{"id":100118,"date":"2025-05-15T10:00:00","date_gmt":"2025-05-15T17:00:00","guid":{"rendered":"https:\/\/developer.nvidia.com\/blog\/?p=100118"},"modified":"2025-05-29T12:04:59","modified_gmt":"2025-05-29T19:04:59","slug":"predicting-performance-on-apache-spark-with-gpus","status":"publish","type":"post","link":"https:\/\/developer.nvidia.com\/blog\/predicting-performance-on-apache-spark-with-gpus\/","title":{"rendered":"Predicting Performance on Apache Spark with GPUs"},"content":{"rendered":"\n

The world of big data analytics is constantly seeking ways to accelerate processing and reduce infrastructure costs. Apache Spark has become a leading platform for scale-out analytics, handling massive datasets for ETL, machine learning, and deep learning workloads. While traditionally CPU-based, the advent of GPU acceleration offers a compelling promise: significant speedups for data processing tasks.<\/p>\n\n\n\n

However, migrating Spark workloads from CPUs to GPUs isn’t a straightforward endeavor.\u00a0GPU acceleration, while powerful for certain operations, doesn’t necessarily improve performance in every scenario. Factors like small datasets, large amounts of data movement, and using user-defined functions (UDFs) can sometimes negatively impact GPU performance. Conversely, workloads involving high-cardinality data, such as joins, aggregates, sort, window operations, and transcoding tasks (like encoding\/compressing Apache Parquet or Apache ORC or parsing CSV) are typically positive indicators for GPU acceleration.<\/p>\n\n\n\n

This presents a crucial problem for organizations looking to leverage GPUs: How do you know if your specific Spark workload would truly benefit from GPU acceleration before investing time and resources in migration?\u00a0The specific environment that a Spark workload is running in on CPU vs. GPU can vary greatly. In addition, the network setup, disk bandwidth, and even the GPU type can play a factor in performance on GPU, and these variables can be hard to capture from Spark logs.\u00a0\u00a0<\/p>\n\n\n\n

The Qualification Tool<\/i><\/a><\/h2>\n\n\n\n

The proposed solution to this problem is the Spark RAPIDS Qualification Tool. This tool is designed to analyze your existing CPU-based Spark applications and predict which ones are good candidates for migration to a GPU cluster. It aims to project the performance of the Spark application on GPUs with a machine learning estimation model trained on industry benchmarks and historical results from many real-world examples. The tool is available as a command-line interface via a pip package<\/a> and can be used in various environments, including cloud service providers (CSPs) like AWS EMR, Google Dataproc, Databricks (AWS\/Azure), as well as on-premise environments.\u00a0There are quick-start notebooks available specifically for the AWS EMR<\/a> and Databricks<\/a> environments.<\/p>\n\n\n\n

The tool works by taking the Spark event logs<\/a> generated from your CPU-based Spark applications as its primary input. These event logs contain valuable information about the application, its executors, and the expressions used, along with relevant operating metrics. The tool supports event logs from both Spark 2.x and Spark 3.x jobs.<\/p>\n\n\n

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Figure 1. High-level flow for the qualification tool<\/em><\/figcaption><\/figure><\/div>\n\n\n

As output, the qualification tool provides several key pieces of information to aid in the migration process:<\/p>\n\n\n\n