Accelerating Sequential Python User-Defined Functions with RAPIDS on GPUs for 100X Speedups – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-03T22:20:47Z http://www.open-lab.net/blog/feed/ Vibhu Jawa <![CDATA[Accelerating Sequential Python User-Defined Functions with RAPIDS on GPUs for 100X Speedups]]> http://www.open-lab.net/blog/?p=32421 2022-08-21T23:51:47Z 2021-06-07T15:00:00Z Motivation Custom ��row-by-row�� processing logic (sometimes called sequential User-Defined Functions) is prevalent in ETL workflows. The sequential nature of...]]> Motivation Custom ��row-by-row�� processing logic (sometimes called sequential User-Defined Functions) is prevalent in ETL workflows. The sequential nature of...

Custom ��row-by-row�� processing logic (sometimes called sequential User-Defined Functions) is prevalent in ETL workflows. The sequential nature of UDFs makes parallelization on GPUs tricky. This blog post covers how to implement the same UDF logic using RAPIDS to parallelize computation on GPUs and unlock 100x speedups. Typically, sequential UDFs revolve around records with the same��

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