User-Directed Profiling Framework for Application-Specific Compiler Tuning
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Solution Overview
Problem
Customary feedback-directed optimization techniques lack the ability to understand application-specific details, limiting their effectiveness in tuning performance parameters, and manual tuning processes are labor-intensive.
Innovation Solution
A framework that allows software developers to profile selected application parts and direct compilers to perform value profile transformations based on user-defined instrumentation and optimization compilations, using GCC built-in functions for counter allocation, instrumentation, and transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customary feedback-directed optimization is used, then compilers can perform basic optimizations, but they cannot understand application-specific details and algorithms
Solution Approach 1:
The patent introduces a profiling framework as an intermediary layer between the application and the compiler. This framework includes profile instrumentation code that collects runtime behavior data and profile data structures that store application-specific information. The compiler then uses this profile data to perform optimized code generation, effectively bridging the gap between the application's high-level logic and the compiler's low-level optimization capabilities.
Solution Approach 2:
The patent performs profiling during a separate instrumentation phase before the actual compilation and optimization. Runtime behavior data is collected and stored in profile data structures during application execution, which are then used by the compiler in a subsequent optimization pass. This preliminary collection of application-specific information enables the compiler to make informed optimization decisions without needing to understand the application logic during the compilation process itself.
2Productivity
If manual parameter tuning is performed, then application performance can be optimized for specific cases, but the process becomes labor-intensive
Solution Approach 1:
The patent enables the system to automatically tune its own performance parameters through profile-driven optimization. The profiling framework collects runtime behavior data and the compiler automatically uses this information to optimize code generation without requiring manual intervention. This self-service approach eliminates the need for developers to manually analyze and tune performance parameters while still achieving application-specific optimization.
Solution Approach 2:
The patent implements a feedback loop where runtime behavior data is collected during application execution, stored in profile data structures, and then used by the compiler to optimize code generation. This feedback mechanism automatically adjusts optimization parameters based on actual application performance characteristics, eliminating the need for manual trial-and-error tuning while continuously improving performance.
3Measurement precision
If profile data structures are implemented, then application-specific information can be captured, but the data structure complexity increases
Solution Approach 1:
The patent divides the profile data collection into separate, modular components: profile instrumentation code that collects specific runtime events, profile data structures that organize the collected information, and profile processing routines that analyze the data. This segmentation allows each component to be independently optimized and managed, reducing the perceived complexity while maintaining comprehensive application behavior measurement capability.
Data Source
AI summary
A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.