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 v25.04.0
Hardware Requirements:
The plugin is designed to work on NVIDIA Volta, Turing, Ampere, Ada Lovelace, Hopper and Blackwell generation datacenter GPUs. The plugin jar is tested on the following GPUs:
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)
Databricks 14.3 ML LTS (GPU, Scala 2.12, Spark 3.5.0)
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 1.2
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 v25.04.0
| Processor | Scala Version | Download Jar | Download Signature |
|---|---|---|---|
| x86_64 | Scala 2.12 | RAPIDS Accelerator v25.04.0 | Signature |
| x86_64 | Scala 2.13 | RAPIDS Accelerator v25.04.0 | Signature |
| arm64 | Scala 2.12 | RAPIDS Accelerator v25.04.0 | Signature |
| arm64 | Scala 2.13 | RAPIDS Accelerator v25.04.0 | Signature |
This package is built against CUDA 11.8. It is tested on V100, T4, A10, A100, L4, H100 and GB100 GPUs with CUDA 11.8 and CUDA 12.8.
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_2.12-25.04.0.jar.asc rapids-4-spark_2.12-25.04.0.jar - Verify the signature for Scala 2.13 jar:
gpg --verify rapids-4-spark_2.13-25.04.0.jar.asc rapids-4-spark_2.13-25.04.0.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 approx_count_distinct
- Support group by on binary type
- Support ArrayPosition function
- Support Databricks 14.3 ML LTS (without support for Deletion Vector reads in Delta Lake)
- Support Slice
- Enable Hive text writer
- Refine split-retry logs when out of memory happens to expose the real reason
- Allow BigSizedJoinIterator#buildPartitioner to produce more sub-partitions to avoid CudfColumnSizeOverflowException
Note: There is a known issue in the 25.04.0 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.