Below are archived releases for RAPIDS Accelerator for Apache Spark.

Release v23.12.1

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

OS: Ubuntu 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8

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
	Apache Spark 3.4.0, 3.4.1
	Apache Spark 3.5.0

Supported Databricks runtime versions for Azure and AWS:
	Databricks 10.4 ML LTS (GPU, Scala 2.12, Spark 3.2.1)
	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)

Supported Dataproc versions:
	GCP Dataproc 2.0
	GCP Dataproc 2.1

Supported Dataproc Serverless versions:
	Spark runtime 1.1 LTS

*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 v23.12.1

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_2.12-23.12.1.jar.asc rapids-4-spark_2.12-23.12.1.jar
  • Verify the signature for Scala 2.13 jar: gpg --verify rapids-4-spark_2.13-23.12.1.jar.asc rapids-4-spark_2.13-23.12.1.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

New functionality and performance improvements for this release include:

  • Introduced support for chunked reading of ORC files.
  • Enhanced support for additional time zones and added stack function support.
  • Enhanced performance for join and aggregation operations.
  • Kernel optimizations have been implemented to improve Parquet read performance.
  • RAPIDS Accelerator also built and tested with Scala 2.13.
  • Last version to support Pascal-based Nvidia GPUs; discontinued in the next release.
  • Introduced support for parquet Legacy rebase mode (spark.sql.parquet.datetimeRebaseModeInRead=LEGACY and spark.sql.parquet.int96RebaseModeInRead=LEGACY)
  • Introduced support for Percentile function.
  • Delta lake 2.3 support.
  • Qualification and Profiling tool:
    • Profiling Tool now processes Spark Driver log for GPU runs, enhancing feature analysis.
    • Auto-tuner recommendations include AQE settings for optimized performance.
    • New configurations in Profiler for enabling off-default features: udfCompiler, incompatibleDateFormats, hasExtendedYearValues.

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

Release v23.12.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

OS: Ubuntu 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8

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
	Apache Spark 3.4.0, 3.4.1
	Apache Spark 3.5.0

Supported Databricks runtime versions for Azure and AWS:
	Databricks 10.4 ML LTS (GPU, Scala 2.12, Spark 3.2.1)
	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)

Supported Dataproc versions:
	GCP Dataproc 2.0
	GCP Dataproc 2.1

Supported Dataproc Serverless versions:
	Spark runtime 1.1 LTS

*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 v23.12.0

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_2.12-23.12.0.jar.asc rapids-4-spark_2.12-23.12.0.jar
  • Verify the signature for Scala 2.13 jar: gpg --verify rapids-4-spark_2.13-23.12.0.jar.asc rapids-4-spark_2.13-23.12.0.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

New functionality and performance improvements for this release include:

  • Introduced support for chunked reading of ORC files.
  • Enhanced support for additional time zones and added stack function support.
  • Enhanced performance for join and aggregation operations.
  • Kernel optimizations have been implemented to improve Parquet read performance.
  • RAPIDS Accelerator also built and tested with Scala 2.13.
  • Last version to support Pascal-based Nvidia GPUs; discontinued in the next release.
  • Introduced support for parquet Legacy rebase mode (spark.sql.parquet.datetimeRebaseModeInRead=LEGACY and spark.sql.parquet.int96RebaseModeInRead=LEGACY)
  • Introduced support for Percentile function.
  • Delta lake 2.3 support.
  • Qualification and Profiling tool:
    • Profiling Tool now processes Spark Driver log for GPU runs, enhancing feature analysis.
    • Auto-tuner recommendations include AQE settings for optimized performance.
    • New configurations in Profiler for enabling off-default features: udfCompiler, incompatibleDateFormats, hasExtendedYearValues.

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

Release v23.10.0

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA P100, V100, T4, A10/A100, L4 and H100 GPUs

Software Requirements:

OS: Ubuntu 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8

NVIDIA Driver*: R470+

Runtime: 
	Scala 2.12
	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. 
	Please be aware that we do not currently support Spark builds with Scala 2.13.

