MPICluster Class

An MPI cluster is a set of machines that work together to solve a problem. These machines are configured to communicate with each other using the Message Passing Interface (MPI) protocol.

With the MPICluster class, you can create a dedicated MPI cluster on-demand that is configured automatically to run your simulations in a distributed manner across the machines.

Instantiating an MPICluster object

The following parameters can be configured:

• the machine_type defines the type of CPU used for each machine. This parameter follows the naming convention set by Google Cloud, e.g., c2-standard-16. This convention is composed of a prefix that defines the CPU series, a suffix that sets the number of virtual CPUs (vCPU) per machine and the middle word refers to the level of RAM per vCPU. In the example, c2 refers to an Intel Xeon Scalable processor of 2nd generation, standard means 4 GB of RAM per vCPU and will contain 16 vCPUs. Check out the complete machine catalog available via the API.
• the zone allows to select the zone where the machines will be launched. By default, machines are launched in the europe-west1-b zone.
• the num_machines sets the number of machines available in the cluster. While the computational resource is active, these machines will be reserved for the user.
• the data_disk_gb defines the size of the NFS partition that is mounted on the head node and shared with the worker nodes. This partition is used to store the input and output files of the simulations.
• the max_idle_time determines the time a machine group can remain idle (without receiving any task) before it is terminated. By default, this value is 3 minutes.
• the auto_terminate_ts defines the moment in time in which the resource will be automatically terminated, even if there are tasks still running.

For example, the following code creates an MPICluster with 2 machines of type c2-standard-30:

import inductiva

mpi_cluster = inductiva.resources.MPICluster(
   machine_type="c2-standard-30",
   num_machines=2,
   data_disk_gb=100)

Creating an instance of MPICluster does not start the machines. This only registers the configuration on the API which can now be used to manage the cluster further.

Managing the MPI Cluster

Visit our Manage Resources guide to learn how to monitor and control your MPICluster resources.