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 v24.12.0

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA V100, T4, A10/A100, L4 and H100 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
	Apache Spark 3.5.0, 3.5.1, 3.5.2

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 v24.12.0

Processor Scala Version Download Jar Download Signature
x86_64 Scala 2.12 RAPIDS Accelerator v24.12.0 Signature
x86_64 Scala 2.13 RAPIDS Accelerator v24.12.0 Signature
arm64 Scala 2.12 RAPIDS Accelerator v24.12.0 Signature
arm64 Scala 2.13 RAPIDS Accelerator v24.12.0 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_2.12-24.12.0.jar.asc rapids-4-spark_2.12-24.12.0.jar
  • Verify the signature for Scala 2.13 jar: gpg --verify rapids-4-spark_2.13-24.12.0.jar.asc rapids-4-spark_2.13-24.12.0.jar

The output of signature verify:

gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <[email protected]>"

Release Notes

  • Add repartition-based algorithm fallback in hash aggregate
  • Support Spark function months_between
  • Support asynchronous writing for Parquet files
  • Add retry support to improve sub hash-join stability
  • Improve JSON scan and from_json
  • Improved performance for CASE WHEN statements comparing a string column against multiple values
  • Falling back to the CPU for ORC boolean writes by the GPU due to a bug in cudf’s ORC writer
  • Fix a device memory leak in timestamp operator in incompatibleDateFormats case
  • Fix a host memory leak in GpuBroadcastNestedLoopJoinExecBase when spillableBuiltBatch is 0
  • For updates on RAPIDS Accelerator Tools, please visit this link

Note: There is a known issue in the 24.12.0 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.