OpenTURNS meets High-Performance Computing

What is othpc?

othpc is a simple Python tool that facilitates the evaluation of numerical models on a SLURM based High-Performance Computing (HPC) facility.

The Python package allows one to apply the Uncertainty Quantification (UQ) methods from OpenTURNS directly on a computationally costly numerical model (e.g., FEM or CFD model) deployed on HPC.

Minimal example

Create a separate script defining your function, here is an example for a script named product_function.py:

# product_function.py
import openturns as ot

class ProductFunction(ot.OpenTURNSPythonFunction):
    def __init__(self):
        super().__init__(2, 1)

    def _exec(self, x):
        return [x[0] * x[1]]

Write the following launching script:

import othpc
import openturns as ot 
from product_function import ProductFunction

ot_product = ProductFunction()
othpc_product = othpc.SubmitFunction(ot_product, ntasks_per_node=3, timeout_per_job=5)
distribution = ot.JointDistribution([ot.Uniform(0., 1.), ot.Normal(0., 1.)])
x_sample = distribution.getSample(12) # Monte Carlo sample with size N=12
y_sample = othpc_product(x_sample) # Submits 4 SLURM jobs, each including a batch of 3 evaluations 
print(y_sample)

Here is the corresponding output:

100%|██████████████████████████████████████████████████████████████████████| 4/4 [00:39<00:00,  9.79s/it]
     [ y0          ]
 0 : [  0.0552903  ]
 1 : [ -0.351668   ]
 2 : [ -0.0928364  ]
 3 : [  0.023483   ]
 4 : [ -0.0724111  ]
 5 : [  0.33814    ]
 6 : [  0.10313    ]
 7 : [  0.332978   ]
 8 : [  0.0561647  ]
 9 : [ -0.00693689 ]
10 : [  0.735135   ]
11 : [  0.107765   ]

Beyond this basic example, the ProductFunction class is meant to be replaced by the execution of a numerical model. The CantileverBeam example illustrates the use of an executable in this context, and exploits most of the services provided by othpc.

Services and utils

Working with othpc simplifies the evaluation of costly numerical models and gives access to the OpenTURNS UQ methods. Among the services possibly provided by the package:

• Temporary result directory management.
• Cache mechanism to avoid repeating evaluations.
• Compatibility with multi-core or multi-node numerical models.
• Summary csv table presenting the evaluated inputs with their corresponding outputs.

How to install?

To install the current version:

pip install othpc

or, from conda:

conda install conda-forge::othpc

Documentation

Package documentation : http://openturns.github.io/othpc/main/

Contributors

Elias Fekhari, Joseph Muré, Julien Schueller, Michaël Baudin, Pascal Borel.