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eunmoliveira merged 4 commits into from
Dec 18, 2025
Merged

Fixes on Docs #67

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fae7cde
Update 2.scaling-with-mpi.md
eunmoliveira Dec 18, 2025
c1dd961
Merge branch 'development' into new-gprmax-tutorial
eunmoliveira Dec 18, 2025
08c7fab
Update 1.quick-start.md
eunmoliveira Dec 18, 2025
21389e3
Merge pull request #66 from inductiva/new-gprmax-tutorial
eunmoliveira Dec 18, 2025
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2 changes: 1 addition & 1 deletion docs/14.gprmax/1.tutorials/2.scaling-with-mpi.md
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Expand Up @@ -74,7 +74,7 @@ Next, we will explore MPI-based parallel execution, which will significantly spe
## MPI Processing
gprMax supports MPI, allowing each of the 54 runs to be executed **in parallel**. This requires a machine with enough vCPUs to support all the runs. Hence, we'll be running the case on a `c2d-highcpu-112`, which has 112 vCPUs, providing ample resources for all 54 simulations to run concurrently.

> ⚠️ **Note on vCPUs and Hyperthreading**: In most cloud environments (e.g., Google Cloud), a vCPU represents a single thread rather than a full physical core. By default, Google Cloud VMs provide 2 vCPUs per physical core, so a `c2d-standard-112` machine with 112 vCPUs typically has 56 physical cores with hyperthreading enabled.
> ⚠️ **Note on vCPUs and Hyperthreading**: In most cloud environments (e.g., Google Cloud), a vCPU represents a single thread rather than a full physical core. By default, Google Cloud VMs provide 2 vCPUs per physical core, so a `c2d-highcpu-112` machine with 112 vCPUs typically has 56 physical cores with hyperthreading enabled.

Wrap the Python command with `mpirun` as follows:

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2 changes: 1 addition & 1 deletion docs/31.swmm/1.tutorials/1.quick-start.md
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Expand Up @@ -52,7 +52,7 @@ task.download_outputs()
task.print_summary()
```

In this basic example, we're using a cloud machine (`c2d-highcpu-2`) equipped with 2 virtual CPUs.Since SWMM runs in a single thread, increasing the number of vCPUs won’t speed up a single simulation. You may consider switching machine families via the `machine_type` parameter, as newer CPU generations can improve single-core performance. You can explore the full range of available machines [here](https://console.inductiva.ai/machine-groups/instance-types).
In this basic example, we're using a cloud machine (`c2d-highcpu-2`) equipped with 2 virtual CPUs. Since SWMM runs in a single thread, increasing the number of vCPUs won’t speed up a single simulation. You may consider switching machine families via the `machine_type` parameter, as newer CPU generations can improve single-core performance. Explore the full range of available machines [here](https://console.inductiva.ai/machine-groups/instance-types).

> **Note**: Setting `spot=True` enables the use of [spot machines](/guides/machines/spot-machines), which are available at substantial discounts.
> However, your simulation may be interrupted if the cloud provider reclaims the machine.
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