Load-Store Dependency Predictor for Out-of-Order Processor Latency
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Solution Overview
Problem
The increasing clock frequencies in processors lead to increased latency in load and store operations, particularly due to deep pipelining and latency speculation, which can result in appreciable delays when traversing linked lists and using load instruction result data, impacting performance.
Innovation Solution
Incorporating a load-store unit (LSU) with reservation stations, address generation units, a data cache, a load queue, a store queue, and a load-store dependency predictor to detect dependencies and predict whether result data resides in the data cache or store queue, allowing for direct forwarding of data from the cache to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If deep pipelining is used to increase clock frequency, then processor speed is improved, but latency for load operations increases
Solution Approach 1:
The patent applies preliminary action by predicting whether a producing load will result in a cache hit or store queue hit before the actual load operation completes. The load-store dependency predictor speculatively determines the outcome and prepares the consumer load to proceed early if the prediction indicates a cache hit, thus reducing latency while maintaining high clock frequencies through deep pipelining
2Speed
If data forwarding is implemented to reduce latency, then load operation speed is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary component - the load-store dependency predictor - that mediates between the producing load and consumer load operations. This predictor acts as a specialized structure that intercepts and predicts load outcomes, enabling early issuance of consumer loads without requiring complex general-purpose data forwarding mechanisms throughout the entire processor
3Productivity
If latency speculation is used to issue consumer loads early, then productivity is improved, but reliability decreases due to potential mispredictions
Solution Approach 1:
The patent implements feedback by training the load-store dependency predictor using actual outcomes of producing loads. The predictor structure is updated based on whether cache hits or store queue hits actually occur, allowing the system to learn from past behavior and improve prediction accuracy over time, thus maintaining high productivity while improving reliability through adaptive learning
Data Source
AI summary
Systems, methods, and apparatuses for reducing the load to load/store address latency in an out-of-order processor. When a producer load is detected in the processor pipeline, the processor predicts whether the producer load is going to hit in the store queue. If the producer load is predicted not to hit in the store queue, then a dependent load or store can be issued early. The result data of the producer load is then bypassed forward from the data cache directly to the address generation unit. This result data is then used to generate an address for the dependent load or store, reducing the latency of the dependent load or store by one clock cycle.