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.

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

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

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

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

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.

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

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

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.

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

Unified benchmarking and profiling framework for the JAX scientific ML ecosystem. Timing, GPU/energy monitoring, FLOPS counting, roofline analysis, statistical testing, regression detection, and CI integration.

Complexity Assessment of LC methods on CPU and GPU

Kernel-only profiling workflow for CUDA and Triton kernels with Nsight Compute, standardized reports, visual analysis, and vendor-portable adapters.

Live 3D GPU visualizer synced to real PyTorch training telemetry — SM activity, memory bandwidth, kernel execution, rendered in real time.

Attention backend benchmark on Turing GPUs comparing Vanilla, SDPA Math, SDPA Efficient, and a custom Triton FlashAttention implementation. SDPA efficient achieves 130× memory reduction and 10× speedup; Triton FA achieves O(n) memory but is 64× slower than SDPA efficient on RTX 2070.

Hand-built MoE expert parallelism: routing-skew capture, fused grouped-GEMM Triton kernels, 2-all2all dispatch/combine, overlap ablations — proving by counter-example why DeepEP exists.

Profiling and Triton-based KV-cache optimization for protein language model inference on consumer GPUs.

Long-context benchmark pushing Qwen2-0.5B from 4K to 32K tokens on RTX 2070 using SDPA + chunked prefill. Shows 40x speedup at 8K, FP16 beating INT4 at long context, and that quantization is NOT a long-context solution — KV-cache is the real bottleneck.