Ashwin Srinath - cudf.pandas: The Zero Code Change GPU Accelerator for Pandas | PyData Global 2023

cudf.pandas allows running Pandas code on GPUs without code changes by using a proxy pattern that intercepts Pandas calls

Current support covers 60-75% of Pandas API with 94% test suite passing - unsupported operations fallback gracefully to CPU

Performance improvements of 10-100x are possible for supported operations, though actual speedups vary by operation type:

• String operations: 9ms vs 3000ms
• Merge operations: 1s vs 14s
• Group by operations: 50ms vs 1000ms+

Key limitations:

• Only provides GPU acceleration (no CPU performance improvements)
• Requires minimizing CPU fallbacks for best performance
• Data movement between CPU/GPU can create overhead
• Not all third-party libraries compatible due to type checking

Best practices for using cudf.pandas:

• Use built-in profiler to identify bottlenecks
• Minimize operations that require CPU fallback
• Write code using idiomatic Pandas patterns
• Consider data size vs GPU memory constraints

Installation available via Conda, Pip and Docker with support for:

• Jupyter notebooks
• Google Colab
• Stand-alone Python scripts

Compatible with existing Pandas-based libraries and tools through proxy pattern, allowing gradual adoption without full codebase changes

Particularly useful for:

• Large datasets
• Performance-critical applications
• Existing Pandas codebases needing acceleration
• Teams wanting to maintain Pandas API compatibility