API Doc for Polars GPU Engine

docs/cudf/source/cudf_polars/index.rst

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+cuDF-based GPU backend for Polars [Open Beta]
+=============================================
+
+cuDF supports an in-memory, GPU-accelerated execution engine for Python users of the Polars Lazy API. 
+The engine supports most of the core expressions and data types as well as a growing set of more advanced dataframe manipulations 
+and data file formats. When using the GPU engine, Polars will convert expressions into an optimized query plan and determine 
+whether the plan is supported on the GPU. If it is not, the execution will transparently fall back to the standard Polars engine 
+and run on the CPU. 
+
+Benchmark
+---------
+We reproduced the `Polars Decision Support (PDS) <https://github.com/pola-rs/polars-benchmark>`_ benchmark to compare Polars GPU engine with the default CPU settings across several dataset sizes. Here are the results:
+
+.. figure:: ../_static/pds_benchmark_polars.png
+   :width: 600px
+
+
+The per-query speedups using Polars GPU Engine for the 80 GB dataset are below:
+
+
+.. figure:: ../_static/Polars_GPU_speedup_80GB.png
+   :width: 1000px
+
+You can reproduce the results by visiting the `Polars Decision Support (PDS) Github repository. <https://github.com/pola-rs/polars-benchmark>`_
+
+Learn More
+----------
+
+The GPU backend for Polars is now available in Open Beta and the engine is undergoing rapid development. To learn more, visit the `GPU Support page <https://docs.pola.rs/user-guide/gpu-support/>`_ on the Polars website.
+
+Launch on Google Colab
+----------------------
+
+.. figure:: ../_static/colab.png
+   :width: 200px
+   :target: https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/accelerated_data_processing_examples/polars_gpu_engine_demo.ipynb
+
+   Take the cuDF backend for Polars for a test-drive in a free GPU-enabled notebook environment using your Google account by `launching on Colab <TBD>`_