How to Rewrite Apache Spark UDFs for GPU Acceleration with AI Agents | NVIDIA Technical Blog

Enterprise applications have become increasingly demanding. Agentic AI has compounded the need for vast amounts of enterprise data with frequent, complex queries across increasingly large datasets.

Meeting that demand requires greater data throughput without a proportional increase in cost. NVIDIA cuDF addresses this challenge by leveraging the massive parallelism and high-bandwidth memory of GPUs for data analytics. Particularly, the NVIDIA cuDF plugin for Apache Spark brings this acceleration to Apache Spark, the industry-leading platform for big data processing and analytics, to achieve better TCO performance in major workloads.

User-defined functions (UDFs) have long been problematic for accelerated query engines, introducing runtime overheads due to their opaque nature. While UDFs can often be replaced with more performant code, rewriting UDFs has long been a laborious process requiring careful validation.

This post describes how to use agents to rewrite UDFs into GPU-accelerated equivalents within minutes, achieving 22x+ speedups in Apache Spark jobs. We’ll introduce the cuDF skills for Apache Spark, a set of skills to quickly break through the UDF bottleneck for end-to-end development, from testing, to benchmarking, through integration.