Focused Data Value Prediction for Processor Pipeline Stalls
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Solution Overview
Problem
Modern superscalar processor performance is limited by control flow dependencies and slow memory, with traditional techniques like branch prediction and memory prefetching having accuracy and coverage restrictions.
Innovation Solution
Focused data value prediction is implemented to accelerate execution by predicting only a subset of instructions that feed into delinquent loads or hard-to-predict branch instructions, using a value prediction table with confidence values to facilitate early speculative execution and reduce pipeline stalls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general value prediction is implemented across all instructions, then some performance improvement is achieved, but the complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the value prediction system by identifying and predicting only a focused subset of instructions (feeder instructions) that are most likely to cause pipeline stalls, rather than implementing general value prediction across all instructions. This segmentation reduces system complexity while maintaining performance benefits.
Solution Approach 2:
The patent applies local quality by concentrating prediction resources on specific instructions with high impact (delinquent loads and hard-to-predict branches) rather than uniformly applying prediction across all instructions. This localized approach optimizes the ratio of performance improvement to system complexity.
2Adaptability or versatility
If value prediction is applied to all instructions, then comprehensive coverage is achieved, but the accuracy and effectiveness decrease due to resource dilution
Solution Approach 1:
The patent applies partial action by selectively predicting only the most critical feeder instructions rather than all instructions. This focused approach concentrates prediction accuracy on high-impact instructions while avoiding the resource dilution that would occur with universal prediction.
Solution Approach 2:
The patent changes the parameter of prediction scope from universal to selective, focusing on instructions that feed into delinquent loads and hard-to-predict branches. This parameter change maintains adaptability for critical paths while improving accuracy by concentrating resources.
3Productivity
If delinquent loads and hard-to-predict branches are addressed with traditional techniques, then some performance improvement is achieved, but pipeline stalls continue to occur
Solution Approach 1:
The patent applies preliminary action by predicting the values of feeder instructions in advance, before the actual execution of delinquent loads and hard-to-predict branches. This allows the processor to prepare data values ahead of time, reducing pipeline stalls when these critical instructions are executed.
Solution Approach 2:
The patent introduces an intermediary value prediction mechanism that bridges the gap between feeder instructions and delinquent loads/branches. By predicting intermediate values, the system reduces waiting time and keeps the pipeline flowing smoothly through critical sections.
Data Source
AI summary
In one embodiment, an apparatus includes: a value prediction storage including a plurality of entries each to store address information of an instruction, a value prediction for the instruction and a confidence value for the value prediction; and a control circuit coupled to the value prediction storage. In response to an instruction address of a first instruction, the control circuit is to access a first entry of the value prediction storage to obtain a first value prediction associated with the first instruction and control execution of a second instruction based at least in part on the first value prediction. Other embodiments are described and claimed.