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Resources

The Inductiva API provides flexible computational resources for different simulation needs. Whether you're running a single simulation or orchestrating thousands of parallel tasks, our resource management system handles the complexity of infrastructure provisioning, scaling, and monitoring for you.

Available Resource Types

Inductiva provides three types of resources you can launch for your simulations.

Machine Group

A collection of homogeneous machines designed to operate individually, enabling the distribution of multiple simulations across different machines for parallel processing.

Best for: Multiple independent simulations
Key benefit: Parallel execution of separate simulation tasks
Learn more: Machine Group documentation

Elastic Machine Group

Similar to Machine Group but with dynamic scaling capabilities that automatically adjust the number of machines based on your simulation queue, ensuring efficient resource utilization and cost optimization.

Best for: Variable workloads, batch processing with unpredictable demand
Key benefit: Automatic scaling based on simulation demand
Learn more: Elastic Machine Group documentation

MPI Cluster

A network of machines configured to work collaboratively on a single simulation task, distributing the computational workload across multiple CPUs for maximum performance on complex simulations.

Best for: Complex simulations that exceed the capabilities of a single machine
Key benefit: Parallel processing within a single simulation
Learn more: MPI Cluster documentation

Choosing the Right Resource

Choose the resource type that best fits your simulation needs:

	Machine Group	Elastic Machine Group	MPI Cluster
Scaling	Fixed number of machines	Dynamic auto-scaling	Fixed cluster size
Simulation Distribution	One simulation per machine	One simulation per machine	Single simulation across all machines
Best for	Parallel independent tasks	Variable workloads	Large-scale distributed computing
Cost	Pay for all machines	Pay for active machines only	Pay for entire cluster
Resource Utilization	All machines run independently	Number of active machines scale with demand	All machines work together

Quick Start Example

To illustrate the performance of running simulations on resources using the Inductiva API, we will run SWASH Simulator on c2-standard-30 Google Cloud Platform (GCP) machines as an example. To keep costs down, we will use a preemptible Spot Machine.

import inductiva

# Download the input files for the SWASH simulation
input_dir = inductiva.utils.download_from_url(
    "https://storage.googleapis.com/inductiva-api-demo-files/"
    "swash-resources-example.zip", unzip=True)

# Instantiate a MachineGroup object with 1 preemptible machine of type
# c2-standard-30 and start it immediately
machine_group = inductiva.resources.MachineGroup(
    machine_type="c2-standard-30", spot=True)
machine_group.start()

# Initialize the SWASH simulator and run the simulation
# in your just launched dedicated MachineGroup
swash = inductiva.simulators.SWASH()
task = swash.run(input_dir=input_dir,
                 sim_config_filename="input.sws",
                 on=machine_group)

# Wait for the task to finish and download the outputs
task.wait()

# Terminate your dedicated MachineGroup at the end of the simulation.
machine_group.terminate()

Overview

MachineGroup Class