Composite Visualization for Correlating CPU Performance and Functional Data
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Solution Overview
Problem
Existing software profiling methods face challenges in efficiently correlating hardware performance counter data with functional information, particularly in complex software programs like video games, due to the difficulty in aligning and overlaying sampling data from different time periods, leading to unreliable trend analysis.
Innovation Solution
The method involves overlaying statistical CPU performance data and functional data aligned by frames, creating a composite visual display that facilitates the correlation of these data types using a heat-map-like representation, where data overlap is indicated by intensity changes, allowing for better identification of performance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical sampling is used to profile executing software, then the analysis can be performed on real-world execution data, but the sampled data from different time periods becomes difficult to align and overlay, leading to unreliable trend analysis
Solution Approach 1:
The patent segments the execution timeline into discrete frames based on the software's frame rate (e.g., 30 or 60 frames per second). Each frame becomes a time slice for sampling, allowing consistent alignment across multiple frames. This segmentation resolves the alignment difficulty by providing a natural, uniform time grid that matches the software's execution rhythm.
Solution Approach 2:
The patent uses periodic sampling at regular frame intervals to collect profile data. By sampling at consistent periodic intervals (one sample per frame), the system creates alignable data structures that can be overlaid across time. This periodic approach transforms irregular sampling into a structured, alignable format that enables reliable trend analysis.
2Measurement precision
If manual code review is used to track down problems, then detailed inspection is possible, but the process becomes exponentially more complex and time-consuming as project size increases
Solution Approach 1:
The patent replaces manual code review with automated statistical sampling and visual analysis. Instead of humans reading and analyzing code line-by-line, the system automatically samples execution data, aggregates statistics across frames, and generates visual representations. This substitution dramatically reduces time while maintaining detection precision through quantitative measurement.
Solution Approach 2:
The patent employs color-coded visual representations to encode performance data intensity and patterns. Different colors indicate different levels of function execution frequency or performance anomalies, allowing analysts to quickly identify problems through visual perception rather than detailed textual analysis. This visual encoding enables rapid scanning of large codebases without losing measurement precision.
3Ease of operation
If textual or tabular presentation of profiler information is used, then simplicity is maintained, but the ability to correlate different types of information is difficult
Solution Approach 1:
The patent transforms one-dimensional textual/tabular data into two-dimensional visual plots where the x-axis represents time (frame number) and the y-axis represents performance metrics. This dimensional transformation enables simultaneous display of multiple data types (CPU time, memory, function calls) that can be visually correlated across time, revealing patterns and relationships that are difficult to perceive in tabular form.
Solution Approach 2:
The patent merges multiple types of profile data (CPU performance counters, memory usage, function call statistics) into a single unified visual display. By combining these data types in one plot with different visual encodings (line styles, colors, markers), the system enables simultaneous correlation analysis of all metrics without requiring multiple separate views or complex cross-referencing procedures.
Data Source
AI summary
Analysis data from the execution of a computer program is collected. The analysis data may include samples of two different types of performance data from the execution of the computer program. Performance data may include sampled hardware performance counter data and sampled executing functions. The performance data is used to create a composite view of the data. The composite view may allow a user to efficiently correlate the different types of performance data by using a colored visual display.