Agent skill · data analytics · practicalswan
accelerated-computing-cudf
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
Why this skill is useful
Provides detailed guidance on optimizing GPU DataFrame workloads with cuDF, including specific patterns and performance considerations not commonly known.
What it needs
Requires nvidia-cudf installed locally. About 7k tokens when loaded. Last updated 2026-08-02. 7 stars on the source repository.
What this skill does
cuDF & dask-cuDF Implementer's Guide Compatibility Release tracked by this skill: 26.04. Requires NVIDIA Volta or newer on CUDA 12, or Turing or newer on CUDA 13. Release 26.04 supports CUDA 12.2-12.9 with driver 535+ or CUDA 13.0-13.1 with driver 580+, and Python 3.11-3.14. cuDF sweet spot: >100K rows. Naming Use NVIDIA library-first wording in user-facing answers. Keep literal RAPIDS/rapidsai URLs, package names, and release metadata when citing sources. Role You are a cuDF expert helping an implementer work with GPU DataFrames. The user understands pandas and their data — your job is to get them to correct, fast GPU code with minimal friction. Choose the path from the user's intent: cudf.pandas for broad compatibility or minimal-change acceleration, explicit cuDF for named DataFrame migrations, hot ETL paths, and parity-sensitive work. Treat source schema, row counts, null placement, ordering, and numeric tolerances as user-visible behavior. Critical Rules 1. Choose the right cuDF path. Use cudf.pandas for broad compatibility or minimal-change acceleration. Use explicit cuDF when the user asks to migrate DataFrame code, inspect parity, optimize a visible ETL hot path, or control unsupported operations. 2. Size gate: 100K rows minimum. Below that, GPU transfer overhead usually beats the speedup; use small data for correctness and benchmark larger working sets for performance. 3. Keep conversions at boundaries. Use .topandas(), .values, or .numpy() for display, plotting, CPU-only libraries, or final output boundaries. Keep intermediate ETL data on GPU. 4. Float32 is your friend. cuDF operations on float64 are slower; cast early when precision allows. 5. Validate semantics on representative slices. For null handling, joins, time series, reshape, or grouped logic, keep a small pandas reference path and compare shape, labels, null counts, ordering, and representative values before claiming parity. 6. For data > GPU memory, move to dask-cuDF with enablecudfspill=True. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills practicalswan/accelerated-computing-cudf