RAPIDS Accelerator For Apache Spark provides a set of plugins for Apache Spark that leverage GPUs to accelerate Dataframe and SQL processing.

The accelerator is built upon the RAPIDS cuDF project and UCX.

The RAPIDS Accelerator For Apache Spark requires each worker node in the cluster to have CUDA installed.

The RAPIDS Accelerator For Apache Spark consists of two jars: a plugin jar along with the RAPIDS cuDF jar, that is either preinstalled in the Spark classpath on all nodes or submitted with each job that uses the RAPIDS Accelerator For Apache Spark. See the getting-started guide for more details.

Release v25.02.1

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA V100, T4, A10/A100, L4, H100 and B100 GPUs

Software Requirements: 

OS: Spark RAPIDS is compatible with any Linux distribution with glibc >= 2.28 (Please check ldd --version output).  glibc 2.28 was released August 1, 2018. 
Tested on Ubuntu 20.04, Ubuntu 22.04, Rocky Linux 8 and Rocky Linux 9

NVIDIA Driver*: R470+

Runtime: 
	Scala 2.12, 2.13
	Python, Java Virtual Machine (JVM) compatible with your spark-version. 

	* Check the Spark documentation for Python and Java version compatibility with your specific 
	Spark version. For instance, visit `https://spark.apache.org/docs/3.4.1` for Spark 3.4.1.

Supported Spark versions:
	Apache Spark 3.2.0, 3.2.1, 3.2.2, 3.2.3, 3.2.4
	Apache Spark 3.3.0, 3.3.1, 3.3.2, 3.3.3, 3.3.4
	Apache Spark 3.4.0, 3.4.1, 3.4.2, 3.4.3, 3.4.4
	Apache Spark 3.5.0, 3.5.1, 3.5.2, 3.5.3, 3.5.4, 3.5.5

Supported Databricks runtime versions for Azure and AWS:
	Databricks 11.3 ML LTS (GPU, Scala 2.12, Spark 3.3.0)
	Databricks 12.2 ML LTS (GPU, Scala 2.12, Spark 3.3.2)
	Databricks 13.3 ML LTS (GPU, Scala 2.12, Spark 3.4.1)

Supported Dataproc versions (Debian/Ubuntu/Rocky):
	GCP Dataproc 2.1
	GCP Dataproc 2.2

Supported Dataproc Serverless versions:
	Spark runtime 1.1 LTS
	Spark runtime 2.0
	Spark runtime 2.1
	Spark runtime 2.2

*Some hardware may have a minimum driver version greater than R470. Check the GPU spec sheet for your hardware’s minimum driver version.

*For Cloudera and EMR support, please refer to the Distributions section of the FAQ.

RAPIDS Accelerator’s Support Policy for Apache Spark

The RAPIDS Accelerator maintains support for Apache Spark versions available for download from Apache Spark

Download RAPIDS Accelerator for Apache Spark v25.02.1

Processor Scala Version Download Jar Download Signature
x86_64 Scala 2.12 RAPIDS Accelerator v25.02.1 Signature
x86_64 Scala 2.13 RAPIDS Accelerator v25.02.1 Signature
arm64 Scala 2.12 RAPIDS Accelerator v25.02.1 Signature
arm64 Scala 2.13 RAPIDS Accelerator v25.02.1 Signature

This package is built against CUDA 11.8. It is tested on V100, T4, A10, A100, L4 and H100 GPUs with CUDA 11.8 through CUDA 12.0.

Verify signature

  • Download the PUB_KEY.
  • Import the public key: gpg --import PUB_KEY
  • Verify the signature for Scala 2.12 jar: gpg --verify rapids-4-spark_2.12-25.02.1.jar.asc rapids-4-spark_2.12-25.02.1.jar
  • Verify the signature for Scala 2.13 jar: gpg --verify rapids-4-spark_2.13-25.02.1.jar.asc rapids-4-spark_2.13-25.02.1.jar

The output of signature verify:

gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <sw-spark@nvidia.com>"

Release Notes

  • Support the Spark functions Bin and TruncDate
  • Support group-limit optimization for ROW_NUMBER
  • Improve Spark metrics: Print the batch size information to executor log
  • Refine filter push down to avoid double evaluation
  • Grab the GPU Semaphore when reading cached batch data with the GPU to avoid a GPU OOM case
  • Add an option to disable measuring buffer copy to improve large shuffle large partition serialization
  • For updates on RAPIDS Accelerator Tools, please visit this link
  • Upgraded statically linked CUDA toolkit to 12.8, which includes support for GB100 GPUs

Note: There is a known issue in the 25.02.1 release when decompressing gzip files on H100 GPUs. Please find more details in issue-16661.

For a detailed list of changes, please refer to the CHANGELOG.

Archived releases

As new releases come out, previous ones will still be available in archived releases.