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
	Apache Spark 3.4.0, 3.4.1
	Apache Spark 3.5.0

Supported Databricks runtime versions for Azure and AWS:
	Databricks 10.4 ML LTS (GPU, Scala 2.12, Spark 3.2.1)
	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)

Supported Dataproc versions:
	GCP Dataproc 2.0
	GCP Dataproc 2.1

*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 v23.10.0

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

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

New functionality and performance improvements for this release include:

  • Introduced support for Spark 3.5.0.
  • Improved memory management for better control in YARN and K8s on CSP.
  • Strengthened Parquet and ORC tests for enhanced stability and support.
  • Reduce GPU out-of-memory (OOM) occurrences.
  • Enhanced driver log with actionable insights.
  • Qualification and Profiling tool:
    • Enhanced user experience with the availability of the ‘ascli’ tool for qualification and profiling across all platforms.
    • The qualification tool now accommodates CPU-fallback transitions and broadens the speedup factor coverage.
    • Extended diagnostic support for user tools to cover EMR, Databricks AWS, and Databricks Azure.
    • Introduced support for cluster configuration recommendations in the profiling tool for supported platforms.

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

Release v23.08.2

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA P100, V100, T4, A10/A100, L4 and H100 GPUs

Software Requirements:

OS: Ubuntu 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8

NVIDIA Driver*: R470+

Runtime: 
	Scala 2.12
	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. 
	Please be aware that we do not currently support Spark builds with Scala 2.13.

Supported Spark versions:
	Apache Spark 3.1.1, 3.1.2, 3.1.3 
	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
	Apache Spark 3.4.0, 3.4.1
	Apache Spark 3.5.0

Supported Databricks runtime versions for Azure and AWS:
	Databricks 10.4 ML LTS (GPU, Scala 2.12, Spark 3.2.1)
	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)

Supported Dataproc versions:
	GCP Dataproc 2.0
	GCP Dataproc 2.1

*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.

Download v23.08.2

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.

Note that v23.08.1 is deprecated.

Verify signature

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

New functionality and performance improvements for this release include:

  • Compatibility with Databricks AWS & Azure 12.2 ML LTS.
  • Enhanced stability and support for ORC and Parquet.
  • Reduction of out-of-memory (OOM) occurrences.
  • Corner case evaluation for data formats, operators and expressions
  • Qualification and Profiling tool:
    • Profiling tool now supports Azure Databricks and AWS Databricks.
    • Qualification tool can provide advice on unaccelerated operations.
    • Improve user experience through CLI design.
    • Qualification tool provides configuration and migration recommendations for Dataproc and EMR.
  • Fixes Databricks build issues from the previous 23.08 release.

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

Release v23.08.1

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA P100, V100, T4, A10/A100, L4 and H100 GPUs

Software Requirements:

OS: Ubuntu 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8

NVIDIA Driver*: R470+

Runtime: 
	Scala 2.12
	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. 
	Please be aware that we do not currently support Spark builds with Scala 2.13.

Supported Spark versions:
	Apache Spark 3.1.1, 3.1.2, 3.1.3 
	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
	Apache Spark 3.4.0, 3.4.1
	Apache Spark 3.5.0

Supported Databricks runtime versions for Azure and AWS:
	Databricks 10.4 ML LTS (GPU, Scala 2.12, Spark 3.2.1)
	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)

Supported Dataproc versions:
	GCP Dataproc 2.0
	GCP Dataproc 2.1

*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.

Download v23.08.1

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.

Note that v23.08.0 is deprecated.

Verify signature

The output if signature verify:

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

Release Notes

New functionality and performance improvements for this release include:

  • Compatibility with Databricks AWS & Azure 12.2 ML LTS.
  • Enhanced stability and support for ORC and Parquet.
  • Reduction of out-of-memory (OOM) occurrences.
  • Corner case evaluation for data formats, operators and expressions
  • Qualification and Profiling tool:
    • Profiling tool now supports Azure Databricks and AWS Databricks.
    • Qualification tool can provide advice on unaccelerated operations.
    • Improve user experience through CLI design.
    • Qualification tool provides configuration and migration recommendations for Dataproc and EMR.

