/accelerated-computing-cudf

name: accelerated-computing-cudf
description: 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.
license: CC-BY-4.0 AND Apache-2.0
metadata:
  author: NVIDIA
  tags:
    - cudf
    - dataframes
    - pandas
    - dask-cudf
    - etl

cuDF & dask-cuDF Implementer's Guide

Compatibility

• Development release tracked by this skill: 26.12 (`VERSION`: `26.12.00`). Use the selected installed release for deployment requirements.
• Read `dependencies.yaml` for the support matrix (`py_version` group for Python versions, `cuda_version` for CUDA versions). Match cuDF, pylibcudf, libcudf, and RMM release versions, and match pip wheel suffixes (`-cu12` / `-cu13`) to the CUDA major version.
• Requires a supported NVIDIA GPU and compatible driver. Check the [installation requirements](https://docs.nvidia.com/datascience/install/#system-req) for the selected release and CUDA version.
• For another checkout or installed release, check `VERSION`, `dependencies.yaml`, `python/cudf/pyproject.toml`, and `cudf.__version__` before choosing versions or relying on an API.

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. **Benchmark the working set.** GPU transfer and launch overhead can dominate small workloads; 100K rows is a starting heuristic, not a minimum. Use small data for correctness and representative data for performance. 3. **Keep conversions at boundaries.** Use `.to_pandas()` or `.to_numpy()` for CPU-only libraries, display, or final output boundaries. `.values` and `.to_cupy()` return GPU arrays, not NumPy arrays. Keep intermediate ETL data on GPU. 4. **Choose precision deliberately.** Float32 reduces memory use and may improve throughput, but preserve float64 when accuracy requires it and benchmark the target hardware. 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 `enable_cudf_spill=True`. See `references/dask-cudf-patterns.md`.

Three Paths to GPU DataFrames

Path 1: cudf.pandas Accelerator (Compatibility / Minimal Change)

Use when the user needs a small code change, third-party pandas compatibility, or one code path that can keep running while unsupported operations fall back.

**Jupyter/IPython:**

%load_ext cudf.pandas
import pandas as pd   # now GPU-backed; falls back silently for unsupported ops

**Script:**

python -m cudf.pandas my_script.py

**With multiprocessing:**

import cudf.pandas
cudf.pandas.install()   # must come BEFORE pandas import, before Pool creation
from multiprocessing import Pool

Confirm acceleration with the cudf.pandas profiler before claiming speedup. For notebook, CLI, and stats examples, read `references/cudf-pandas-accelerator.md`. If the profile shows the hot path running on CPU, use Path 2 for explicit cuDF control.

Path 2: Explicit cuDF API

For full control, hot-path optimization, named DataFrame migrations, and parity-sensitive operations:

import cudf

# Read data directly to GPU
df = cudf.read_parquet("data.parquet")

# Operations mirror pandas
result = df.groupby("key")["value"].sum()
merged = df.merge(lookup, on="id", how="left")
filtered = df[df["amount"] > 1000]

# String operations
df["clean"] = df["name"].str.strip().str.lower()

# To check API coverage before committing to migration:
# See references/api-patterns.md for known gaps and workarounds

**Keep data on GPU end-to-end.** Only call `.to_pandas()` at the very end for display or CPU or non-GPU handoff.

Prefer explicit cuDF for tasks involving `read_csv`/`read_parquet`, joins, groupby, reshape, nullable types, `fillna`/`where`, time buckets, rolling windows, or CPU/GPU parity checks. Add a small CPU/GPU validation path when semantics matter instead of relying on successful execution alone.

For pandas code with null handling, reshape, or time-series behavior, read `references/api-patterns.md` for the relevant semantic checklist before rewriting. A `cudf.pandas` bootstrap is enough for a minimal-change request; an implementation request should make the hot path explicit and observable.

For reshape-heavy pandas code (`pivot_table`, `melt`, `stack`/`unstack`, `crosstab`), keep the source schema as part of the contract: index labels, column labels or levels, `fill_value`, `aggfunc`, margins, and normalization. Use explicit cuDF where the equivalent is supported; use `cudf.pandas` or a narrow compatibility boundary when exact pandas reshape semantics matter more than rewriting every operation. Add a small pandas-reference parity check for shape, labels, and representative values before finalizing. See `references/api-patterns.md`.

Path 3: dask-cuDF (Multi-GPU / Large Data)

When dataset exceeds GPU memory. See `references/dask-cudf-patterns.md` f