Run Your First Simulation

This tutorial will show you how to run CP2K simulations using the Inductiva API.

This tutorial will cover the H20-64 benchmark, available in the official CP2K website, to help you get started with simulations.

The use case simulates a system containing 64 water molecules (192 atoms, 512 electrons) in a 12.4 ų cell, with MD running for 10 steps.

We will also demonstrate Inductiva’s ability to efficiently scale this use case on a more powerful machine.

Prerequisites

Download the required files here and place them in a folder called H2O-64. Then, you’ll be ready to send your simulation to the Cloud.

Running an CP2K Simulation

Here is the code required to run OpenSees simulation using the Inductiva API:

"""CP2K Simulation."""
import inductiva

# Allocate a machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup(
    provider="GCP",
    machine_type="c2d-highcpu-16",
    spot=True)

# Initialize the Simulator
cp2k = inductiva.simulators.CP2K(
    version="2025.1")

# Run simulation
task = cp2k.run(
    input_dir="/Path/to/H2O-64",
    sim_config_filename="H2O-64.inp",
    on=cloud_machine)

# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()

task.download_outputs()

task.print_summary()

In this basic example, we're using a cloud machine (c2d-highcpu-16) equipped with 16 virtual CPUs. For larger or more compute-intensive simulations, consider adjusting the machine_type parameter to select a machine with more virtual CPUs or one equipped with GPUs. You can explore the full range of available machines here.

Note: Setting spot=True enables the use of spot machines, which are available at substantial discounts. However, your simulation may be interrupted if the cloud provider reclaims the machine.

To adapt this script for other CP2K simulations, replace input_dir with the path to your CP2K input files and set the sim_config_filename accordingly.

When the simulation is complete, we terminate the machine, download the results and print a summary of the simulation as shown below.

Task status: Success

Timeline:
    Waiting for Input         at 17/04, 15:02:28      0.883 s
    In Queue                  at 17/04, 15:02:29      37.933 s
    Preparing to Compute      at 17/04, 15:03:07      11.183 s
    In Progress               at 17/04, 15:03:18      84.261 s
        └> 84.152 s        /opt/openmpi/5.0.6/bin/mpirun --use-hwthread-cpus cp2k.psmp H2O-64.inp
    Finalizing                at 17/04, 15:04:43      0.428 s
    Success                   at 17/04, 15:04:43

Data:
    Size of zipped output:    86.81 KB
    Size of unzipped output:  290.38 KB
    Number of output files:   5

Total estimated cost (US$): 0.0131 US$
    Estimated computation cost (US$): 0.0031 US$
    Task orchestration fee (US$): 0.010 US$

Note: A per-run orchestration fee (0.010 US$) applies to tasks run from 01 Dec 2025, in addition to the computation costs.
Learn more about costs at: https://inductiva.ai/guides/how-it-works/basics/how-much-does-it-cost

As you can see in the "In Progress" line, the part of the timeline that represents the actual execution of the simulation, the core computation time of this simulation was approximately 84 seconds (around 1 min and 24 seconds).

For comparison, the same simulation takes 1 minute and 15 seconds on a similar local machine with 16 virtual CPUs (Ryzen 7 7700X). This performance difference is expected as cloud CPUs typically have lower clock speeds compared to regular desktop processors, prioritizing energy efficiency and density over raw speed.

However, increasing the number of vCPUs on the cloud machine could improve performance.

Scaling Up Your Simulation

Scaling up your simulation is as simple as updating the machine_type parameter to a 56 vCPU machine (c2d-highcpu-56).

As mentioned above, running this simulation on a 16 vCPU cloud machine was slower than on a similarly powered local computer. To improve performance, we upgraded to a c2d-highcpu-56 instance with 56 vCPUs, reducing the runtime to just 43 seconds — with a slight cost increase to $0.0058.

By leveraging the Inductiva API, you can efficiently scale your CP2K simulations to meet your computational needs. Try different machine configurations and optimize your workflow for faster, more cost-effective results!