Documents published in Scipedia

• A Parallel Dynamic Asynchronous Framework for Uncertainty Quantification by Hierarchical Monte Carlo Algorithms

  R. Tosi, R. Amela, R. Badia, R. Rossi

  A Parallel Dynamic Asynchronous Framework for Uncertainty Quantification by Hierarchical Monte Carlo Algorithms (2021). https://link.springer.com/article/10.1007/s10915-021-01598-6

  
  

  Abstract

  The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, current solvers must satisfy both statistical accuracy and computational efficiency. The aim of this work is to introduce an asynchronous framework for Monte Carlo and Multilevel Monte Carlo methods to achieve such a result. The proposed approach presents the same reliability of state of the art techniques, and aims at improving the computational efficiency by adding a new level of parallelism with respect to existing algorithms: between batches, where each batch owns its hierarchy and is independent from the others. Two different numerical problems are considered and solved in a supercomputer to show the behavior of the proposed approach.

  Abstract
  The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, [...]

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• D5.3 Report on theoretical work to allow the use of MLMC with adaptive mesh refinement

  Q. Ayoul-Guilmard, S. Ganesh, M. Nuñez, R. Tosi, F. Nobile, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2021). 10

  
  

  Abstract

  This documents describes several studies undertaken to assess the applicability of MultiLevel Monte Carlo (MLMC) methods to problems of interest; namely in turbulent fluid flow over civil engineering structures. Several numerical experiments are presented wherein the convergence of quantities of interest with mesh parameters are studied at different Reynolds’ numbers and geometries. It was found that MLMC methods could be used successfully for low Reynolds’ number flows when combined with appropriate Adaptive Mesh Refinement (AMR) strategies. However, the hypotheses for optimal MLMC performance were found to not be satisfied at higher turbulent Reynolds’ numbers despite the use of AMR strategies. Recommendations are made for future research directions based on these studies. A tentative outline for an MLMC algorithm with adapted meshes is made, as well as recommendations for alternatives to MLMC methods for cases where the underlying assumptions for optimal MLMC performance are not satisfied. 

  Abstract
  This documents describes several studies undertaken to assess the applicability of MultiLevel Monte Carlo (MLMC) methods to problems of interest; namely in turbulent fluid [...]

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• D5.4 Report on MLMC for time dependent problems

  Q. Ayoul-Guilmard, S. Ganesh, M. Nuñez, R. Tosi, F. Nobile, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2021). 7

  
  

  Abstract

  In this report, we study the use of Multi-Level Monte Carlo (MLMC) methods for time dependent problems. It was found that the usability of MLMC methods depends strongly on whether or not the underlying time dependent problem is chaotic in nature. Numerical experiments are conducted on both simple problems, as well as fluid flow problems of practical interest to the ExaQUte project, to demonstrate this. For the non-chaotic cases, the hypotheses that enable the use of MLMC methods were found to be satisfied. For the chaotic cases, especially the case of high Reynolds’ number fluid flow, the hypotheses were not satisfied. However, it was found that correlations between the different levels were high enough to merit the use of multi-fidelity or control-variate approaches. It was also noted that MLMC methods could work for chaotic problems if the time window of analysis were chosen to be small enough. Future studies are proposed to examine this possibility.

  Abstract
  In this report, we study the use of Multi-Level Monte Carlo (MLMC) methods for time dependent problems. It was found that the usability of MLMC methods depends strongly on [...]

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• D1.3 First public Release of the solver

  R. Badia, J. Ejarque, F. Nobile, M. Nuñez, C. Soriano, C. Roig, R. Rossi, R. Tosi

  Open Access Repository of the ExaQUte project: Deliverables (2021). 5

  
  

  Abstract

  This deliverable presents the software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definition [8]. This report is meant to serve as a supplement to the public release of the software. Kratos is “a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. XMC is a python library for hierarchical Monte Carlo algorithms. Hyperloom and PyCOMPSs are environments for enabling parallel and distributed computation. 

  Abstract
  This deliverable presents the software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definition [8]. This report is meant to serve [...]

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• D1.4 Final public Release of the solver

  F. Nobile, R. Badia, J. Ejarque, L. Cirrottola, A. Froehly, B. Keith, A. Kodakkal, M. Nuñez, C. Roig, R. Tosi, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2021). 3

  
  

  Abstract

  This deliverable presents the final software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definitions [13]. This release also contains the latest developements on MPI parallel remeshing in ParMmg. This report is meant to serve as a supplement to the public release of the software. Kratos is “a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. XMC is “a Python library for parallel, adaptive, hierarchical Monte Carlo algorithms, aiming at reliability, modularity, extensibility and high performance“. Hyperloom and PyCOMPSs are environments for enabling parallel and distributed computation. ParMmg is an open source software which offers the parallel mesh adaptation of three dimensional volume meshes.

  Abstract
  This deliverable presents the final software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definitions [13]. This release also [...]

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• D1.2 First realease of the softwares

  R. Tosi, R. Amela, M. Nuñez, C. Roig, R. Badia, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2021). 1

  
  

  Abstract

  This deliverable presents the software release of the Kratos Multiphysics software [3], ”a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. In this deliverable we focus on the development of Uncertainty Quantification inside Kratos. This takes place in the MultilevelMonteCarloApplication, a recent development inside the software that allows to deal with uncertainty quantification.

  Abstract
  This deliverable presents the software release of the Kratos Multiphysics software [3], ”a framework for building parallel, multi-disciplinary simulation software, aiming [...]

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• D5.2 Release of ExaQUte MLMC Python engine

  R. Amela, Q. Ayoul-Guilmard, S. Ganesh, R. Tosi, R. Badia, F. Nobile, R. Rossi, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2021). 3

  
  

  Abstract

  In this deliverable, the ExaQUte xmc library is introduced. This report is meant to serve as a supplement to the publicly release of the library. In the following sections, the ExaQUte xmc library is described along with its current and future capabilities. The structure of the library, along with its  dynamic import mechanism, are described using samples of code. The algorithms behind the example files supplied with the public release are explained in detail as well.

  Abstract
  In this deliverable, the ExaQUte xmc library is introduced. This report is meant to serve as a supplement to the publicly release of the library. In the following sections, [...]

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• D4.2 Profiling report of the partner’s tools, complete with performance suggestions

  R. Amela, R. Badia, S. Böhm, R. Tosi, C. Soriano, R. Rossi

  Open Access Repository of the ExaQUte project: Deliverables (2021). 4

  
  

  Abstract

  This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions.  While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.

  Abstract
  This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks [...]

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• D2.3. Adjoint-based error estimation routines

  B. Keith, A. Apostolatos, A. Kodakkal, R. Rossi, R. Tosi, B. Wohlmuth, C. Soriano

  Open Access Repository of the ExaQUte project: Deliverables (2021). 5

  
  

  Abstract

  This document presents a simple and ecient strategy for adaptive mesh renement (AMR) and a posteriori error estimation for the transient incompressible Navier{Stokes equations. This strategy is informed by the work of Prudhomme and Oden [22, 23] as well as modern goal-oriented methods such as [5]. The methods described in this document have been implemented in the Kratos Multiphysics software and uploaded to https://zenodo.org [27].1 This document includes: A review of the state-of-the-art in solution-oriented and goal-oriented AMR. The description of a 2D benchmark model problem of immediate relevance to the objectives of the ExaQUte project. The denition and a brief mathematical summary of the error estimator(s). The results obtained. A description of the API.

  Abstract
  This document presents a simple and ecient strategy for adaptive mesh renement (AMR) and a posteriori error estimation for the transient incompressible Navier{Stokes equations. [...]

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