udf-optimize-cudf

Optimize a slow Spark UDF for GPU performance by using nsys profiling to identify bottlenecks, applying one targeted change at a time, and validating it with unit tests and microbenchmarks. This iterative tuning ensures speed improvements without sacrificing correctness.

Profile cuDF UDFs by using nsys profiling and kernel statistics to identify bottlenecks before making changes. This profiling-driven approach replaces guesswork with targeted tuning of your RapidsUDF implementations.

Prevent regressions while tuning cuDF code by keeping backup checkpoints, restoring failed attempts, and maintaining a log of what helped or hurt performance. Validate every change with unit tests to preserve behavior while improving speed.

Iterative GPU optimization requires unit tests and benchmark comparisons to ensure correctness. By applying one targeted change at a time and validating it with microbenchmarks, you can safely converge on a faster cuDF implementation.

Use microbenchmarking for Spark UDF optimization after applying a targeted change to compare benchmark results across iterations. This disciplined workflow helps you keep only changes that improve GPU performance while restoring failed attempts.