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

Release v23.06.0

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 20.04, Ubuntu 22.04, CentOS 7, or Rocky Linux 8

CUDA & NVIDIA Drivers*: 11.x & v470+

Apache Spark 3.1.1, 3.1.2, 3.1.3, 3.2.0, 3.2.1, 3.2.2, 3.2.3, 3.2.4, 3.3.0, 3.3.1, 3.3.2, 3.4.0 Databricks 10.4 ML LTS or 11.3 ML LTS Runtime and GCP Dataproc 2.0, Dataproc 2.1

Python 3.6+, Scala 2.12, Java 8, Java 17

*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 v23.06.0

This package is built against CUDA 11.8, all CUDA 11.x and 12.x versions are supported through CUDA forward compatibility. It is tested on V100, T4, A2, A10, A30, A100, L4 and H100 GPUs with CUDA 11.8-12.0. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.8 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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Enhanced operator support with an OOM retry framework to minimize OOM or GPU specific config changes
  • Spill framework to reduce OOM issues to minimize OOM or GPU specific config changes
  • AQE for skewed broadcast hash join performance improvement
  • Support JSON to struct
  • Support StringTranslate
  • Support windows function with string input in order by clause
  • Support regular expressions with line anchors in choice input
  • Support rlike function with line anchor input
  • Improve the performance of ORC small file reads
  • Qualification and Profiling tool:
    • Qualification tool support for Azure Databricks
    • The Qualification and Profiling tools do not require a live cluster, and only require read permissions on clusters
    • Improve Profiling tool recommendations to support more tuning options

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

Release v23.04.1

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 20.04, Ubuntu 22.04, CentOS 7, or 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, 3.3.2, Databricks 10.4 ML LTS or 11.3 ML LTS Runtime and GCP Dataproc 2.0, Dataproc 2.1

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 v23.04.1

This package is built against CUDA 11.8 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) <sw-spark@nvidia.com>"

Release Notes

The 23.04.1 release patches a possible driver OOM which can occur on an executor broadcast. New functionality and performance improvements for this release include:

  • Introduces OOM retry framework for automatic OOM handling in memory-intensive operators, such as: join, aggregates and windows, coalescing, projections and filters.
  • Support dynamic repartitioning in large/skewed hash joins
  • Optimize the transpilation in regexp_extract function
  • Support Delta Lake write with auto-optimization and auto-compaction on Databricks platforms
  • Qualification and Profiling tool:
    • Add support to recommend cluster shape options on Dataproc and EMR
    • Add support for Databricks local mode with cost savings based on cluster metadata
    • Add TCO calculator to estimate annualized cost savings, including estimated frequency for applications
    • Add support in the qualification tool to generate estimated speed-up for ML functionality in Spark applications

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

Release v23.02.0

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 10.4 ML LTS or 11.3 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 v23.02.0

This package is built against CUDA 11.8 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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Delta Lake MERGE/DELETE/UPDATE (experimental feature, can be enabled with a config flag)
  • Function from_json
  • Hive text table write
  • Databricks 11.3 ML LTS support
  • Support batched full join to improve full join’s performance
  • Qualification and Profiling tool:
    • EMR user tools support for qualification
    • EMR user tools support for bootstrap
    • Updated estimated speedup factors for on-prem, Dataproc, and EMR environments for qualification

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

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) <sw-spark@nvidia.com>"

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.

