gpu-profiling

Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks.

Cross-platform .NET performance engineering skill for coding agents, covering CPU, memory, GC, benchmarking, concurrency, startup, native profiling, GPU rendering, and production diagnostics on macOS, Windows, and Linux.

Profine automatically profiles and optimizes PyTorch training jobs on real GPUs, delivering measurable speedups and lower GPU costs before teams waste days tuning configs by hand.

Interactive exercises for understanding compute, communication, and dependencies in distributed LLM training traces.

Evidence-first tools to measure, verify, and optimize LLM inference across local GPUs and OpenAI-compatible APIs.

Automated GPU profiling analysis for Adreno — turns Snapdragon Profiler captures into actionable insights with LLM

NAV extracts and analyzes GPU performance traces from NVIDIA Nsight™ Systems (NSYS), enabling comparative analysis and visualization for efficient performance profiling and regression testing.

Agent Skill + Claude Code Plugin for debugging Chrome WebGPU on macOS — Chrome DevTools MCP + Xcode Metal tracing

Per-precision XMX (matrix engine) profiling for Intel Arc GPUs via Level Zero metric streamers — observes any workload without wrapping it

Communication cost modeling for tensor parallel LLM inference with TP vs PP vs hybrid comparison, VRAM analysis, pipeline bubble modeling, regime detection, and cost-efficiency. Shows TP dominates on NVLink, PP has 47% bubble at 8 GPUs, and LLaMA-70B needs 8× A100 or 2× H100 for VRAM.

Hands-on ML accelerator profiling labs using PyTorch Profiler, mixed precision benchmarking, Google Colab, and Perfetto.

Reproducible MiniMax-H3 benchmarks on NVIDIA GB300: SGLang scaling, host eBPF CUDA tracing, Grace-GPU PMU traffic, energy, and Nsight comparison.

NAV extracts and analyzes GPU performance traces from NVIDIA Nsight™ Systems (NSYS), enabling comparative analysis and visualization for efficient performance profiling and regression testing.

Collection of examples and links that uses different profiling tools to show memory usage and timings.

Capture and analyze Metal GPU frames from Unity on macOS — an Editor window + AI Assistant skill driving macOS 27 gpucapture/gpudebug (no Xcode): real GPU frame/pass timing, frame-budget gauge, GPU bottleneck classification, and deterministic Top-3 URP optimization insights.

基于证据的 vLLM v0.26 Scheduler Trace、KV Cache 压力、Waiting HOL 策略与 GPU Profiling 实验

Read and analyze NVIDIA Nsight Graphics GPU Trace captures with Rust, JSON CLI, and MCP

JAX benchmarking, profiling and evaluation metrics for Flax NNX: a registry of pure-function metrics (regression, classification, calibration, uncertainty, forecasting, generative, image, text, audio, graph, fairness), XLA FLOP counting, roofline analysis, GPU and energy monitoring, regression detection, publication exports, W&B and MLflow.

Reproducible study of how tensor layout, reduction length, and pointer alignment drive vendor GEMM dispatch and latency cliffs.

Agent Skills and an MCP server for GPU performance profiling, benchmarking, optimization, and reporting, with an inference focus.