Hybrid Random Sampling for Video Game Profiling
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Solution Overview
Problem
Traditional statistical profilers for software profiling in embedded devices like video game consoles often result in inefficient sampling due to fixed or random sampling methods, leading to oversampling or undersampling of code sections, and generate large amounts of data that overwhelm limited memory and communication resources, causing performance issues.
Innovation Solution
A hybrid random distribution of sampling points aligned with the periodic nature of video game frame refresh rates, combined with efficient data representation techniques such as transforming call stack data to include only function start addresses and using a 'first child-next sibling' binary tree structure, reduces memory usage and enables faster data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If purely deterministic sampling times are used, then sampling is simple to implement, but some areas of code are oversampled and other areas are undersampled, resulting in poor statistical accuracy
Solution Approach 1:
The patent applies dynamics by transitioning from static deterministic sampling to dynamic sampling where the offset varies over time. The sampling offset is changed periodically or randomly to ensure different code regions are sampled at different times, improving statistical accuracy while maintaining implementation simplicity through systematic variation patterns.
Solution Approach 2:
The patent implements periodic action by systematically varying the sampling offset over time according to a defined pattern. This periodic variation ensures that different phases of the program execution are sampled, preventing consistent oversampling or undersampling of specific code areas while maintaining a structured approach that is easy to implement.
2Measurement precision
If purely random distribution for sampling points is used, then sampling coverage is improved, but samples tend to group or cluster together, resulting in poor reconstruction
Solution Approach 1:
The patent applies dynamics by using dynamic offset variation that changes over time, preventing sample clustering while ensuring comprehensive code coverage. The systematic variation of offsets ensures samples are distributed across different execution phases without random grouping, improving both coverage and reconstruction quality.
Solution Approach 2:
The patent implements feedback mechanisms where sampling data is analyzed to understand execution patterns, and this information feeds back into adjusting future sampling offsets. This feedback loop ensures optimal distribution of samples across code regions while preventing clustering, thereby improving reconstruction quality.
3Measurement precision
If traditional statistical profilers are used, then profiling capability is provided, but large amounts of data are generated that overwhelm limited memory and communication resources
Solution Approach 1:
The patent extracts only the essential profiling data needed for accurate analysis, filtering out redundant information. By focusing on capturing meaningful execution patterns rather than all possible data points, the system maintains profiling capability while significantly reducing the volume of data that needs to be stored and transmitted.
Solution Approach 2:
The patent applies partial action by sampling only the necessary portions of execution data required for accurate profiling. Instead of collecting exhaustive data from every execution event, the system strategically samples representative data points that provide sufficient insight into program behavior, reducing overall data volume while maintaining profiling effectiveness.
4Loss of information
If heavy instrumentation is used, then detailed program flow information is obtained, but the program's dynamic operation is altered or slowed down
Solution Approach 1:
The patent implements self-service by using lightweight sampling mechanisms that require minimal intervention in the program's execution flow. The profiling system serves itself by periodically capturing execution state without requiring extensive instrumentation code, thereby obtaining program flow information while maintaining near-native execution speed.
Solution Approach 2:
The patent applies partial action by implementing minimal instrumentation that captures only essential profiling data. Instead of heavily instrumenting every function and variable, the system strategically places limited sampling points that provide sufficient program flow information while causing negligible impact on execution speed.
Data Source
AI summary
Efficient statistical profiling in embedded computing devices, such as video games, uses a hybrid random distribution of sampling points for more accurate reconstruction of executing code. Transmission of only function start addresses and corresponding representation of the call graph data reduces the memory overhead and increases communication speed.