Machine Learning Static Profiling for Accurate Branch Frequencies
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Solution Overview
Problem
Existing profile-guided optimization (PGO) techniques rely on dynamic profiling, which is costly and less accurate, while static profiling is less precise, leading to suboptimal program optimizations.
Innovation Solution
Employing a regression machine learning model to process control flow split node features from an intermediate representation (IR) graph to generate branch frequency values, which are then used to create a more accurate static profile for optimizing programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic profiling is used to collect execution data, then profile quality is improved, but execution cost increases significantly
Solution Approach 1:
The patent performs static profiling during the compilation phase before program execution, extracting control flow features and generating branch frequency predictions in advance. This preliminary action eliminates the need for costly runtime instrumentation and execution-based data collection, achieving high-quality profiles without the execution overhead of dynamic profiling
Solution Approach 2:
The patent replaces the mechanical execution-based data collection process with a machine learning-based prediction system. Instead of instrumenting the program and running it to collect profile data, the system uses a trained regression model to predict branch frequencies directly from control flow features extracted from the intermediate representation, substituting computational execution with algorithmic prediction
2Loss of time
If static profiling with traditional heuristics is used, then execution cost is reduced, but profile accuracy deteriorates
Solution Approach 1:
The patent replaces traditional heuristic-based static profiling with a machine learning-based prediction system. The regression model, trained on control flow features from intermediate representations, predicts branch frequencies with higher accuracy than conventional heuristics, maintaining low execution cost while improving profile quality
Solution Approach 2:
The patent changes the parameters used in static profiling from simple heuristic rules to complex machine learning models that process multiple control flow features. By transforming the prediction parameters from basic heuristics to trained neural network predictions, the system achieves superior accuracy while maintaining the efficiency benefits of static analysis
3Measurement precision
If machine learning model is used for static profiling, then profile accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediate representation (IR) as a mediator between the source code and the machine learning model. The control flow graph extracted from the IR provides a standardized, structured input format that simplifies the interface between the compiler infrastructure and the ML system, reducing integration complexity while enabling accurate predictions
Solution Approach 2:
The patent segments the profiling system into distinct modular components: control flow graph extraction, feature extraction from control flow nodes, machine learning prediction, and profile generation. This segmentation allows each component to be developed and optimized independently, managing system complexity through modularity while achieving high accuracy through the coordinated workflow
Data Source
AI summary
Machine learning based static profiling includes obtaining an intermediate representation (IR) graph of source code of a program, extracting multiple control flow split node features of a control flow split node in the IR graph, and processing, by a regression machine learning model, the control flow split node features to generate a branch frequency value of a branch from the control flow split node. The machine learning based static profiling further includes adding the branch frequency value to a profile for the program and executing an optimizer on the program according to the profile to generate optimized code.