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optimize-for-gpu

Transform CPU-bound Python numerical code into CUDA-accelerated versions.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#python#optimization#gpu#cuda#cupy#numba
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill optimize-for-gpu-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: optimize-for-gpu
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/optimize-for-gpu
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill optimize-for-gpu-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GPU-accelerate Python code by transforming CPU-bound numerics to CUDA-accelerated versions, unlocking substantial speedups on compatible NVIDIA hardware.

Core Features & Use Cases

  • Profiling-first optimization: identify hotspots with CPU/GPU profiling and map them to GPU-accelerated variants.
  • Library guidance: select among CuPy, Numba CUDA, Warp, cuDF/cuML/cuGraph based on workload.
  • End-to-end patterns: provide transformation patterns for array ops, dataframe workflows, and simulations.
  • Use Case: accelerate a NumPy-heavy data pipeline by replacing hot loops with Warp kernels and CuPy vectorized ops to achieve 10x–1000x improvements.

Quick Start

Install the required GPU libraries and begin by profiling the code, then replace CPU-bound sections with CuPy/Numba/Warp equivalents and run on CUDA.

Frequently Asked Questions about optimize-for-gpu

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to GPU-accelerate Python code for large array workloads?▼

To GPU-accelerate Python code for large array workloads, use CuPy for drop-in NumPy replacement or Numba CUDA for custom kernels, achieving 10x–1000x speedups on NVIDIA hardware.

How do I profile CPU-bound Python code before converting it to CUDA acceleration?▼

Use a profiling-first workflow to identify CPU hotspots before applying CUDA acceleration, mapping the most time-consuming loops and array operations to their GPU-accelerated equivalents.

When should I use CuPy versus Numba CUDA for optimizing Python numerical code?▼

Use CuPy for drop-in NumPy array replacements and Numba CUDA for writing custom GPU kernels, selecting the library based on whether your workload needs standard vectorized ops or custom simulation logic.

Does this GPU optimization approach work with dataframe workflows and ML pipelines?▼

Yes, GPU optimization extends to dataframe workflows and ML pipelines by prescribing cuDF, cuML, and cuGraph libraries to accelerate data-processing tasks alongside standard array operations.

Do I need specific NVIDIA hardware to run CUDA-accelerated Python code?▼

Yes, CUDA acceleration requires compatible NVIDIA hardware to execute the transformed GPU-accelerated Python code and unlock substantial processing speedups.

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