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

Data is the fuel of modern business, but relying on older CPU-based Apache Spark pipelines introduces a heavy toll. They’re inherently slow…

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NVIDIA
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Navin Kumar
•6 min read•advanced•
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Overview

The article discusses Project Aether, a tool developed by NVIDIA to facilitate the migration of CPU-based Apache Spark workloads to GPU-accelerated environments on Amazon EMR. It highlights the benefits of GPU acceleration, including improved performance and reduced cloud costs, while providing a detailed workflow for the migration process.

What You'll Learn

1

How to migrate existing CPU-based Spark workloads to GPU-accelerated environments using Project Aether

2

Why GPU acceleration is beneficial for Apache Spark workloads

3

How to configure Aether for use with Amazon EMR

4

When to use the Predict, Optimize, Validate, and Migrate phases in Aether

Prerequisites & Requirements

  • Amazon EMR on EC2 with GPU instance quotas
  • AWS CLI configured with aws configure
  • Aether NGC access and configuration

Key Questions Answered

How does Project Aether automate the migration of Spark workloads?
Project Aether automates the migration of CPU-based Spark workloads to GPU-accelerated environments by using a suite of microservices that eliminate manual processes. It includes features like a prediction model for GPU speedup, out-of-the-box testing, smart optimization, and full integration with Amazon EMR workloads.
What are the core phases of the Aether migration workflow?
The Aether migration workflow consists of four core phases: Predict, Optimize, Validate, and Migrate. Each phase focuses on different aspects of the migration process, from determining GPU compatibility to optimizing job performance and ensuring data integrity.
What prerequisites are needed to use Project Aether?
To use Project Aether, you need an AWS account with GPU instance quotas, the AWS CLI configured, and access to Aether NGC. These prerequisites ensure that users can effectively set up and utilize the tool for migrating Spark workloads.
How does the validation phase ensure data integrity in GPU jobs?
The validation phase in Project Aether checks the integrity of GPU job outputs by comparing key metrics, such as rows read and rows written, between the GPU run and the original CPU run. This ensures that the results are consistent and accurate post-migration.

Technologies & Tools

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Backend
Apache Spark
Used for processing large-scale data workloads.
Cloud Service
Amazon Emr
Provides a managed Hadoop framework for processing big data.
Backend
Rapids Accelerator
Optimizes Apache Spark workloads for GPU acceleration.

Key Actionable Insights

1
Leverage Project Aether to significantly reduce migration time for Spark workloads from CPU to GPU. This tool automates many of the manual processes involved, allowing for faster deployment and optimization.
Using Aether can lead to substantial cost savings and improved performance, making it a valuable asset for teams looking to enhance their data processing capabilities.
2
Utilize the prediction model in Aether to assess the potential speedup of your Spark jobs before migration. This can help in making informed decisions about which workloads to prioritize for GPU acceleration.
Understanding the expected performance gains can guide resource allocation and project planning, ensuring that the most impactful workloads are addressed first.
3
Ensure proper configuration of the Aether client for EMR to streamline the migration process. Following the setup instructions carefully will minimize errors and enhance the efficiency of the migration.
A well-configured environment is crucial for the successful execution of GPU-accelerated jobs, as it directly affects job performance and resource utilization.

Common Pitfalls

1
Failing to properly configure the Aether client can lead to migration failures or suboptimal performance.
It's essential to follow the setup instructions closely to ensure that all necessary parameters are set correctly, which directly impacts the success of the migration process.

Related Concepts

GPU Acceleration
Data Processing Optimization
Cloud Computing With AWS