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optimize-for-gpu

Port CPU-bound Python code to GPU-accelerated RAPIDS libraries.

1|Updated Apr 8, 2026
Search Tags:#python#pandas#gpu#graph#numpy#rapids#acceleration
One-click install
npx skills add https://github.com/brainworkup/skills --skill optimize-for-gpu-brainworkup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: optimize-for-gpu
Source: https://github.com/brainworkup/skills/tree/main/neuropsych-reports/references/luria-related-complement-skills/optimize-for-gpu
Command: npx skills add https://github.com/brainworkup/skills --skill optimize-for-gpu-brainworkup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

GPU acceleration for Python workloads across data processing, ML, visualization, and scientific computing by leveraging RAPIDS libraries to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, Faiss workloads.

Core Features & Use Cases

  • Guidance on when to apply CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT to accelerate CPU-bound code and large pipelines.
  • 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.
  • Includes migration patterns and interoperability tips to port existing code from NumPy/pandas/NetworkX to GPU-accelerated equivalents.

Quick Start

Select RAPIDS components (CuPy, Warp, cuDF, cuML, cuGraph, etc.) and progressively port CPU-bound code to GPU paths.

Frequently Asked Questions about optimize-for-gpu

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

FAQPage Schema
How do I port pandas and NumPy code to GPU acceleration?▼

Port pandas and NumPy workloads to GPU acceleration by replacing them with RAPIDS equivalents like cuDF and CuPy. This provides GPU-accelerated execution for data processing and scientific computing workflows.

What is needed to run RAPIDS libraries for Python GPU acceleration?▼

RAPIDS libraries require CUDA-enabled GPUs and compatible RAPIDS versions. You select the necessary components and progressively port CPU-bound code to the corresponding GPU paths.

Can I use RAPIDS to accelerate NetworkX graph processing?▼

Yes, you can accelerate NetworkX graph processing using cuGraph. It provides GPU-accelerated equivalents for network analysis and visualization workflows within the RAPIDS ecosystem.

What's the best way to speed up scikit-learn machine learning models?▼

Speed up scikit-learn models by migrating them to cuML. This RAPIDS library provides GPU-accelerated equivalents to execute machine learning algorithms significantly faster.

Does RAPIDS support geospatial analysis and medical imaging?▼

RAPIDS supports geospatial analysis and medical imaging through cuSpatial and cuCIM. These libraries provide GPU-accelerated execution for geospatial workflows and medical image processing.

When should I use CuPy or Numba CUDA for physics simulation workloads?▼

Use CuPy and Numba CUDA for physics simulation workloads to enable GPU-accelerated execution. They handle differentiable rendering, mesh ray casting, and particle systems for complex pipelines.

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