Branch Predictor Scheduling for Balanced Multi-Instruction Fetch
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Solution Overview
Problem
Current branch predictors in high-performance processors are inefficient in utilizing resources due to inherent patterns in matching and usage mechanisms, leading to reduced efficiency and speed in instruction processing.
Innovation Solution
A method and apparatus for scheduling a branch predictor that determines a group of branch predictors based on initial fetch addresses and instruction types, ensuring equal selection probability and optimizing the utilization rate by allocating branch predictors efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the branch predictor attempts to parallelly predict a plurality of instructions within a single clock cycle, then the instruction processing speed is improved, but the branch predictor cannot be fully and effectively utilized, resulting in resource wastage
Solution Approach 1:
The patent implements dynamic scheduling of branch predictors by introducing a scheduling unit that assigns different branch predictors to different instructions based on real-time conditions. The scheduling unit determines usage order based on initial fetch addresses and instruction types, dynamically allocating branch predictors (1501-150N) to instructions (101-10M) rather than using static assignment. This dynamic allocation ensures that branch predictors are fully utilized while maintaining parallel prediction capability across multiple instructions within a single clock cycle.
2Productivity
If multiple branch predictors are used to process multiple instructions, then the utilization rate is improved, but the complexity of managing and scheduling the branch predictors increases
Solution Approach 1:
The patent introduces a scheduling unit as an intermediary component that manages the allocation between multiple branch predictors (1501-150N) and multiple instructions (101-10M). This scheduling unit acts as a mediator that receives instructions, determines appropriate branch predictors based on fetch addresses and instruction types, and assigns them in a coordinated manner. This intermediary layer simplifies the management complexity by centralizing the scheduling logic while enabling efficient utilization of multiple branch predictors across multiple instructions.
Data Source
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AI summary
The present disclosure provides a method for scheduling a branch predictor for multiple instructions. The method comprises: determining a branch predictor group; acquiring a set of instructions; acquiring an initial instruction fetch address and an instruction type of each instruction; determining a use sequence of N branch predictors according to the initial instruction fetch address and the instruction type of the instruction; determining a correspondence between M instructions and the N branch predictors according to the use sequence and an instruction arrangement sequence in the set of instructions; and scheduling a corresponding branch predictor according to the instruction arrangement sequence in the set of instructions and the correspondence so as to process an instruction in the set of instructions. By acquiring the initial instruction fetch address and on the basis of the instruction type, a branch predictor corresponding to each instruction is determined, so that rapid allocation of a branch predictor can be achieved, and it can be guaranteed that the probability of selecting each branch predictor is the same, thereby improving the utilization rate of a branch predictor and the instruction processing efficiency.