Performance Analyzer Skill
Purpose
Analyzes performance benchmarks and profiling data from CUDA, CPU, memory, and system tests. Automatically parses benchmark output, identifies performance bottlenecks, tracks metrics over time, and generates actionable optimization insights.
Capabilities
Benchmark Parsing
- •CUDA kernel benchmarks (GFLOPS, TFLOPS, execution time)
- •CPU benchmarks (GFLOPS, multi-core performance)
- •Memory bandwidth tests
- •Custom benchmark formats (CSV, JSON, text output)
Analysis Features
- •Performance regression detection
- •Bottleneck identification
- •Comparison across runs/configurations
- •Statistical analysis (mean, std dev, percentiles)
- •Speedup calculations
- •Efficiency metrics
Tracking
- •Historical performance data
- •Trend analysis over time
- •Configuration impact analysis
Usage
The skill automatically loads when you:
- •Ask to analyze benchmark results
- •Request performance comparisons
- •Need to identify bottlenecks
- •Want to track performance over time
Files
- •
parsers.py- Parsers for various benchmark formats - •
analyzer.py- Core analysis logic and metrics calculation - •
visualizer.py- Data visualization and charting
Example Workflows
- •
Single Benchmark Analysis
- •Parse benchmark output
- •Calculate key metrics
- •Identify bottlenecks
- •Generate summary
- •
Comparative Analysis
- •Parse multiple benchmark runs
- •Compare performance across configurations
- •Calculate speedups and improvements
- •Highlight regressions
- •
Historical Tracking
- •Store results over time
- •Detect performance trends
- •Alert on regressions
- •Track optimization impact
Integration
Works seamlessly with:
- •
tech-reportskill for generating professional reports - •
xlsxskill for detailed data tables - •
pptxskill for presentation-ready visualizations