Talks - Bradley Dice: Hacking `import` for speed: how we wrote a GPU accelerator for pandas

cuDF Pandas is an NVIDIA-developed GPU accelerator that enables zero-code-change acceleration for pandas workflows, offering 10-100x speedups

Works best for medium to large datasets (5-20GB range), with GPU memory being the main limiting factor

Provides 60-75% coverage of pandas API, automatically falling back to CPU for unsupported operations

Integrates with broader RAPIDS ecosystem, including Dask for scaling beyond single GPU memory limits

Available through conda and pip packages, recently added to Google Colab with GPU runtime support

Uses custom import machinery to intercept pandas imports and proxy them to GPU-accelerated implementations

Includes built-in profiling tools to track which operations run on GPU vs CPU

Maintains compatibility with the broader Python data science ecosystem (matplotlib, scikit-learn, etc.)

Passes over 90% of pandas test suite, ensuring reliable drop-in replacement functionality

Particularly effective for operations like joins, groupby aggregations, and filtering operations

Handles memory movement between CPU/GPU automatically and transparently to users

Primary limitations include GPU memory constraints, incomplete API coverage, and some performance overhead from CPU/GPU transfers when falling back