Profile Guided Optimization Using Correlation Data for Precise Mapping
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Solution Overview
Problem
Existing profile guided optimization techniques face limitations in accurately mapping hardware instruction counts to corresponding source code locations, leading to inefficient compiler optimizations due to reliance on debugging information and inability to differentiate instructions spread across multiple basic blocks.
Innovation Solution
The generation of correlation data that maps sampled instructions to basic blocks using relative virtual addresses (RVAs), enabling precise block count updates and edge count determination for compiler optimizations, thereby improving code execution efficiency and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If debugging information is used to map hardware instructions to source code locations, then the mapping process is simplified, but the accuracy of mapping is insufficient and cannot differentiate instructions spread across multiple basic blocks
Solution Approach 1:
The patent segments the source code into basic blocks and creates separate correlation data structures for each basic block, mapping hardware instructions to specific basic blocks rather than relying on coarse-grained debugging information. This segmentation enables precise differentiation of instructions spread across multiple basic blocks while maintaining systematic organization through the correlation data structure.
Solution Approach 2:
The patent introduces correlation data as an intermediary structure that bridges hardware instructions and source code locations. This correlation data includes basic block identifiers and instruction mappings, serving as a mediator that translates hardware instruction addresses to precise source code locations without directly relying on debugging information, thereby improving mapping accuracy.
2Productivity
If profile data is collected from sample runs to guide compiler optimizations, then optimization effectiveness is improved, but the complexity of collecting and processing profile data increases
Solution Approach 1:
The patent performs preliminary compilation to generate correlation data structures before executing sample runs. This preliminary action prepares the mapping framework in advance, allowing profile data collected during sample runs to be directly correlated with basic blocks using the pre-established correlation data, thereby simplifying the processing complexity while maintaining optimization effectiveness.
Solution Approach 2:
The patent implements a feedback loop where profile data from sample runs is collected, correlated with basic blocks using the correlation data structure, and then used to guide compiler optimizations. This feedback mechanism continuously improves optimization effectiveness by incorporating actual runtime behavior information while the structured correlation data keeps processing manageable.
3Measurement precision
If instructions are mapped to basic blocks using relative virtual addresses, then precise block count updates are enabled, but the complexity of address mapping and correlation increases
Solution Approach 1:
The patent creates a copied and simplified representation of address mappings in the correlation data structure. Instead of directly managing complex virtual address translations, the system copies essential mapping information (basic block identifiers and address ranges) into the correlation data, enabling precise block count updates through this simplified copied representation while reducing direct address mapping complexity.
Data Source
AI summary
A profile guided optimization compiler utilizes sample profile data including a control flow representation of a program having block counts associated with each basic block of the program, and edge counts associated with each control flow edge estimated from the block counts. The sample profile data utilizes correlation data to map the address of a sampled instruction from a fully optimized binary directly into a corresponding basic block of source code control flow of the program using a relative virtual address (RVA) that is associated with each source code basic block and the sampled instruction. The correlation data is able to differentiate multiple blocks on the same source code line and handle inlining and optimizations with greater precision and efficiency. The block counts are then used to guide the optimization of the program.