Looking for feedback on my GPU-accelerated Spark pipeline

[adil-faiyaz98]

Hey everyone,

I’ve been working on a proof-of-concept project that integrates the RAPIDS Accelerator with Apache Spark 3 to showcase the performance gains you can achieve using GPUs. It covers real-world workloads like ETL, Spark SQL, and XGBoost pipelines, and includes benchmarks comparing CPU vs GPU performance.

Repo: https://github.com/adil-faiyaz98/accelerated-spark-gpu

I would really appreciate any feedback whether it’s on the code structure, implementation approach, integration with RAPIDS, or anything I could improve. If you've worked with RAPIDS or GPU-accelerated workloads in Spark, I'd love to hear your thoughts.

[nvliyuan]

Hi @adil-faiyaz98, thanks for trying the plugin. We hope your pipeline will gain significant performance benefits from Spark-Rapids. Please let us know if you encounter any performance issues. For the code structure, implementation approach, and integration with RAPIDS, I assume AI analysis is a good starting point. Here I shared the result generated by the cursor, I hope it could help.

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1. Code Structure

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Strengths:

• Multi-module layout (core, models, project, reports, etc.) is a good practice for separation of concerns.
• Sample data, reports, and scripts are included, which helps onboarding.

Suggestions:

• Add per-module READMEs: Each module (e.g., core, models) should have a short README.md describing its purpose, main classes, and how it fits into the overall pipeline.
• Clarify directory roles: Some folders (like project, modules) could be more self-explanatory or documented.
• Consistent naming: Use consistent naming conventions for files and folders (e.g., all-lowercase, hyphens vs underscores).
• Flatten where possible: If some modules are thin wrappers, consider merging or flattening to reduce complexity.

2. Implementation Approach

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Strengths:

• End-to-end pipeline: Covers ETL, ML, and reporting, which is great for real-world demonstration.
• Cross-platform scripts: Both run.sh and run.bat are provided.

Suggestions:

• Parameterize workloads: Allow users to specify data size, number of partitions, and GPU settings via config files or command-line arguments, rather than hardcoding.
• Decouple data generation: Make it easy to swap in user-provided datasets, not just the included samples.
• Improve error handling: Ensure all Spark and RAPIDS operations have robust error handling and logging. Use try/catch blocks and log errors with context.
• Add more tests: Expand on sbt test with more unit and integration tests, especially for GPU-specific logic and performance regressions.
• Use Spark best practices: Cache/persist only when needed. Avoid wide transformations before shuffles. Use partitioning wisely for GPU workloads.

3. Integration with RAPIDS

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Strengths:

• Uses RAPIDS Spark plugin: Correctly sets spark.plugins=com.nvidia.spark.SQLPlugin and other RAPIDS configs.
• Demonstrates real GPU acceleration: Shows clear CPU vs GPU comparisons.

Suggestions:

• Document RAPIDS configs: In your sample configs, comment on what each RAPIDS setting does (e.g., spark.rapids.sql.enabled, spark.rapids.memory.gpu.allocFraction).
• Expose more RAPIDS tuning options: Let users easily adjust memory pool sizes, batch sizes, and other RAPIDS-specific parameters.
• Add RAPIDS version checks: At runtime, print the detected RAPIDS and CUDA versions for easier debugging.
• Showcase more RAPIDS features: Consider adding examples for UDFs, window functions, or MLlib integration with RAPIDS.

4. General Improvements

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Documentation:

• Add usage examples: Show how to run a specific ETL or ML pipeline, and how to interpret the results.
• Add a contributing guide: Explain how to submit issues/PRs, coding standards, and review process.
• Add badges: For build status, license, and supported Spark/RAPIDS versions.

Usability:

• Provide Docker Compose: For multi-service setups (e.g., Spark master/worker, Jupyter, etc.).
• Automate environment setup: Provide a script or Makefile to automate environment setup, dependency installation, and data download.
• Add cloud deployment docs: Show how to run on AWS EMR, Databricks, or GCP Dataproc with GPU nodes.

Performance and Monitoring:

• Automate benchmarking: Add scripts to run benchmarks and collect results automatically, possibly with different data sizes and cluster configurations.
• Visualize results: Enhance the reporting module to generate visualizations (charts/graphs) comparing CPU vs GPU performance.
• Add monitoring hooks: Integrate tools like NVIDIA Nsight, Spark UI, or custom metrics to monitor GPU utilization and memory usage.

Code Quality:

• Use linters and formatters: Enforce code style with tools like Scalafmt for Scala, and add style checks to CI.
• Improve logging: Use SLF4J/log4j for consistent, configurable logging across modules.

Concrete Action Items Table

Area	Action Item
Code Structure	Add per-module READMEs, clarify directory roles, use consistent naming, flatten if possible
Implementation	Parameterize workloads, decouple data gen, improve error handling, add more tests
RAPIDS Integration	Document configs, expose tuning options, add version checks, showcase more RAPIDS features
Documentation	Add usage/contributing examples, badges, cloud deployment docs
Usability	Provide Docker Compose, automate setup, allow user datasets
Performance	Automate benchmarks, visualize results, add monitoring
Code Quality	Use linters, improve logging, enforce style in CI

References & Examples

• RAPIDS Accelerator for Apache Spark Docs: https://nvidia.github.io/spark-rapids/
• NVIDIA spark-rapids-examples: https://github.com/NVIDIA/spark-rapids-examples
• RAPIDS Spark config guide: https://nvidia.github.io/spark-rapids/docs/configs.html

These suggestions will help make the repo more robust, user-friendly, and production-ready.

[adil-faiyaz98]

I wasn't aware that cursor does provide indepth code review feedback like the above. It definitely seems helpful to improvise. Thanks for sharing !