Release v22.10.0

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, 3.3.0, 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 v22.10.0

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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Dataproc qualification, profiling, bootstrap and diagnostic tool
  • Databricks custom docker container
  • AQE support on Databricks
  • MultiThreaded Shuffle feature
  • Binary write support for parquet
  • Cast binary to string
  • Hive parquet table write support
  • Qualification and Profiling tool:
    • Print Databricks cluster/job information
    • AutoTuner for Profiling tool

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

Release v22.08.0

Hardware Requirements:

The plugin is tested on the following architectures:

GPU Models: NVIDIA 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, 3.3.0, 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 v22.08.0

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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Rocky Linux 8 support
  • Ability to build Spark RAPIDS jars for Java versions 9+
  • Zstandard Parquet and ORC read support
  • Binary read support from parquet
  • Apache Iceberg 0.13 support
  • Array function support: array_intersect, array_union, array_except and arrays_overlap
  • Support nth_value, first and last in windowing function
  • Alluxio auto mount for AWS S3 buckets
  • Qualification tool:
    • SQL level qualification
    • Add application details view

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

Release v22.06.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 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, 3.3.0, 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 v22.06.0

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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Combined cuDF jar and rapids-4-spark jar to a single rapids-4-spark jar. The RAPIDS Accelerator jar (rapids-4-spark jar) is the only jar that needs to be passed to Spark.
    The cuDF jar is now bundled with the rapids-4-spark jar and should not be specified.
  • Enable CSV read by default
  • Enable regular expression by default
  • Enable some float related configurations by default
  • Improved ANSI support
  • Add a UI for the Qualification tool
  • Support function map_filter
  • Enable MIG with YARN on Dataproc 2.0
  • Changed to ASYNC allocator from ARENA by default

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

Release v22.04.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS 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, 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 v22.04.0

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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Avro reader for primitive types
  • ExistenceJoin support
  • ArrayExists support
  • GetArrayStructFields support
  • Function str_to_map support
  • Function percent_rank support
  • Regular expression support for function split on string
  • Support function approx_percentile in reduction context
  • Support function element_at with non-literal index
  • Spark cuSpatial UDF

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

Release v22.02.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

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

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

Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, 3.2.0, 3.2.1, Databricks 7.3 ML LTS or 9.1 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 v22.02.0

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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Parquet reader and writer support for decimal precision up to 38 digits (128-bits)
  • Decimal 128-bits casting
    • Casting of decimal 128-bits values in nested types
    • Casting to String from decimal 128-bits
    • Casting from String to decimal 128-bits
  • MIG on YARN support
  • GPU explain only mode for Spark 3.x and 2.x
  • JSON reader support
  • Sequence function support
  • regexp_extract function support
  • Min and max on single-level struct
  • CreateMap updates and enable CreateMap by default
  • Cast from array to string
  • Add regular expression support to regexp_replace function
  • Support for conditional joins using libcudf’s mixed join feature

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

Release v21.12.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

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

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

Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, 3.2.0, Databricks 7.3 ML LTS or 9.1 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 v21.12.0

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) <sw-spark@nvidia.com>"

Release Notes

New functionality and performance improvements for this release include:

  • Support decimal precision up to 38 digits (128-bits)
  • Support stddev on double in window context
  • Support CPU row-based UDF
  • CreateArray outputs array of struct
  • collect_set outputs array of struct
  • ORC reader and writer support for decimal precision up to 38 digits (128-bits)
  • ORC writer supports array, map, and struct
  • Support SampleExec, rlike
  • regexp_replace supports more patterns such as replacing null
  • ParquetCachedBatchSerializer supports map
  • Add function explainPotentialGpuPlan to print GPU query plan in a CPU Spark cluster
  • Qualification Tool
    • Detect RDD APIs and JDBC Scan
  • Profiling Tool
    • Catch OOM errors and log a hint to increase java heap size
    • Print potential problems

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

Release v21.10.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

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

CUDA & NVIDIA Drivers*: 11.0-11.4 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, 3.2.0, Databricks 7.3 ML LTS or 8.2 ML Runtime, GCP Dataproc 2.0, and Azure Synapse

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 v21.10.0

This package is built against CUDA 11.2 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 11.0-11.4. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

Release Notes

New functionality and performance improvements for this release include:

