gpu-benchmarking
Expert skill for automated GPU performance benchmarking and regression detection. Design micro-benchmarks, measure kernel execution time with CUDA events, calculate achieved vs theoretical performance, generate comparison reports, detect regressions in CI/CD, and profile power/thermal characteristics.
npx skills add a5c-ai/babysitter --skill gpu-benchmarking --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# gpu-benchmarking You are **gpu-benchmarking** - a specialized skill for automated GPU performance benchmarking and regression detection. This skill provides expert capabilities for measuring, analyzing, and tracking GPU kernel performance over time. ## Overview This skill enables AI-powered GPU benchmarking operations including: - Designing micro-benchmarks for kernel operations - Measuring kernel execution time with CUDA events - Calculating achieved vs theoretical performance - Generating performance comparison reports - Detecting performance regressions in CI/CD - Profiling power and thermal characteristics - Benchmarking memory bandwidth and latency - Creating reproducible benchmark configurations ## Prerequisites - NVIDIA CUDA Toolkit 11.0+ - GPU with performance counters support - nvidia-smi for power/thermal monitoring - Optional: Nsight Systems/Compute for detailed profiling - CI/CD system for regression tracking ## Capabilities ### 1. CUDA Event Timing Precise kernel execution time measurement: ```cuda // Benchmark timing wrapper cudaEvent_t start, stop; cudaEventCreate(&start); cudaEventCreate(&stop); // Warm-up run myKernel<<<grid, block>>>(args); cudaDeviceSynchronize
- Overview
- Prerequisites
- Capabilities
- 1. CUDA Event Timing
- 2. Comprehensive Benchmark Framework
- 3. Roofline Model Analysis
- 4. Memory Bandwidth Benchmark
- 5. Latency Benchmark
- 6. Power and Thermal Monitoring
- 7. CI/CD Regression Detection
- 8. Benchmark Report Generation
- MCP Server Integration
- Best Practices
- Benchmark Design
power_monitor.sh - Monitor GPU power during benchmark echo "timestamp,power_w,temp_c,gpu_util,mem_util" > $LOG_FILE Start power monitoring in background nvidia-smi --query-gpu=timestamp,power.draw,temperature.gpu,utilization.gpu,utilization.memory \ Run benchmark eval $BENCHMARK_CMD Stop monitoring kill $MONITOR_PID Generate report echo "=== Power Analysis ==="
What does the gpu-benchmarking skill do?
Expert skill for automated GPU performance benchmarking and regression detection. Design micro-benchmarks, measure kernel execution time with CUDA events, calculate achieved vs theoretical performance, generate comparison reports, detect regressions in CI/CD, and profile power/thermal characteristics.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill gpu-benchmarking --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.