Indirect Target Predictor Partitioning for Context-Sensitive Branches
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Solution Overview
Problem
Existing branch predictors struggle to accurately predict indirect branches and calls due to their limited size and complexity, leading to inefficient allocation of resources and increased latency, especially for context-insensitive branches that do not benefit from context information.
Innovation Solution
Implement a method to classify indirect branches as context-sensitive (CS) or context-insensitive (CIS) and segregate them into separate predictors, using context information for CS branches and simpler mechanisms for CIS branches, optimizing the use of branch predictor space and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all indirect branches and calls are treated as context-sensitive in the indirect target predictor, then context-sensitive branches can be accurately predicted using context information, but context-insensitive branches needlessly occupy scarce predictor space that could be better used for context-sensitive branches
Solution Approach 1:
The indirect target predictor is segmented into two separate structures: a first indirect target predictor for context-insensitive indirect branches and a second indirect target predictor for context-sensitive indirect branches. This segmentation allows each predictor to be optimized for its specific type of branch, preventing context-insensitive branches from occupying space needed for context-sensitive branches while maintaining accurate prediction for both types.
2Measurement precision
If more complicated branch predictor structures are used to improve prediction accuracy, then more branches can be tracked accurately, but access latency increases and redirect cycles increase
Solution Approach 1:
By segmenting the predictor into two separate structures with different complexity levels, the system can use a simpler first predictor for context-insensitive branches (reducing access latency) and a more complex second predictor for context-sensitive branches (improving prediction accuracy). This resolves the contradiction by allowing different complexity levels for different branch types.
Solution Approach 2:
Different parts of the branch prediction system are given different qualities: the first indirect target predictor uses simpler hardware for faster access, while the second indirect target predictor uses more complex structures with context information for higher accuracy. Each part is optimized for its specific function, resolving the trade-off between complexity and latency.
3Loss of time
If simpler branch predictor hardware is used to reduce access latency, then redirect cycles are reduced, but the ability to track complex control flows and predict behavior accurately is reduced
Solution Approach 1:
The segmentation into two predictors allows simple hardware to be used for context-insensitive branches (reducing latency) while complex hardware with context information is used for context-sensitive branches (improving accuracy). This resolves the contradiction by matching hardware complexity to the specific requirements of each branch type.
Data Source
AI summary
Methods and apparatus are provided to improve target prediction for indirect branches of computer programs. To improve the efficiency of indirect target predictors (JTPs), embodiments of the present disclosure partition JTPs to separately handle context-sensitive and context-insensitive indirect branches. Indirect branches are transformed with indicators of their context sensitivity to enable correct handling by a partitioned JTP. Embodiments use a program optimizer to analyze control flows within computer programs to determine the context sensitivity of indirect branches. In some embodiments, context sensitivity is established from data-flow graphs. In other embodiments, context sensitivity is established from profiling data of the computer program.