  • Support collect_list and collect_set in group-by aggregation
  • Support stddev, percentile_approx in group-by aggregation
  • RunningWindow operations on map
  • HashAggregate on struct and nested struct
  • Sorting on nested structs
  • Explode on map, array, struct
  • Union-all on map, array and struct of maps
  • Parquet writing of map
  • ORC reader supports reading map/struct columns
  • ORC reader support decimal64
  • Qualification Tool
    • Add conjunction and disjunction filters
    • Filtering specific configuration values
    • Filtering user name
    • Reporting nested data types
    • Reporting write data formats
  • Profiling Tool
    • Generating structured output format
    • Improved profiling tool performance

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

Release v21.08.0

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

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

CUDA & NVIDIA Drivers*: 11.0-11.4 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.0.3, 3.1.1, 3.1.2, Databricks 7.3 ML LTS or 8.2 ML 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 v21.08.0

This package is built against CUDA 11.2 and all CUDA 11.x versions are supported through CUDA forward compatibility. It is tested on V100, T4, A30 and A100 GPUs with CUDA 11.0-11.4. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

Release Notes

New functionality and performance improvements for this release include:

  • Handling data sets that spill out of GPU memory for group by and windowing operations
  • Running window rank and dense rank operations on the GPU
  • Support for the LEGACY timestamp
  • Unioning of nested structs
  • Adoption of UCX 1.11 for improved error handling for RAPIDS Spark Accelerated Shuffle
  • Ability to read cached data from the GPU on the supported Databricks runtimes
  • Enabling Parquet writing of array data types from the GPU
  • Optimized reads for small files for ORC
  • Qualification and Profiling Tools
    • Additional filtering capabilities
    • Reporting on data types
    • Reporting on read data formats
    • Ability to run the Qualification tool on Spark 2.x logs
    • Ability to run the tool on Apache Spark 3.x, AWS EMR 6.3.0, Dataproc 2.0, Microsoft Azure, and Databricks 7.3 and 8.2 logs
    • Improved Qualification tool performance

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

Release v21.06.2

This is a patch release to address an issue with the plugin in the Databricks 8.2 ML runtime.

Hardware Requirements:

GPU Models: NVIDIA V100, T4 or A10/A30/A100 GPUs

Software Requirements:

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

CUDA & NVIDIA Drivers*: 11.0 or 11.2 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.1.1, 3.1.2, Databricks 7.3 ML LTS or 8.2 ML 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 v21.06.2

This package is built against CUDA 11.2 and all CUDA 11.x versions are supported through CUDA forward compatibility. It is tested on Tesla datacenter GPUs with CUDA 11.0 and 11.2. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

Release Notes

This release patches the plugin to address a backwards incompatible change to Parquet filters made by Databricks in the Databricks 8.2 ML runtime. More information is in issue 3191 in the RAPIDS Spark repository. See the Release v21.06.0 release notes for more detail about new features in 21.06.

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

Release v21.06.1

This is a patch release to address an issue with the plugin in the Databricks 7.3 ML LTS runtime.

Hardware Requirements:

GPU Models: NVIDIA V100, T4 or A10/A30/A100 GPUs

Software Requirements:

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

CUDA & NVIDIA Drivers*: 11.0 or 11.2 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.1.1, 3.1.2, 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 v21.06.1

This package is built against CUDA 11.2 and all CUDA 11.x versions are supported through CUDA forward compatibility. It is tested on Tesla datacenter GPUs with CUDA 11.0 and 11.2. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

Release Notes

This release patches the plugin to address a backwards incompatible change to Parquet filters made by Databricks in the Databricks 7.3 ML LTS runtime. More information is in issue 3098 in the RAPIDS Spark repository. See the Release v21.06.0 release notes for more detail about new features in 21.06.

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

Release v21.06.0

Starting with release 21.06.0, the project is moving to calendar versioning, with the first two digits representing the year, the second two digits representing the month, and the last digit representing the patch version of the release.

