udf-benchmark
Assists with benchmarking and profiling the performance of an Apache Spark UDF on the GPU. This is step 3 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and a RapidsUDF or SQL implementation, and need to benchmark the performance of the CPU UDF against the GPU implementation.
ソース情報
- リポジトリ
- NVIDIA/cudf-spark
- ソースの最終更新活動
- 2026年6月22日 23:17
- 検出された SKILL.md の言語
- 英語
- スター
- 1,010
- フォーク
- 311
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SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- udf-benchmark
- description
- Assists with benchmarking and profiling the performance of an Apache Spark UDF on the GPU. This is step 3 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and a RapidsUDF or SQL implementation, and need to benchmark the performance of the CPU UDF against the GPU implementation.
- license
- CC-BY-4.0 AND Apache-2.0
- metadata
- {"spdx-file-copyright-text":"Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved."}
- model
- inherit
# UDF Benchmark
## Workflow
- [ ] Step 1: Implement BenchUtils (fill in TODO methods)
- [ ] Step 2: Validate with a small dataset
- [ ] Step 3: Generate full benchmark data and run benchmarks
- [ ] Step 4: cuDF microbenchmarks (skip for SQL targets)
**Before making any edits, create a visible TODO checklist for every workflow step in this skill and keep it updated.** Do not produce a final answer until every required checklist item is marked complete.
## Prerequisites
- Project directory from Steps 1-2 (udf-gen-test, udf-convert-to-*) with passing tests
Derive `<CamelName>` and `<snake_name>` from the UDF class name.
> **Note:** Commands require access to `/tmp` (Spark temp storage) and `/dev` (GPU device). If commands fail due to sandbox restrictions, re-run them unsandboxed.
## Step 1: Implement BenchUtils
Read `src/main/scala/com/udf/bench/BenchUtils.scala`. Replace placeholders with the actual camel/snake UDF name.
Fill in the TODO methods following the docstrings. For variable-length inputs, generate sizable rows representative of enterprise-scale data. Refer to the unit test for schema and example data.
## Step 2: Validate
Make scripts executable:
```bash
chmod +x *.sh
```
Run validation mode to test with a small dataset:
```bash
./run_gen_data.sh --rows 1000 --validate
```
This runs both the CPU and GPU implementations on the dataset.
If validation fails, analyze the error and fix the BenchUtils implementation.
## Step 3: Generate Data and Run Benchmarks
The scripts set the default heap size to 16g in `.mvn/jvm.config`; adjust depending on data size.
### Generate benchmark data (10M rows):
```bash
./run_gen_data.sh --rows 10000000
```
### Run benchmarks:
```bash
# CPU benchmark
./run_spark_benchmark.sh --mode cpu --data-path data/bench_data_10000000_rows.parquet
# GPU benchmark
./run_spark_benchmark.sh --mode gpu --data-path data/bench_data_10000000_rows.parquet
```
Results are saved to the `results/` directory as JSON files.
## Step 4: cuDF Microbenchmarks
> Skip this step for SQL targets. This only applies to cuDF RapidsUDF conversions.
Follow [CUDF_MICROBENCHMARKS.md](CUDF_MICROBENCHMARKS.md) to implement and run in-memory microbenchmarks.
## Output
Upon successful completion:
- Benchmark utilities: `src/main/scala/com/udf/bench/BenchUtils.scala`
- Microbenchmarks (cuDF): `src/main/scala/com/udf/bench/MicroBenchRunner.scala`
- Generated data: `data/`
- Benchmark results: `results/`
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