Activity-Level CPU Profiling via Thread Sampling
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Solution Overview
Problem
Conventional CPU profilers fail to identify individual CPU usage among a collective set of activities, leading to false indications of CPU activity and inability to pinpoint CPU hotspots or inefficient code portions, which hinders performance optimization.
Innovation Solution
A sampling-based CPU profiler that identifies CPU hotspots by determining the number of busy and wasted threads, calculating CPU time usage for each activity, and providing data for code optimization, allowing for maximal CPU utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional profilers collect data about collective CPU usage for an entire set of activities, then the measurement coverage is comprehensive, but the measurement precision is insufficient to identify individual CPU usage patterns and hotspots
Solution Approach 1:
The patent segments the collective CPU usage measurement into individual activity-level measurements. By tracking CPU usage at the activity level rather than collectively, the system achieves precise identification of CPU hotspots and individual activity performance without requiring complex profiler architecture.
Solution Approach 2:
The patent uses sampling-based measurement that captures CPU usage at specific intervals rather than continuously monitoring all activities. This partial action approach provides sufficient precision for identifying hotspots while avoiding the complexity of comprehensive continuous monitoring.
2Loss of information
If conventional profilers detect collective CPU usage for all activities, then the data collection is complete, but the information about which activities are using CPU and which are not is lost
Solution Approach 1:
The patent segments CPU usage data by individual activities, allowing identification of which specific activities are consuming CPU resources and which are idle. This segmentation preserves activity-specific information that enables targeted performance optimization.
Solution Approach 2:
The patent introduces activity identifiers as intermediaries between CPU usage events and the profiler. These identifiers link CPU usage data to specific activities, enabling the system to track and report which activities are using CPU resources without losing information.
3Measurement precision
If conventional profilers provide only collective CPU usage data, then the data collection is simple, but the ability to identify CPU hotspots and code portions that excessively use CPU is lost
Solution Approach 1:
The patent segments CPU usage measurement to the activity level, enabling precise identification of CPU hotspots by comparing individual activity usage against thresholds. This segmentation approach achieves hotspot identification accuracy without requiring complex analysis algorithms.
Solution Approach 2:
The patent changes the measurement parameter from collective CPU usage to activity-level CPU usage. This parameter change enables direct identification of hotspots and excessive CPU usage by specific activities while maintaining relatively simple profiler implementation.
4Measurement precision
If the system tracks individual activity CPU usage, then the measurement precision improves for identifying hotspots, but the data processing complexity increases
Solution Approach 1:
The patent uses sampling-based measurement that captures activity CPU usage at selected intervals rather than continuously. This partial monitoring approach provides sufficient precision for identifying hotspots while reducing data processing complexity compared to continuous tracking.
Solution Approach 2:
The patent extracts only the essential CPU usage data at activity level needed for hotspot identification, rather than processing all possible activity attributes. This extraction approach maintains measurement precision while minimizing data processing complexity.
Data Source
AI summary
Processor(s) of a sampling profiler can identify an activity of multiple activities serviced by a central processing unit (CPU). Each activity can be performed by computing thread(s) of multiple computing threads executing various subroutines of a computer program. The processor(s) can set a target representing a total number of computing threads required to work simultaneously for a maximal use of the CPU. The processor(s) can determine a number of busy computing threads that are performing the activity by using the CPU. The processor(s) can calculate a number of wasted computing threads that are not performing the activity and not using the CPU by computing a difference between the target and the number of busy threads. The processor(s) can compute a CPU time usage for the activity by multiplying time duration of the activity by a value obtained by dividing the number of wasted threads by the number of busy threads.