Hardware Requirements:

The plugin is tested on the following architectures:

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

Software Requirements:

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

CUDA & NVIDIA Drivers*: 11.0 or 11.2 & v450.80.02+

Apache Spark 3.0.1, 3.0.2, 3.1.1, 3.1.2, Databricks 8.2 ML 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 v21.06.0

This package is built against CUDA 11.2 and all CUDA 11.x versions are supported through CUDA forward compatibility. It is tested on V100, T4, A30 and A100 GPUs with CUDA 11.0 and 11.2. For those using other types of GPUs which do not have CUDA forward compatibility (for example, GeForce), CUDA 11.2 is required. Users will need to ensure the minimum driver (450.80.02) and CUDA toolkit are installed on each Spark node.

Release Notes

New functionality for this release includes:

  • Support for running on Cloudera CDP 7.1.6, CDP 7.1.7 and Databricks 8.2 ML
  • New functionality related to arrays:
    • Concatenation of array columns
    • Casting arrays of floats to arrays of doubles
    • Creation of 2D array types
    • Hash partitioning with arrays
    • Explode takes expressions that generate arrays
  • New functionality for struct types:
    • Sorting on struct keys
    • Structs with map values
    • Caching of structs
  • New windowing functionality:
    • Window lead / lag for arrays
    • Range windows supporting non-timestamp order by expressions
  • Enabling large joins that can spill out of memory
  • Support for the concat_ws operator
  • Qualification and Profiling Tools
    • Qualification tool looks at a set of applications to determine if the RAPIDS Accelerator for Apache Spark is a good fit
    • Profiling tool to generate information used for debugging and profiling applications

Performance improvements for this release include:

  • Moving RAPIDS Shuffle out of beta
  • Updates to UCX error handling
  • GPUDirect Storage for spilling

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

Release v0.5.0

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

Hardware Requirements:

GPU Architecture: NVIDIA Pascal™ or better (Tested on V100, T4 and A100 GPU)

Software Requirements:

OS: Ubuntu 18.04, Ubuntu 20.04 or CentOS 7, CentOS8

CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0 
	
Python 3.6+, Scala 2.12, Java 8 

Download v0.5.0

Release Notes

New functionality for this release includes:

  • Additional support for structs, including casting structs to string, hashing structs, unioning structs, and allowing array types and structs to pass through when doing joins
  • Support for get_json_object, pivot, explode operators
  • Casting string to decimal and decimal to string

Performance improvements for this release include:

  • Optimizing unnecessary columnar->row->columnar transitions with AQE
  • Supporting out of core sorts
  • Initial support for cost based optimization
  • Decimal32 support
  • Accelerate data transfer for map Pandas UDF
  • Allow spilled buffers to be unspilled

Release v0.4.1

Download v0.4.1

Requirements

Hardware Requirements:

GPU Architecture: NVIDIA Pascal™ or better (Tested on V100, T4 and A100 GPU)

Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0 
	
Python 3.6+, Scala 2.12, Java 8 

Release Notes

This is a patch release based on version 0.4.0 with the following additional fixes:

  • Broadcast exchange can fail when job group is set

The release is supported on Apache Spark 3.0.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS and Google Cloud Platform Dataproc 2.0.

The list of all supported operations is provided here.

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

Note: Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer driver. This issue is resolved in the 0.5.0 and higher releases.

Release v0.4.0

Download v0.4.0

Requirements

Hardware Requirements:

GPU Architecture: NVIDIA Pascal™ or better (Tested on V100, T4 and A100 GPU)

Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0 
	
Python 3.6+, Scala 2.12, Java 8 

Release Notes

New functionality for the release includes

  • Decimal support up to 64 bit, including reading and writing decimal from Parquet (can be enabled by setting spark.rapids.sql.decimalType.enabled to True)
  • Ability for users to provide GPU versions of Scala, Java or Hive UDFs
  • Shuffle and sort support for struct data types
  • array_contains for list operations
  • collect_list and average for windowing operations
  • Murmur3 hash operation
  • Improved performance when reading from DataSource v2 when the source produces data in the Arrow format

This release includes additional performance improvements, including

  • RAPIDS Shuffle with UCX performance improvements
  • Instructions on how to use Alluxio caching with Spark to leverage caching.

