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name: benchmark-functions description: "Measure function performance and compare implementations. Use when optimizing critical code paths." mcp_fallback: none category: analysis tier: 2 user-invocable: false

Benchmark Functions

Systematically measure function execution time, memory usage, and performance characteristics to identify optimization opportunities.

When to Use

• Comparing different algorithm implementations
• Measuring performance before/after optimization
• Profiling SIMD vs scalar implementations
• Establishing performance baselines for CI/CD

Quick Reference

Copy# Python benchmarking with timeit
python3 -m timeit -s 'import module' 'module.function(args)' -n 1000 -r 5

# Mojo benchmarking with built-in timing
mojo run benchmark_script.mojo

Workflow

1. Set up benchmarks: Create timing harness with warm-up iterations
2. Run measurements: Execute function multiple times, record timing
3. Collect statistics: Calculate mean, median, std deviation
4. Compare baselines: Compare against previous implementations
5. Identify bottlenecks: Pinpoint functions needing optimization

Output Format

Benchmark report:

• Function name and parameters tested
• Execution time statistics (mean, median, min, max)
• Memory usage (if applicable)
• Comparison to baseline (improvement percentage)
• Iterations and sample size used

References

• See profile-code skill for detailed performance profiling
• See suggest-optimizations skill for improvement strategies
• See CLAUDE.md > Performance for Mojo optimization guidelines