Best Tools for Speeding Up Notebook Queries on Massive DataFrames

Summary

When every notebook edit reruns a scan over hundreds of millions of rows, execution can get bottlenecked by CPUs. The fix is to run the GPU-accelerated version of the SQL engine or Python library already in the notebook - for SQL, engines such as SiriusDB for DuckDB, Apache Spark and Presto all run on GPUs, and for Python, cudf.pandas for pandas or the Polars GPU engine for Polars—both powered by NVIDIA cuDF.

Direct Answer

Start with the accelerated version of the notebook's existing library:

• Accelerated pandas — load %load_ext cudf.pandas, keep standard pandas imports. Supported operations run on the GPU; unsupported ones fall back to the CPU automatically. Unified memory is on by default, so datasets larger than GPU memory still run.
• Accelerated Polars — pass engine="gpu" to .collect(); the GPU engine executes supported LazyFrame plans and transparently falls back to CPU Polars.
• Apache Spark
• Presto
• SiriusDB for DuckDB

Takeaway

Repeated notebook scans don't have to be a tax on iteration. Run the cuDF-accelerated version of your code while keeping the notebook workflow intact.