The release is supported on Apache Spark 3.0.0, 3.0.1, 3.0.2, 3.1.1, Databricks 7.3 ML LTS and Google Cloud Platform Dataproc 2.0.

The list of all supported operations is provided here.

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

Note: Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer driver. This issue is resolved in the 0.5.0 and higher releases.

Release v0.3.0

Download v0.3.0

Requirements

Hardware Requirements:

GPU Architecture: NVIDIA Pascal™ or better (Tested on V100, T4 and A100 GPU)

Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1, Databricks 7.3 ML LTS Runtime, or GCP Dataproc 2.0 
	
Python 3.6+, Scala 2.12, Java 8 

Release Notes

This release includes additional performance improvements, including

  • Use of per thread default stream to make more efficient use of the GPU
  • Further supporting Spark’s adaptive query execution, with more rewritten query plans now able to run on the GPU
  • Performance improvements for reading small Parquet files
  • RAPIDS Shuffle with UCX updated to UCX 1.9.0

New functionality for the release includes

  • Parquet reading for lists and structs,
  • Lead/lag for windows, and
  • Greatest/least operators

The release is supported on Apache Spark 3.0.0, 3.0.1, Databricks 7.3 ML LTS and Google Cloud Platform Dataproc 2.0.

The list of all supported operations is provided here.

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

Note: Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer driver. This issue is resolved in the 0.5.0 and higher releases.

Release v0.2.0

Download v0.2.0

Requirements

Hardware Requirements:

GPU Architecture: NVIDIA Pascal™ or better (Tested on V100, T4 and A100 GPU)

Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7

CUDA & NVIDIA Drivers: 10.1.2 & v418.87+, 10.2 & v440.33+ or 11.0 & v450.36+

Apache Spark 3.0, 3.0.1
	
Python 3.x, Scala 2.12, Java 8 

Release Notes

This is the second release of the RAPIDS Accelerator for Apache Spark. Adaptive Query Execution SPARK-31412 is a new enhancement that was included in Spark 3.0 that alters the physical execution plan dynamically to improve the performance of the query. The RAPIDS Accelerator v0.2 introduces Adaptive Query Execution (AQE) for GPUs and leverages columnar processing SPARK-32332 starting from Spark 3.0.1.

Another enhancement in v0.2 is improvement in reading small Parquet files. This feature takes into account the scenario where input data can be stored across many small files. By leveraging multiple CPU threads v0.2 delivers up to 6x performance improvement over the previous release for small Parquet file reads.

The RAPIDS Accelerator introduces a beta feature that accelerates Spark shuffle for GPUs. Accelerated shuffle makes use of high bandwidth transfers between GPUs (NVLink or p2p over PCIe) and leverages RDMA (RoCE or Infiniband) for remote transfers.

The list of all supported operations is provided here.

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

Note: Using NVIDIA driver release 450.80.02, 450.102.04 or 460.32.03 in combination with the CUDA 10.1 or 10.2 toolkit may result in long read times when reading a file that is snappy compressed. In those cases we recommend either running with the CUDA 11.0 toolkit or using a newer driver. This issue is resolved in the 0.5.0 and higher releases.

Release v0.1.0

Download v0.1.0

Requirements

Hardware Requirements:

GPU Architecture: NVIDIA Pascal™ or better (Tested on V100 and T4 GPU)

Software Requirements:

OS: Ubuntu 16.04, Ubuntu 18.04 or CentOS 7
(RHEL 7 support is provided through CentOS 7 builds/installs)

CUDA & NVIDIA Drivers: 10.1.2 & v418.87+ or 10.2 & v440.33+

Apache Spark 3.0

Python 3.x, Scala 2.12, Java 8