Probabilistic Call Chain Profiling for Hardware Event Correlation
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Solution Overview
Problem
Current profiling tools for resource use, such as cache misses or memory usage, are limited in detail and accuracy, often resulting in high overhead in execution time and memory usage, making them unsuitable for large applications.
Innovation Solution
A probabilistic method for profiling that correlates resource usage with hardware events by sampling call chains and mapping them to execution times, providing detailed resource usage information with minimal overhead through a program module that samples call chains and maps them to hardware events using probabilistic integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional profiling tools are used to track resource usage, then measurement precision is improved, but execution time increases significantly
Solution Approach 1:
The patent implements periodic sampling of call chains at hardware events instead of continuous tracking. The profiler samples the call chain stack at periodic hardware events (cache misses, memory allocations, etc.) to estimate resource usage, thereby reducing execution time overhead while maintaining measurement precision through statistical sampling methods.
2Measurement precision
If detailed call chain tracking is implemented, then measurement precision is improved, but memory usage increases significantly
Solution Approach 1:
The patent uses a compressed representation of call chains instead of storing complete call chain histories. It samples and records call chain information at hardware events, creating a compressed profile that captures essential attribution data while using minimal memory, avoiding the need to store full call stack traces for every event.
3Measurement precision
If complete resource tracking is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces hardware performance counters as intermediaries between the application and the profiler. These counters automatically track hardware events (cache misses, memory allocations, CPU cycles) without requiring complex software instrumentation, thereby reducing the complexity of the profiling system while maintaining precise resource measurement and correlation with call chains.
4Measurement precision
If sampling rate is increased to improve accuracy, then measurement precision is improved, but execution time overhead increases
Solution Approach 1:
The patent uses periodic sampling triggered by hardware events rather than continuous high-rate sampling. By sampling at the occurrence of specific hardware events (cache misses, memory allocations) which naturally occur periodically during execution, the system achieves good measurement precision without the excessive overhead of continuous high-frequency sampling.
Data Source
AI summary
A system and method for correlation of resources with hardware events includes event driven sampling a call chain of functions to determine when functions of the call chain are active. The call chain is mapped to execution times based upon a probabilistic integration of the functions such that when portions of the call chain are active, resources associated with call chain activity are correlated with hardware events.