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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 v22.12.0

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA P100, V100, T4 and A2/A10/A30/A100 GPUs

Software Requirements:

OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, Rocky Linux 8

CUDA & NVIDIA Drivers*: 11.x & v450.80.02+

Apache Spark 3.1.1, 3.1.2, 3.1.3, 3.2.0, 3.2.1, 3.2.2, 3.2.3, 3.3.0, 3.3.1, Databricks 9.1 ML LTS or 10.4 ML LTS Runtime and GCP Dataproc 2.0

Python 3.6+, Scala 2.12, Java 8

*Some hardware may have a minimum driver version greater than v450.80.02+. 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.

Download v22.12.0

This package is built against CUDA 11.5 and all CUDA 11.x versions are supported through CUDA forward compatibility. It is tested on V100, T4, A2, A10, A30 and A100 GPUs with CUDA 11.0-11.5. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.5 or later is required. Users will need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

Verify signature

The output if signature verify:

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

Release Notes

New functionality and performance improvements for this release include:

  • Zstandard compression for Parquet and ORC
  • Support for Hive text reading
  • Improved performance on like operations
  • Tiered projections for more expressions to optimize performance
  • Support for mapInArrow, instr and array_remove operations
  • z-ordering capability on Databricks Delta Lake
  • Dynamic Partition Pruning (DPP) on Databricks
  • Qualification and Profiling tool:
    • Support cost estimations for Dataproc 1.5 and Dataproc2.x
    • Added new Github repo for user tools functionality

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.