Migrate Apache Spark Workloads to GPUs at Scale on Amazon EMR with Project Aether

2025-12-17 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

NVIDIA's Project Aether is a new tool designed to automate the migration and optimization of existing CPU-based Apache Spark workloads to GPU-accelerated Amazon Elastic MapReduce (EMR) using the RAPIDS Accelerator. This suite of microservices aims to reduce migration time by providing a prediction model for GPU speedup, out-of-the-box testing and tuning in a sandbox environment, smart optimization for cost and runtime, and full integration with Amazon EMR. The migration process involves four phases: Predict, which uses the QualX machine learning system's XGBoost model to assess GPU viability; Optimize, which iteratively tests and tunes Spark configurations on a GPU cluster; Validate, which confirms data integrity by comparing output metrics; and Migrate, which generates detailed reports and recommendations. An automated run command combines these steps for streamlined execution.

Key takeaway

For MLOps Engineers managing big data processing on AWS EMR, Project Aether offers a direct path to significant performance and cost improvements. You should consider adopting this tool to automate the transition of your existing CPU-based Apache Spark workloads to GPU-accelerated EMR. This will streamline your migration efforts, reduce cloud expenditure, and free up development hours, allowing your team to focus on higher-value tasks.

Key insights

Project Aether automates migrating CPU Spark workloads to GPU-accelerated EMR, optimizing performance and cost.

Principles

• Automate migration to reduce friction
• Optimize for both performance and cost
• Validate data integrity post-migration

Method

Project Aether's migration workflow includes four phases: Predict (qualify job for GPU), Optimize (test/tune on GPU cluster), Validate (check data integrity), and Migrate (report recommendations).

In practice

• Use `aether qualify` to predict GPU speedup
• Employ `aether tune` for iterative optimization
• Run `aether validate` to confirm output integrity

Topics

• Project Aether
• GPU-accelerated Spark
• Amazon EMR
• RAPIDS Accelerator
• Workload Migration

Best for: Machine Learning Engineer, Data Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.