Welcome to NVIDIA Parabricks v4.6.0

Installation Requirements

The following are hardware requirements for running Parabricks. * Any NVIDIA GPU that supports CUDA architecture 75, 80, 86, 89, 90, 100, or 120 and has at least 16 GB of GPU RAM. NVIDIA Parabricks has been tested on the following NVIDIA GPUs:

  • T4

  • A10, A30, A40, A100, A6000

  • L4, L40

  • H100, H200

  • GH200

  • B200, B300

  • GB200, GB300

  • RTX PRO 6000 Blackwell Server Edition

  • All tools require at least 16 GB of GPU memory per GPU. The following tools have defaults that require more than 16 GB but have options to lower the device memory usage to fit on devices with 16 GB of GPU memory.

    • giraffe (vg giraffe + GATK): Default configuration requires at least 40 GB of GPU memory per GPU. To use a 16 GB GPU, use the --low-memory option. More details on tuning the configuration for GPUs with various memory sizes can be found in giraffe -- Useful Options for Performance.

    • haplotypecaller and mutectcaller: Default configurations require at least 18 GB of GPU memory per GPU. To use a 16 GB GPU, use the --htvc-low-memory and --mutect-low-memory options, respectively.

    • minimap2: Default configuration requires at least 18 GB of GPU memory per GPU. To use a 16 GB GPU, use the --low-memory option.

    • rna_fq2bam: Default configuration requires at least 24 GB of GPU memory per GPU. To use a GPU with less device memory, use the --low-memory option.

  • System Requirements:

    • A 2 GPU system should have at least 100GB CPU RAM and at least 24 CPU threads.

    • A 4 GPU system should have at least 196GB CPU RAM and at least 32 CPU threads.

    • A 8 GPU system should have at least 392GB CPU RAM and at least 48 CPU threads.

Note
  • Running Parabricks on a single GPU is supported but not recommended. If using a single GPU, ensure that your system meets the 2-GPU requirement mentioned above.

  • Parabricks is supported on time-sliced virtual (vGPU) but not on Multi-Instance (MIG) GPUs.

The following are software requirements for running Parabricks.

  • Any NVIDIA driver that is compatible with CUDA 12.9 (535, 550, 570, 575, or similar). Ensure you check your

CUDA compatibility here * Any Linux Operating System that supports nvidia-docker2 Docker version 20.10 (or higher)

Refer to the following pages for more information on supported driver configurations

Note

Parabricks is available as Docker image. For Singularity users, refer to Singularity and Docker for more information about importing a Docker image into a Singularity Image,

Ensure you verify the hardware and software requirements before running Parabricks.

Checking available NVIDIA hardware and driver

To check your NVIDIA hardware and driver version, use the nvidia-smi command:

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$ nvidia-smi +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 570.86.15 Driver Version: 570.86.15 CUDA Version: 12.9 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA RTX 6000 Ada Gene... Off | 00000000:17:00.0 Off | Off | | 30% 28C P8 10W / 300W | 16MiB / 49140MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 3019 G /usr/lib/xorg/Xorg 56MiB | +-----------------------------------------------------------------------------------------+

This shows the following important information:

  • The NVIDIA driver version is 570.86.15.

  • The supported CUDA driver API is 12.9.

  • The GPU has 48 GB of memory.

Checking available CPU RAM and threads

To check how much RAM and CPU threads in your machine, you can run the following:

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# To check available memory $ cat /proc/meminfo | grep MemTotal # To check available number of threads $ cat /proc/cpuinfo | grep processor | wc -l

Checking nvidia-docker2 installation

To make sure you have nvidia-docker2 installed, run this command:

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$ docker run --rm --gpus all nvidia/cuda:12.9-base-ubuntu22.04 nvidia-smi

When it finishes downloading the container, it will run the nvidia-smi command and show you the same output as above.

Checking python version

To get which version of Python you have, enter the following command:

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$ python3 --version

Make sure it is at least version 3 (for example, 3.6.9 or 3.7).

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