CentOS 7 CE GPU Installation With Yum

This is an end-to-end recipe for installing MapD Community Edition on a CentOS 7 machine running with NVIDIA Kepler or Pascal series GPU cards using Yum.

Here is a quick video overview of the installation process.

The installation phases are:

Note: The order of these instructions is significant. Please install each component in the order presented to prevent aggravated hair loss.


These instructions assume the following:
  • You are installing on a “clean” CentOS 7 host machine with only the operating system installed.
  • Your MapD host only runs the daemons and services required to support MapD.
  • Your MapD host is connected to the Internet.


Prepare your Centos 7 machine by installing EPEL, updating your system, installing CUDA, creating the MapD user, and enabling a firewall.


Install the Extra Packages for Enterprise Linux (EPEL) repository. RHEL-based distributions require Dynamic Kernel Module Support (DKMS) in order to build the GPU driver kernel modules. For more information, see https://fedoraproject.org/wiki/EPEL.

sudo yum install epel-release

Update and Reboot

Update the entire system and reboot to activate the latest kernel.

sudo yum update
sudo reboot

Create the MapD User

Create the mapd group and mapd user, who will be the owner of the MapD database. You can create both the group and user with the useradd command and the -U switch.

sudo useradd -U mapd

Install CUDA Drivers

CUDA is a parallel computing platform and application programming interface (API) model. It uses a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The CUDA platform gives direct access to the GPU virtual instruction set and parallel computation elements. For more information on CUDA, see http://www.nvidia.com/object/cuda_home_new.html.

MapD does not require the entire CUDA package, only the CUDA drivers. Without following the installation instructions on the CUDA site, download the CUDA RPM for network install (https://developer.nvidia.com/cuda-downloads).

curl -O -u mapd http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-<VERSION INFO, for example 8.0.61-1.x86_64>.rpm

Use the following commands to install CUDA drivers:

sudo rpm --install cuda-repo-rhel7-<VERSION INFO, for example 8.0.61-1.x86_64>.rpm
sudo yum clean expire-cache
sudo yum install cuda-drivers

Reboot your system to ensure that all changes are active.

sudo reboot


Go to /usr/lib64/ and verify that the file libcuda.so is in that location.


To use Immerse, you must prepare your host machine to accept HTTP connections. You can configure your firewall for external access.

sudo firewall-cmd --zone=public --add-port=9092/tcp --permanent
sudo firewall-cmd --reload

For more information, see https://fedoraproject.org/wiki/Firewalld?rd=FirewallD.


Use curl to download the MapD repository file to the yum repository directory.

curl https://releases.mapd.com/ce/mapd-ce-cuda.repo | sudo tee /etc/yum.repos.d/mapd.repo

Install MapD using yum.

sudo yum install mapd


These are the steps to prepare your MapD environment.

Set Environment Variables

For convenience, you can update .bashrc with the required environment variables.

  1. Go to your home directory.

  2. Use ctrl-h to show hidden files.

  3. Edit the .bashrc file. Add the following export commands under “User specific aliases and functions.”

    # User specific aliases and functions
    export MAPD_USER=mapd
    export MAPD_GROUP=mapd
    export MAPD_STORAGE=/var/lib/mapd
    export MAPD_PATH=/opt/mapd
  4. Save the .bashrc file.

  5. Open a new terminal window to use your changes.

The $MAPD_STORAGE directory must be dedicated to MapD: do not set it to a directory shared by other packages.

MapD Configuration File (mapd.conf)

You can also create a configuration file with optional settings. See Configuration.


This step initializes the database and prepares systemd commands for MapD.

  1. Run the systemd installer. This script requires sudo access. You might be prompted for a password. Accept the values provided (based on your environment variables) or make changes as needed. The script creates a data directory in $MAPD_STORAGE with the directories mapd_catalogs, mapd_data, and mapd_export. mapd_import and mapd_log directories are created when you insert data the first time. The mapd_log directory is the one of most interest to a MapD administrator.

    cd $MAPD_PATH/systemd
    sudo ./install_mapd_systemd.sh


Start and use MapD Core and Immerse.

  1. Start MapD Core

    cd $MAPD_PATH
    sudo systemctl start mapd_server
    sudo systemctl start mapd_web_server
  2. Enable MapD Core to start when the system reboots.

    sudo systemctl enable mapd_server
    sudo systemctl enable mapd_web_server


To verify that all systems are go, load some sample data, perform a mapdql query, and generate a pointmap using Immerse.

MapD ships with two sample datasets of airline flight information collected in 2008. To install the sample data, run the following command.

sudo ./insert_sample_data

When prompted, choose whether to insert dataset 1 (7 million rows) or dataset 2 (10 thousand rows).

Enter dataset number to download, or 'q' to quit:
#     Dataset           Rows    Table Name          File Name
1)    Flights (2008)    7M      flights_2008_7M     flights_2008_7M.tar.gz
2)    Flights (2008)    10k     flights_2008_10k    flights_2008_10k.tar.gz

Connect to MapD Core by entering the following command in a terminal on the host machine (default password is HyperInteractive):

password: ••••••••••••••••

Enter a SQL query such as the following:

mapdql> SELECT origin_city AS "Origin", dest_city AS "Destination", AVG(airtime) AS
"Average Airtime" FROM flights_2008_10k WHERE distance < 175 GROUP BY origin_city,
Origin|Destination|Average Airtime
Ft. Myers|Orlando|28.666667
Orlando|Ft. Myers|32.583333

Connect to Immerse using a web browser connected to your host machine on port 9092. For example, http://mapd.mycompany.com:9092.

Create a new dashboard and a pointmap to verify that backend rendering is working.

  1. Click New Dashboard.
  2. Select the flights_2008_10K table as the datasource.
  3. Click Connect to Table.
  4. Click Add Chart.
  5. Click SCATTER.
  6. Click X Axis +Add Measure.
  7. Choose arrdelay.
  8. Click Y Axis +Add Measure.
  9. Choose depdelay.

The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.