state. The predictor table stores dependency strength indicators that vary based on historical accuracy, allowing the processor to adjust the aggressiveness of out-of-order scheduling based on predicted reliability.
3Reliability
If the processor enforces load-store dependencies to prevent ordering violations, then reliability is improved, but memory level parallelism is reduced due to unnecessary dependency enforcement
Solution Approach 1:
The predictor table applies dependency enforcement locally to specific load-store pairs rather than globally to all memory operations. Each entry in the predictor table is specific to a particular store-load pair with matching addresses, allowing fine-grained control over which operations are constrained and which can proceed in parallel.
4Reliability
If the processor uses a predictor table to track load-store dependencies, then ordering violations are prevented, but device complexity increases due to the additional predictor structure
Solution Approach 1:
The predictor table serves multiple functions: it tracks address matching between loads and stores, stores dependency strength indicators, manages entry validity states, and provides lookup capabilities for both load and store operations. This multi-functionality reduces the need for separate specialized structures for each function.
Data Source
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AI summary
Methods and apparatuses for managing load-store dependencies in an out-of-order processor. A load store dependency predictor may include a table for storing entries for load-store pairs that have been found to be dependent and execute out of order. Each entry in the table includes a counter to indicate a strength of the dependency prediction. If the counter is above a threshold, a dependency is enforced for the load-store pair. If the counter is below the threshold, the dependency is not enforced for the load-store pair. When a store is dispatched, the table is searched, and any matching entries in the table are armed. If a load is dispatched, matches on an armed entry, and the counter is above the threshold, then the load will wait to issue until the corresponding store issues.