Dynamic Sampling Frequency Adjustment for Runtime Profiling
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Solution Overview
Problem
Determining an optimal sampling frequency for generating runtime profiles from processor performance counters is challenging, as high frequencies increase execution overhead, while low frequencies yield inaccurate information, impacting code optimization in runtime environments.
Innovation Solution
A profile management unit dynamically adjusts sampling frequencies based on information entropy values, generating accurate representative profiles without affecting code execution, allowing for informed optimizations by combining multiple profiles and adjusting sampling rates accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling frequency is increased to improve profiling accuracy, then measurement precision is improved, but execution overhead increases
Solution Approach 1:
The system dynamically adjusts the sampling frequency based on the current execution state and profile quality metrics. Instead of using a fixed high sampling frequency, the system adapts the sampling rate in real-time, increasing it when profile accuracy needs improvement and decreasing it when the current profile is sufficient, thereby balancing profiling accuracy with execution overhead.
Solution Approach 2:
The system implements a feedback mechanism where the quality of generated profiles is evaluated and used to adjust future sampling frequencies. The system monitors profile quality metrics and uses this feedback to determine whether to increase or decrease the sampling frequency in subsequent profiling phases, optimizing the balance between accuracy and overhead.
2Loss of energy
If sampling frequency is decreased to reduce execution overhead, then loss of energy is reduced, but measurement precision deteriorates
Solution Approach 1:
The system uses dynamic sampling frequency adjustment to avoid the need for consistently high sampling rates. By adapting the sampling frequency to the actual needs of profile generation, the system can operate at lower overhead during phases where high-frequency sampling is not critical, while maintaining accuracy when needed.
Solution Approach 2:
The system applies partial sampling action by using different sampling frequencies for different profiling phases or different code regions. Instead of uniformly high-frequency sampling throughout, the system applies sampling at appropriate frequencies only where and when needed, reducing overall overhead while maintaining necessary profile accuracy.
Data Source
AI summary
A method for managing a profile includes generating an initial profile of code using an initial sampling frequency. An information entropy value of the profile is computed. A representative profile of the code is determined from additional profiles using a sampling frequency determined from the information entropy value. Other embodiments are disclosed and claimed.