With 5 GB of data, pandas performance slows to a crawl, taking minutes to perform the series of join and advanced groupby operations. DuckDB’s benchmark setup compares popular CPU-based DataFrame and SQL engines on a series of common analytics tasks such as joining data together or computing statistical measures on a per-group basis. You can see this in action by running the pandas portion of the popular DuckDB Database-like Ops Benchmark originally developed by H2o.ai. With the new RAPIDS cuDF, you can keep using pandas as your primary tool and access the highest performance. To bring GPU acceleration into your pandas workflows in a Jupyter notebook, load the cudf.pandas extension:īringing top performance to pandas workflowsĪs data sizes scale into the gigabytes, using pandas often becomes challenging due to slower performance, causing some data scientists to grudgingly give up the pandas API they love. Unified CPU/GPU workflows: Develop, test, and run in production with a single code path, regardless of hardware.It will even accelerate pandas operations within these libraries. Third-party library compatibility: pandas accelerator mode is compatible with most third-party libraries that operate on pandas objects.Zero code change acceleration: Just load the cuDF Jupyter Notebook extension or use the cuDF Python module option.With the latest release, cuDF now provides the following features: This enables a unified CPU/GPU experience that brings best-in-class performance to your pandas workflows. In cuDF’s pandas accelerator mode, operations execute on the GPU where possible and on the CPU (using pandas) otherwise, synchronizing under the hood as needed. This feature was built for data scientists who want to continue using pandas as data sizes grow into the gigabytes and pandas performance slows. Starting with the RAPIDS v23.10 release, cuDF now provides a pandas accelerator mode to address these challenges, in addition to the existing GPU-only experience. Manually switching between cuDF and pandas when interacting with other PyData libraries or organization-specific tooling designed for pandas.Designing separate code paths for CPU and GPU execution in codebases that require running on heterogeneous hardware.Working around any pandas functionality not yet implemented or supported in cuDF.However, adopting cuDF has sometimes required workarounds: Bringing a unified CPU/GPU experience to pandas workflowsĬuDF has always provided users with top DataFrame library performance using a pandas-like API. In the video, you can see identical pandas workflows running side-by-side: one uses pandas with CPU-only and the other uses pandas accelerator mode in RAPIDS cuDF. Accelerate Pandas by Nearly 150X with RAPIDS cuDF
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