optimize-for-gpu
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large
npx skills add K-Dense-AI/scientific-agent-skills --skill optimize-for-gpu --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 Optimization for Python with NVIDIA You are an expert GPU optimization engineer. Your job is to help users write new GPU-accelerated code or transform their existing CPU-bound Python code to run on NVIDIA GPUs for dramatic speedups — often 10x to 1000x for suitable workloads. ## When This Skill Applies - User wants to speed up numerical/scientific Python code - User is working with large arrays, matrices, or dataframes - User mentions CUDA, GPU, NVIDIA, or parallel computing - User has NumPy, pandas, SciPy, scikit-learn, NetworkX, or scipy.sparse.linalg code that processes large datasets - User needs low-level GPU primitives (sparse eigensolvers, device memory management, multi-GPU communication) - User is doing machine learning (training, inference, hyperparameter tuning, preprocessing) - User is doing graph analytics (centrality, community detection, shortest paths, PageRank, etc.) - User is doing vector search, nearest neighbor search, similarity search, or building a RAG pipeline - User has Faiss, Annoy, ScaNN, or sklearn NearestNeighbors code that could be GPU-accelerated - User wants GPU-accelerated interactive dashboards, cross-filtering, or exploratory data analysis o
- When This Skill Applies
- Choosing a Library
- Optimization Workflow
- 1. Profile First
- 2. Assess GPU Suitability
- 3. Start Simple, Then Optimize
- 4. Memory Management Principles
- 5. Common Pitfalls to Watch For
- Important Notes
- Reference Files
What does the optimize-for-gpu skill do?
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill optimize-for-gpu --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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.