optimize-for-gpu
Category: data-science-and-ml · License: MIT · Version: 1.4
Tags: GPU acceleration, CUDA, performance optimization, scientific Python
When to use it
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.Example requests
Ask your agent in plain language, for example:- “My numpy simulation loop is crawling on 50M-element arrays. Can I put it on my RTX GPU?”
- “I have a 40 GB pandas script,
merge_cohorts.py, that does groupby/merge on UK Biobank phenotype tables and takes 3 hours on one A100 box. I triedimport cudfdirectly and gotMemoryError: std::bad_allocfrom RMM. How should I speed this up?” - “We run a nearest-neighbor lookup over about 20 million protein embeddings with Faiss on CPU and queries take seconds. We’d like them much faster, and we need results close to what we get now. What are our options?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.