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.

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.

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

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

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

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.

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.

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

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.

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.

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

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.

"The GPU Watchers swore upon their shared memory hierarchy, from L1 to global memory, which also served as their mandate as lords of parallel computation."

Real implementation of speculative decoding showing that speedup is strongly prompt- and target-dependent: GPT-2 → GPT-2-medium achieves mean best speedup 1.013×, while GPT-2 → GPT-2-large reaches 1.253× and up to 1.846× on high-agreement prompts.

C++23 Vulkan renderer for glTF/BIM/USD scenes with PBR materials, render graph, GPU culling, telemetry, and debug visualization.